PRIVACY SENSTIVE PERSONA MANAGEMENT TOOLS
1. A processor-based system, comprising:
- memory for storing contents that are executable by processor electronics;
the processor electronics configured to execute the contents in order to;
create generic data by analyzing customer data and removing identifying customer data by;
determining identifying customer data by cross referencing data in at least a portion of the customer data with non-generic marketing data; and
substituting generic marketing data with substantially similar associated probabilities for the identifying customer data.
The disclosed tools include enhanced and flexible tools to enable users who may be business competitors to share non-generic data in a substantially generic and in a substantially equitable manner. The resulting incentive to more freely share data between competitors will benefit users such as brand owners and enhance content delivered to their end users based on shared data.
- 1. A processor-based system, comprising:
memory for storing contents that are executable by processor electronics; the processor electronics configured to execute the contents in order to; create generic data by analyzing customer data and removing identifying customer data by; determining identifying customer data by cross referencing data in at least a portion of the customer data with non-generic marketing data; and substituting generic marketing data with substantially similar associated probabilities for the identifying customer data.
- View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
- 11. A non-transitory computer readable medium having contents stored thereon that are executable by processor electronics in order to receive generic customer data by:
receiving the generic customer data transmitted by a remote computing system, wherein the generic customer data was determined by; finding customer data in a first data partition and the customer data in another data partition that are in common between the partitions, wherein the common customer data are associated with probabilities; combining the associated probabilities between common customer data from the partitions; and selecting the customer data, that are in both the first partition and the other data partition and any associated combined probabilities as the generic customer data.
- View Dependent Claims (12, 13, 14)
- 15. A non-transitory computer readable medium having contents stored thereon that are executable by processor electronics in order to:
receive customer data which was transmitted by a remote device, in which the customer data is comprised of tags and associated statistical probabilities, wherein the received customer data is stored on a private partition; and receive instructions from the remote device to allow access to the customer data in the private partition to a user in response to the user offering offer data if the offer data is comprised of; generic customer data from which identifying data has been removed.
- View Dependent Claims (16, 17, 18)
This application is a continuation of U.S. application Ser. No. 15/846,042 filed Dec. 18, 2017, which is a continuation of U.S. application Ser. No. 15/146,860 filed May 4, 2016, now U.S. Pat. No. 9,875,490, which is a continuation of U.S. patent application Ser. No. 14/280,480 filed May 16, 2014, now U.S. Pat. No. 9,348,979, and claims the benefit of and priority to: U.S. Provisional Patent Application No. 61/824,353 filed May 16, 2013, each of which is herein incorporated by reference in their entireties.
Related Applications: The following previously filed applications are herein incorporated by reference in their entirety:
U.S. Provisional Patent Application No. 61/493,965;
U.S. Provisional Patent Application No. 61/533,049;
U.S. Provisional Patent Application No. 61/506,601;
U.S. Provisional Patent Application No. 61/567,594;
U.S. Provisional Patent Application No. 61/597,136;
U.S. Provisional Patent Application No. 61/603,216;
U.S. Provisional Patent Application No. 61/683,678;
U.S. Provisional Patent Application No. 61/724,863;
CONSUMER DRIVEN ADVERTISING SYSTEM, U.S. patent application Ser. No. 13/490,444 filed Jun. 6 2012;
SYSTEM AND METHOD FOR DELIVERING ADS TO PERSONAS BASED ON DETERMINED USER CHARACTERISTICS, U.S. patent application Ser. No. 13/490,449 filed Jun. 6, 2012;
METHOD AND APPARATUS FOR DISPLAYING ADS DIRECTED TO PERSONAS HAVING ASSOCIATED CHARACTERISTICS, U.S. patent application Ser. No. 13/490,447 filed Jun. 6, 2012;
CONSUMER DRIVEN ADVERTISING SYSTEM, International Patent Application No, PCT/US12/41178 filed Jun. 6, 2012;
CONSUMER SELF-PROFILING GUI, ANALYSIS AND RAPID INFORMATION PRESENTATION TOOLS, U.S. application Ser. No. 13/707,581 filed Dec. 6, 2012;
CONSUMER SELF-PROFILING GUI, ANALYSIS AND RAPID INFORMATION PRESENTATION TOOLS, U.S. application Ser. No. 13/707,578 filed Dec. 6, 2012;
CONSUMER SELF-PROFILING GUI, ANALYSIS AND RAPID INFORMATION PRESENTATION TOOLS, PCT Application No. PCT/US12/68319 filed Dec. 6, 2012;
AD BLOCKING TOOLS FOR INTEREST-GRAPH DRIVEN PERSONALIZATION, U.S. patent application Ser. No. 13/843.635 filed Mar. 15, 2013;
REVERSE BRAND SORTING TOOLS FOR INTEREST-GRAPH DRIVEN PERSONALIZATION, U.S. patent application Ser. No. 13/843,270 filed Mar. 15, 2013; and
TOOLS FOR INTEREST GRAPH-DRIVEN PERSONALIZATION, PCT Patent Application No. PCT/US13/32643 filed Mar. 15, 2013.
The technology described in these applications as well as the current application are interoperable.
Appendix A has a summary description of e technologies described in the incorporated applications.
Currently, consumer users of email, e-commerce sites and databases as well as brand owners lack tools to efficiently and conveniently manage their account information. Specifically, these users lack ability to easily, equitably and anonymously/generically share their information and access each other'"'"'s anonymous/generic information. Sharing data across these users may help all of the users supplement their own proprietary (non-generic) information in order to facilitate better delivery of meaningful and personalized content and for other various marketing and advertising uses.
What is needed are enhanced and flexible tools to enable users such as the above to share non-proprietary data in a substantially fair manner.
As discussed in previous patent applications, advertars, personas and profiles of a user may reflect demographics/characteristics and associated probabilities of a user actually having said demographics/characteristics among other information. Interest graphs provide a valuable tool to represent this information in a computing device. As the user sorts brands and inputs Swote™ input, provides other information regarding their likes or dislikes, or other information, profile data that is reflected in a representation via interest graphs may be created or supplemented as illustrated in
As opposed to a social graph (which may also be included or may contribute to a profile) an interest graph focuses on shared interests regardless of personal connections while a social graph focuses on connections based on personal connections. (In some embodiments, profiles may incorporate social graphs as well or just social graphs alone).
In one embodiment, the nodes of an interest graph refer to the specific and varied interests that form one'"'"'s personal identity, and the links representing statistical probabilities attempt to connect people based on those interests. Individually, this may mean different things one person is interested in—be it jogging, celebrity gossip, or animal rights—that make up likes and dislikes, and what has more meaning to them over someone else. On a broader scale, the interest graph represents the way those interests form unspoken relationships with others who share them, and expand to create a network of like-minded people.
While the social graph consists of who a user knows, the interest graph consists of what they like, what moves them, and the facets of their personality that, in part, make up who they are. These interests can be represented in an interest graph by an interest graph node 708 and the probabilities, which may be propensities for the user toward each interest as well as propensities between interests themselves. These propensities may be represented as link 706 may also be incorporated into interest graphs. These connections can be much stronger, and much more telling, than simply who they are friends or acquaintances with. For example, two people being linked together because they knew each other in elementary school or work at the same job doesn'"'"'t necessarily indicate anything about them beyond their connection to each other. And for the people involved, it doesn'"'"'t always foster a very strong or lasting connection. As such, an interest graph may offer more insight into each person'"'"'s personal tastes, preferences and behaviors. To further on
Thus, given two connected users (such as user 702) connected in an interest graph, the users likely are more interested in the same advertising as opposed to users who are not. In addition, characteristics and associated characteristics (e.g., via a taxonomy) of those users can be studied and offers, products and other goods/services can be developed specifically for those demographics. This provides a highly personalized experience and also connects a user to users who have characteristics in common. As illustrated, not only different users, but also a user'"'"'s advertar such as 704 may be incorporated into interest graphs.
The data used to create the Interest graphs may itself contain or be associated to properties of the data. For example, in
Thus, interest graphs serve as a useful tool to represent persona information. In addition to nodes and links, other tools such as tables may be used to represent interest graphs and the data the interest graphs are based upon. The tools discussed herein may use interest graphs or other tools to represent and manipulate any profile data. The profile herein may be stored in any number of ways such as a database, array, spreadsheet etc.
One advantage to aggregation of brand information in a central partition is enhanced analytics. As illustrated in operation 3 in
Here, a central partition may be comprised of demographic data, characteristic/interest data, various data from user inputs, brand data, social graphs, contact information, friends, content such as pictures, posts by the user/friends, spend graphs, preferences, location, purchase history, browsing history (e.g., Personally Identifiable Information such as contact information) or any other types of data. As used herein, a partition may be any data in logical memory. Examples may be any data grouping such data randomly dispersed in a dataset across different physical partitions.
As discussed herein, a balance must be found between data sharing and proprietary data confidentiality. In one embodiment as discussed below, any piece of data in the central partition and brand partitions/profiles (e.g., interests graphs) may be tagged with permissions/attribution and statistical probability contributions and then stored on various devices configured for sharing with other partitions as in
In one embodiment as illustrated in
As illustrated, the central partition information may be represented via an interest graph stored optionally within the central partition. The origin and contribution of each node or partition and probability contribution may also be tracked by attribution tags or other tools to indicate ownership.
Sharing information between partitions is also illustrated in
As illustrated in the embodiment in
Expanding on the above, in one embodiment, the brand partition information may be copied from a plurality of brand owner private partitions to an audience engine server 214. Each of the brand information profiles is then optionally stored in its own partition or profile with the desired permissions (e.g., not accessible by other brand owners and users). The brand profile partitions/profiles are then linked to the central partition/profile upon user request as illustrated in
As illustrated in operation 3, one of many contemplated data sharing configurations may be between the audience engine 214 (audience engine memory) and the brand owner server(s) 210 and 212 and may be executed by either the user/brand owner configuring via webpage login on the audience engine or by the user/brand owner configuration while logged into the brand'"'"'s servers from a remote device. The data sharing between the central and brand profiles may be a one-time exchange of information or continuously updated as the data if either one of the partitions change. In one embodiment, the central profile partition and the brand profile partitions may be on the same device such as an audience engine. Brand owners may access and store their partitions on the audience engine 214 or copies may be made on brand owner servers for latency purposes.
A brand profile/partition 204, 206 may be associated with an existing user account at the brand owner'"'"'s server such as a credit card account, bank account, customer account (e.g., a Nordstrom credit card account). The partition may contain non-generic/private information proprietary to the brand owner. In one embodiment, the brand account may be a simple database of purchases, browsing history, PIE, contact information and other customer information. The brand partition may also have data stored in interest graph form.
The central partition may also contain or may be linked to a partition such as sub-partition 406 in
In operation 4, the shared information from operation 3 (such as the data contained in a sub-partition) may be used as discussed in previous applications- such as to make content recommendations to a consumer end user, conduct reverse brand sorting, enhance brand owner partition data etc.
As discussed above, a brand owner may wish not to expose their private non-generic information to others. Thus proprietary information may be kept confidential from other brand owners and optionally the owner of the audience engine 214. For example, in this embodiment, private partition 204 has data represented as nodes 308 and 312. These nodes and associated statistical probability links connecting them, contain information that the brand owner wants to remain private. As such, via permission tags or other tools, the data for these nodes and links is not visible or otherwise accessible to at least the second brand owner. In turn, data partition 206 is configured in a manner similar to data partition 204.
As illustrated in
Connecting nodes 312 and 314 and the links 408 that connect them, serve to associate their respective private partitions to the central partition 202. The data within nodes 312 and 314 may or may not be known by the audience engine 214 or others, These nodes may be used for partition association purposes as discussed below, which may include partition interchanges. Connecting nodes 312 and 314 may be configured or based on data the same as node 302, or each may be different as desired. For instance, node 312 may be an anonymous ID associated to the end user'"'"'s customer account on the brand owner'"'"'s server or elsewhere on the audience engine. This specific integration may ease integration into the brand owner'"'"'s databases. In another embodiment, nodes 312 and 314 may be a category of product or other commonality (e.g., the same tag or data property 716) that the two partitions have in common e.g., a “frozen food” node as discussed below. In other embodiments, central node 302 may have other nodes connecting partition 202 to other private and non-private partitions. The private partitions and central partition in turn may also have multiple nodes connecting them to any number of other partitions. Linking between partitions may use the illustrated nodes and links. If the data are not represented as interest graphs, any similar tools that link data partitions together may be used.
Once the various brand owners have established their private partitions on, or otherwise associated them to, audience engine 214, the data within the partitions may be directly or indirectly shared between partitions as illustrated in
Genericization of private information such as that in private partitions 204 and 206 is an important tool to allow such levering of non-generic proprietary information. Genericization may remove information such as identity information and/or substitute private information to make the information substantially common/generic. Such approaches may be via removing, substituting or altering non-generic data such as specific references, brands, content, statistical probabilities, time, type, characteristic, demographic, size (e.g., large shirt size), color (e.g., dress color), origin, property, identity, dates, locations, relationships and other data that is substantially private, data which in combination is substantially private or data designated as private by the brand owner.
The data in private partitions 502 and 504 may be based on specific user inputs such as Swote™ inputs, brand sorting, direct input, user purchase history etc. As illustrated, this data was based on a Swote™ input from a specific end user about her food preferences and interests gathered by the Wal-Mart and Costco directly from their customers. In other embodiments, the partitions may comprise entire customer bases, customers with a specific interest, a specific demographic in common, a location in common or other commonality e.g., any types or grouping of data desired.
In operation 1, the private partitions 502 and 504 feature various pieces of data, which may be in any database format on a single or multiple physical partitions grouped by a logical partition etc. The information may optionally be represented as an interest graph such as those shown in
In operation 2, the data from private partitions 502 and 504 are examined for commonalities and then grouped by said commonalities (such as grouped/tagged by associated SIC, GICS, NAICS codes discussed below). This may be done for any desired commonality and at various hierarchy levels as discussed below. As illustrated, grouping is done first at a common product category level (e.g., frozen foods category) and then a common product grouping (e.g., two pizza products). Grouping may be done via: any characteristic, demographic, property, description, type, location, usage, brand, ingredient, discount, common tag, content or any other data that may be desired for grouping.
In regards to a first level of commonality, in this embodiment, the data may be examined for a common tag or other property at a common product category level. For instance, Stouffer'"'"'s Pizza. Chicken, Nachos and Lean Cuisine Pizza, Steak and beans share a common property tag in that they are all tagged with “frozen foods”. These may be evident by data in private partitions 502 and 504 e.g., the products were tagged as such by the brand owners. Alternately, audience engine 214 may examine each product and determine appropriate tags from this commonality by taxonomy 512, marketing data, a human marketing expert, NAICS, SIC, GICS codes etc. Here, the products may be tagged with “frozen foods” category 514 and grouped together as in the table in operation 2.
After a first grouping by commonality, individual products between the two partitions may be optionally examined and grouped by further commonality to as many hierarchal levels as desired. This may be done through text matching or taxonomies or other tools. In the former case, the “Stouffer'"'"'s Pizza” and “Lean Cuisine Pizza” shared the common text identifier “Pizza”. Thus, given this further commonality of these frozen foods in addition to both of them being frozen foods, these two products are grouped in the same row in operation 2.
Once the two pizza products are grouped together, their information is split/separated into generic 520 and non-generic 518 portions. In one embodiment, a commonality between data from the two different partitions may be used as the generic data 520 e.g., “pizza”. As illustrated, generic data 520 is chosen as “pizza” since both Stouffer'"'"'s and Lean Cuisine (data from different private partitions that have been grouped together) both offer a product with text the test descriptor “pizza”. In turn, the remaining information- non-generic information e.g., their brand names Stouffers and Lean Cuisine are grouped together in non generic data 518. These are the differences in data from the common grouping between the two different partitions. Alternately, Stouffers and Lean Cuisine Pizza both have a “pizza” tag, their common tag “pizza” itself may be designated as generic (e.g., partition data subsequently tagged as “pizza”) as well in addition to their composite probabilities discussed below.
A taxonomy 512, such as a semantic map and/or marketing data may also select generic data 520 e.g., generic data from a taxonomy table itself. For instance, if a taxonomy table relates different brands of pizza to a common “pizza” category, the generic “pizza” category tag in the taxonomy table can be used as generic data 520. Alternately, a list of brands and other non-generic information can be compiled and used to determine non-generic information. In operation 2, “Lean Cuisine” may be belong to such a compilation as non-generic information list but not the term “steak”. Thus, “Lean Cuisine” may be designated as non-generic and “steak” as generic.
Generic data 520 and groupings, data tagging may be selected by marketing data, a taxonomy, semantic map, a human marketing expert or other tools such as Standard Industrial Classification (SIC) http://en.wikipedia.org/wiki/Standard_Industrial_Classification, Global Industry Classification Standard (GICS) http://en.wikipedia.org/wiki/Global_industry_Classification_Standard and the North American Industry Classification System (NAICS code). Determination of what data is generic/non-generic may be via the tools above as well. Also illustrated here, in the table in operation 2, an ID 516 is assigned for identification and optional record keeping purposes.
Also illustrated in operation 2, data within the same grouping (such as grouped by SIC, GICS, NAICS codes above) above have their associated probabilities grouped together in a composite probability 522. Specifically, their associated probabilities can be combined or otherwise used together. Specifically, Stouffer'"'"'s Pizza had a probability of +0.2 and Lean Cuisine had a probability of +1 in their respective private partitions. In this example, their composite value is then calculated by 0.2+1=1.2. Various formulas other than a summation may be used as well as weighting different probabilities as desired. Some examples are weighting a probability considering number of users it was based on, date range the information was collected etc. As illustrated, the other products such as Steak, Chicken, Nachos and Beans had no substantially generic data counter parts so their probabilities remain separate from the other brand'"'"'s products but are still grouped in the “frozen foods” category 514.
In order to present the generic information and their associated probability to other users without revealing proprietary data, the “non-generic” data 518 column from operation 2'"'"'s table are separated. The generic data column and their associated probabilities are then inserted into a partition such as sub-partition 506 or any other location as desired. Specifically, in operation, 3, associated non-generic data 520 and ID 516 columns are stripped off the table formed in operation 2 to preserve privacy of the brand owner'"'"'s data.
This new generic data in sub-partition 506 may be kept in a separate sub profile/partition as illustrated in
Various other embodiments are contemplated in which any number of private partitions and/or commonly accessible partitions may under go operations 1-3. For instance, commonality grouping and probability calculations like the above may be based on any number of private partitions using genericized or raw non-generic data, data commonly available from a taxonomy or marketing information, data commonly accessible in central partition 202, etc.
In one embodiment, private non-generic information may be substituted with a substantially similar brand (such as those in Standard Industrial Classification (SIC) groupings e.g., data tagged with the same SIC codes) information to preserve brand owner confidentiality. For instance, instead of a generic category name like “pizza” replacing “Stouffer'"'"'s Pizza”, a similar brand (with similar marketing data as evidenced by substantially similar tags such as customer attributes-demographics, product offerings, content, locations etc.) to the “Stouffer'"'"'s” (e.g., Godfather'"'"'s Frozen Pizza Brand) may be substituted in place of it. Replacement may comprise the associated products probabilities, generic data etc. from that replacement brand. This substitute brand data can thusly be used in lieu of “Stouffer'"'"'s” brand which in this case was private data.
In another embodiment, a brand such as Giant Bicycle brand is contained in a private partition. If no substantially generic brand is substantially equivalent to this brand, then a variety of tools can be used to protect the proprietary brand information “Giant Bicycles” and its associated probabilities and products. For instance via a taxonomy, marketing information or other tools can be used to replace “Giant Bicycles” with a generic category such as “bicycles”. This generic category may be populated by bicycle data (brands although that no one brand alone is suitable for direct replacement of “Giant”). A plurality of products and composite probability values together across a plurality of brands may be a suitable substitute for “Giant”. Thus, from these brands, associated probabilities and products can be genericized across a plurality of existing brands/products and probabilities by the tools above to produce a substantially suitable substitute. E.g., using generic data for the brands/products and averaging their probabilities to genericize the base data into data substantially suitable for replacement of the “Giant” data. For example, to find a substitute for “Giant”, “Giant” may be associated to various NAICs industry classifications. Companies found to be in the same industry classifications may have their information (probabilities and other properties) aggregated together and a composite brand created for substitution for “Giant”.
In another embodiment, private or generic sub partition data may be examined before or after any of the tools above are used. Examination may be for privacy sensitive information by examining the data, tags and probabilities that may lead to the user/brand being identified. If the data belongs to a list of known sensitive information, keywords, there is a probability that someone could use the related data to contact/discover the user/brand, related financial/location information, family or related user or workplace information, items bought together etc. After such examination, the data determined as sensitive can be stripped from the shared interest graph/profile/partition and, replaced, substituted as suitable etc.
Central Partition 202 (with a partial illustration of its nodes and links) is represented as having an interchangeable section 602. Sub partition 406 was calculated in a similar manner to that discussed in
Section 602 is an interchangeable area in the central partition where the partition 202 can interchange various private partitions such as 206 and 204 as well as the newly calculated generic data sub partition 406. Section 602 may be defined as a section of the central partition 202 connected to data in the central partition which as been tagged (or otherwise designated) tags to indicate that they will be connected or otherwise associated to an interchangeable partition.
The particular private partition selected for insertion into section 602 may be selected by the audience engine or other device according to the identity of the entity viewing it and associated access permissions. Specifically, a partition that a brand owner has permission to, is selected via the partition'"'"'s data properties such as ownership and permission tags. More, specifically, while central partition 202 is presented to the owners of private partitions 206 and 204, their particular private data partition that was previously selected by data property tags is presented in place of each other such that a non-owner will not see another owner'"'"'s private partition. However, their respective partitions will be presented with central partition 202 to give viewer with appropriate permissions context and enhance calculations across these partitions.
Thus, partition 202 and a selected partition (e.g., 206, 204 or 406, a combination of these or a new partition based on any of these partitions) can be and presented as a whole partition to the owner of the private partition or other entity that is given access to the selected interchangeable partition and partition 202. Presentation of the plurality of partitions may be made transparent to the owners of the various partitions. For instance, in one embodiment, an entity viewing its own partition and partition 202 together may not even be able to tell that section 602 exists and the existence and extent of other owner'"'"'s private partitions.
As discussed above and illustrated in
If the embodiment in
Specifically, the Frozen Food category 514 or other common data such as a frozen food node and associated probabilities may be inserted into the partitions 502 and 504 and sub partition 506. This serves to provide a point of reference for partition interchangeability. Thus, the frozen food nodes tagged as interchangeable nodes corresponding to frozen food interchangeable nodes in other private partitions would be a point of interchange between private partitions via central node 302, In addition, sub-partition 406 may also feature a “frozen food” node for the same reason given the partition is a genericized version of frozen food information from the private partitions 502 and 504, This frozen food node in sub-partition 506 may also be connected to the generic data 520 and associate probabilities 522.
In a like manner in
As discussed above, this partition interchangeability in context with partition 202 provides meaningful contextual information to the private partitions and generic partitions. As discussed below, a party'"'"'s ability to view various partition may be contingent upon being granted access permission in response to it contributing certain information in return.
Permissions are an important aspect for data privacy not only for the end consumer user, but also for the brand profile owner. In the later case, confidentiality and a prevention of data co-mingling is important as a particular brand owner may not wish to share their specific information (e.g., raw information) with another specific brand owner. However, a brand owner may be willing to share the generic information in partitions like 506 above as well as generic information in the central partition 202 with other brands. But before such sharing is agreed to, a brand owner typically needs assurance that the brand being granted access rights is in itself contributing some substantially suitable information in return for access.
As such, various permissions maybe assigned to the partitions 206, 204 and 406 as well as to other partitions and even individual pieces of data such as individual nodes and probabilities or data that these representations are based upon. Permissions may be set by the audience engine at the request of the private information contributors or set by the device storing the particular partition at issue. Permission setting may be through tagging partitions, portions of partitions or each piece of data such as a node/link with permission tags. For instance, no access permission to anyone but the owner may be assigned to a private partition. Permissions may comprise: access, read, write, execute, delete, move, copy, rename etc. Various partition users can be assigned with differing permission levels as desired such as based on, for example, data contribution level. For instance, access may be granted proportional to the contribution of information made available to other brand owners in raw form or generic form.
Determining if permissions and to data may be granted based upon a variety of criteria. One such access criteria is if an entity requesting access to a sub-partition, central partition or other partition is offering data that is a substantially equitable trade.
In the above case, an owner of a private partition may wish to ensure that an entity requesting access will exchange substantial information by providing a substantial or substantially similar amount of raw data, genericized data or a combination thereof (their offer data) in return to access to the private partition.
To determine if a data exchange like that of the above is indeed substantially equitable, various tools can be used. For instance, a data owner may require that the other owner'"'"'s data proposed for exchange be: comprised of a certain number or range/threshold of users, comprised of a certain number/threshold of nodes, have particular links or other desired pieces of data, be comprised of certain categories of data she does not have or has substantially less of, does not have a substantially large amount of the same information (e.g., categories, content, brands) or equivalent information (as determined by marketing data, taxonomy, marketing experts etc.), be of similar value in terms of being evaluated from a monetary perspective (data as apprised by various well known industry techniques and/or industry experts to be worth a certain dollar amount or virtual currency amount) or threshold amount (e.g., gigabytes of data) or granularity (e.g., in terms of desired detail), the type, number of other brand owners contributing to the requested data, the contributing brand owner'"'"'s data relevancy to the offered data, the requesting brand'"'"'s past contributions, the requesting brand'"'"'s reputation in the relevant market, financial value of the information (e.g., higher contribution if data is about an expensive product item), age of the information, the reputation of the brands, characteristics of the brands, probability ranges (as discussed below) etc. Any or a combination of the above may be used for the same or different potions of the data in question in either the private and/or contributor'"'"'s partition.
In one embodiment, all the contributors to a sub partition such as 406 may layer their permission criteria together. Thus a user wishing access to partition 406, may have to satisfy all their permission criteria or the most restrictive of the criteria are overlapping.
In one example of equitable data exchanges, as above, private partition 204 was used at least in part to create sub-partition 406. The owner of partition 204 wishes only those with substantially similar data (or data above a desired granularity threshold) to be able to access sub-partition 406. In turn, sub partition 406 is configured to be accessed only by authorized users that meet such criteria.
A third party entity who wishes to view sub-partition 406 submits their data to exchange, which may be raw data, or data genericized via the tools above or any other data or a combination of the aforementioned. The data is examined/compared by the audience engine 214 or other device such as a neutral third party device. The offered data may optionally not be accessible to the owner of partition 204 and a neutral party may serve as a data “escrow” for the exchange. If the third party offered data meets the desired criteria as set by one or more of the contributors of sub-partition 406 or optionally, the audience engine owner, then the data in sub-partition 406 may be shared with the third party entity and in turn, the third party entity data shared with the owner of partition 204 or others that have contributed. This access grant may be done by reconfiguring sub-partition 406 e.g., data properties to allow third party viewing but not grant other permissions such as write permissions. In another case, if only partial criteria is met, then an amount of data proportional with the partially satisfied criteria may be shared with third party entity. Proportions may be measured in quantity, quality of data, granularity, number matching categories etc. In one embodiment, a comparison is done between one partition, or a generic partition created from another partition to the offered data. A determination of an increase in detail (granularity) is done to determine if the owner of the partition/generic partition is getting additional data from the offered data.
Once the third party is granted access to sub-partition 406, it may view it as illustrated matched with central partition 202 (assuming it also has been granted access to it as well) together for contextual purposes.
In one embodiment a brand owner may configure the tools above remotely via the audience engine 214. Specifically, as discussed above, after uploading their partition data such as tags, probabilities, affinity content inputs (e.g., brand sorting or Swote™ input) content or any other data such as end user data, the brand owner may login to the audience engine and exclude others from partition access containing their private data. The user may then configure sharing as discussed below. For instance, upon receiving offer data from another user who does not have access to the private partition, the data may be analyzed as discussed above by the audience engine or others. Upon the offer data meeting criteria that the brand owner specifies such as the various equity criteria discussed herein, access to the private partition is granted to the requesting user. The offer data is then shared with the brand owner in return. The brand owner'"'"'s partition and the offer data at any time may contain tags representing an end user (user entering affinity content) input characteristic and associated probabilities. These may be configured as interest graphs and content recommendations executed as discussed in the previously referenced patent applications.
Once a third party entity receives access to a sub-partition such as 406 and a central partition 202, various calculations may be executed. For instance, as illustrated in
In one embodiment, the owner of partition 206 may use partition 206, partition 202 and sub partition 406 together or various pieces of them together. Specifically, it may instruct the audience engine to combine these together for a product recommendation or for other purposes. Specifically, the tags and associated probabilities of the desired partitions may be merged or otherwise manipulated like that in
Probability Ranges. Inclusion or omission of data from a private, central or sub partition to a new generic partition may be decided with the aid of probability ranges. In addition, probability ranges may be used to determine the quality/relevancy of data that is offered for trade and thus may affect a determined value of the data.
For instance, data may be worth less thus not exchanged at all if their nodes are within a particular probability range that infers that they are substantially similar to the owner'"'"'s own data. Data with probabilities ranges that are substantially too low may be designated as not relevant enough to a particular brand owner and thus worth less in a data exchange. Data with probabilities with ranges that are substantially too high may be determined as too irrelevant e.g., too obvious. On the other hand, the same data with substantially high ranges may be designated as high value and substantially relevant. Various ranges, thresholds and scales are contemplated and may defined through the above tools e.g., marketing data, taxonomies, marketing experts, past user data specific to the specific user or segment of users in question or other tools.
As discussed in this document, the discussed subject matter solves several technical problems. Specifically solved, is the current problem that users such as brand owners as well as end users lack an ability to easily, equitably and anonymously/generically share their information. Therefore, what is disclosed are enhanced and flexible tools to enable users such as the above to share non proprietary data in a substantially equitable manner.
The tools above may be used on any computing device and combinations of computing devices connected to each other as illustrated in
As will be discussed in further detail below, the disclosed technology allows users to create personas (also referred to as “advertars” or “advatars”) to serve as a privacy screen or a barrier between a user and advertisers. In addition, the disclosed technology can serve as a tool to segment a user'"'"'s interests/communications. A persona may be represented as an icon or other symbol that can be selected by a user and has a number of characteristics (e.g. demographic characteristics) associated with it. The demographic characteristics may represent either actual or desired demographic characteristics of the user. The demographic characteristics associated with the personas can be used by advertisers to determine a target audience for one or more ads. In one embodiment, ads are delivered to a persona but the advertiser does know the identity of the user associated with the persona. Users may have more than one persona that can receive ads. More than one persona can be active at any time or one or more of the user'"'"'s personas may be programmed to become active based on the time of day, location of the user, current activity of the user, and proximity of the user to objects, other users or locations or other factors.
Personas can be created by the user, copied from other users who have defined their personas or adopted from one of a number of predefined personas. In one embodiment, the demographic characteristics attributed to a persona are determined based on responses to the user'"'"'s indicated opinions such as likes or dislikes of a number of brands. As used herein, characteristics may include the demographic characteristics of a population such as (gender, age, location, marital status etc.) as well as properties, characteristics or traits relating to single individual users such as a user'"'"'s individual interests.
In one example a user who wishes to receive ads from one or more advertisers may use the disclosed tools. The user may select or create a persona that serves as a privacy barrier or screen between the user and the advertisers. Ads are delivered to a logical address, such as to an e-mail address that can be accessed by the user'"'"'s computing device to receive the ads. In another embodiment, ads are delivered to a server computer (not shown) that forwards the ads to the user'"'"'s computing device so that the user can receive the ads. The advertisers may not know the identity or other personal information of the user other than the fact that the user'"'"'s persona has one or more demographic characteristics that indicate that the user may like to receive ads of the type presented by the particular advertiser.
In one embodiment, a persona is implemented as a computer record that represents an address or device identifier to which an advertisement can be directed as well as a number of characteristics (e.g. demographic characteristics) that may be input directly by the user or inferred from user input. The aspects of a persona that can be seen by an advertiser may not identify the identity of the user such that the advertiser cannot contact the user directly other than by the address or device identifier associated with the persona. In one embodiment, a persona has a graphic icon that represents the persona and a number of demographic tags or categories representing the likelihood that the user falls in that demographic category or wishes to receive ads that are directed to people in that demographic category.
In one embodiment, separate cookies and caches are used for each persona when using a web browser or other computing device. This segmentation of persona information prevents information cross over between personas. In addition, this segmentation gives context to the information in the cookies and caches given that all data is related to the persona'"'"'s interests. This makes optional analysis of such cookies and caches more reliable since the user'"'"'s activities only pertain to the selected persona. Optionally, the cookies and caches can be encrypted to protect privacy.
In the example shown, a user has selected the icon 902(b) representing the brand “Fendi” from the neutral area 904 and has dropped it into the area 906 in order to indicate that the user likes this brand more. If the user has no opinion of the brand or is neutral about the brand, the user can leave the icon in an area of the screen 904 that groups icons for which no opinion has been expressed. Alternatively, icons representing brands for which no opinion or a neutral opinion is expressed can be removed from the screen and replaced with another icon representing another brand. Based on the opinions of the user to a group of brands, an estimate can be made of the likelihood that the user has one or more demographic characteristics (or would like to receive ads directed to users having those demographic characteristics). In some embodiments, brands that are left or placed in the neutral area of a screen may also be included in determining likely demographic characteristics in a variety of ways. For instance, if a user has relatively consistent neutral/unfamiliar opinion towards upscale brands such as Rolls Royce™ and Saks Fifth Avenue™, it may be inferred that the consumer is neutral/unfamiliar to the brands because her income level is likely not in the range of consumers who are exposed to these brands.
In an embodiment, upon selection of a brand such as an upscale brand (e.g., Rolls Royce) an inference could be made that the user is a high-income user. In response, a subsequent brand sorting screen may be presented with additional upscale brands to confirm the inference and determine other likely upscale demographic characteristics. For instance, if in the subsequent brand sorting screen, a user declined selection or voted down of all of the subsequent upscale brands, then an inference would be made that the user is a “aficionado” of expensive cars, but not a “big spender” across different types of categories such as spas, airplanes etc.
In the example shown, the brands represent known manufacturers or providers of goods or services that the user can buy or use. However for the purposes of the present application, the term “brand” is meant to be interpreted broadly. A brand may include, but is not limited to, a logo, trademark, animation, text, movies, movie clip, movie still, TV shows, books, musical bands or genres, celebrities, historical or religious figures, geographic locations, colors, foods (e.g. packaged foods), flowers, animals, designs, characteristics (young. old, short, tall), emotions (angry, bored), political views, color combinations, shapes, graphics, sounds, movement, smells, tastes, slogans, social media users, personas, patterns, occupations, hobbies or any other thing that can be associated with some demographic information. For instance any thing that can be broadly accepted or recognized by a plurality of users can be a brand. In addition, anything that can identify a seller/product/service as distinct from another can be a brand which may include Huggies™ brand diapers, Copper River Salmon, Microsoft™ software, a picture of Tom Cruise, a picture of a frame from one of Tom Cruise'"'"'s movies, a musical band name, a musical band album cover, a famous picture such as the picture from Time magazine celebrating victory in WWII in which a sailor is kissing a woman, a picture of a house in the country, a picture of a Porsche™ car, a picture of a smiley face as well as concept brands such as breast cancer awareness or environmentalism etc. In addition, a brand can be an abstract idea such as “World Peace”, “Save the Whales”, political ideologies such as “Republican” or other concepts about which a user may have an opinion.
In one implementation, each persona is associated with one or more tags representing different characteristics such as different demographic characteristics. The association may be determined via the brand sorting during persona creation. A tag may store or be associated with a value that represents the likelihood (e.g., a probability distribution) that the demographic characteristic represented by the tag is applicable to a user. For instance, the value of the tag may reflect a probability that the user is male while another tag represents the likelihood that the user lives in New York. Other tags may store values that represent the likelihood that the user has children, likes Chinese takeout food, and votes Democratic etc.
Based on the user'"'"'s indication of their opinion of the brands, such as if each brand is liked or disliked, the tag values can be combined into a composite value that reflects that likelihood that the user has a particular demographic characteristic. As an example, assume that a user indicates that they like Ford brand trucks, Remington brand shotguns and Golden retriever dogs, while another user indicates that they like Barney'"'"'s of New York brand clothes, Vogue magazine and miniature poodles. Here, the first user likely has a higher probability of being a male than the second user when one compiles the composite values of the probability distributions associated to the gender demographic associated to these brands. A different composite demographic can be associated with the persona created for each user. A user may also reuse composite demographics for multiple personas preventing repetitive entry of opinions. Advertisers then use these determined demographic characteristics to decide which personas should receive their ads. Brands may be selected for presentation to the user for brand sorting based on the likelihood of a user having a certain a certain demographic characteristic. For example, selection of a cosmetic brand X likely indicates a female user in which more brands relevant to females may be presented.
In one embodiment, the composite demographic information is created from the group of brands that are sorted by the user based on her opinions of the brands. In the example shown in
In one embodiment of the disclosed technology, the tags for the brands represent the same demographic characteristic. For example, Tag 1 for all the brands may represent the likelihood that the user is a male between ages 25-40, while Tag 2 may represent the likelihood that the user is a male between ages 40-55. Tag 3 may represent the likelihood that the user is a woman between ages 18-22 etc. Each tag has or is associated with a value representing the likelihood of a user having a defined demographic characteristic. These values for the tags are typically determined from information gathered from consumers who volunteer information about themselves and what brands they like, purchase etc. Such information is typically gathered from marketing data from consumer surveys or a variety of other data sources. The details of associating consumer demographic information with particular brands are considered to be well known to those skilled in marketing. In other embodiments, users may assign a value to a brand by inputting the value itself into the computing device, assigning a relative value to each brand and or tag (brand X given a higher preference to brand Y by giving brand X a location assignment a screen above or to the right of brand Y) etc.
Not every brand may have the same set of tags associated with it. For example Brand 1 does not have a Tag 4, while Brand 2 does not have Tags 2 and 6 and Brand 6 is lacking Tags 3 and 4.
In one embodiment, the composite demographic characteristics for a persona are created by arithmetically combining the values of the tags for the liked and disliked brands. In the example shown, Brands 1, 2 and 4 are liked so their tag values are summed while Brand 6 is disliked so its tag values are subtracted. When combined as illustrated, Tag 2 has a summed value of 4.0 (1.5 plus 1.5 minus (−1.0)). A value of 4.0 for a tag may represent a strong likelihood that a user has the demographic characteristic defined by the tag. On the other hand, a tag with a combined value of −2.5 may provide an indication that the user probably does not have the demographic characteristic associated with the tag and an inference can then be made. For example, if a composite gender tag value suggests the user is likely not a male, an inference can be made that the user is a likely female. A composite of the values of the brand tags across the brands (e.g., the sum of statistical probabilities of tag A across brands X to Y as seen in
In one embodiment, based upon the composite demographic characteristics, the corresponding user or persona may be placed into pre-computed demographic segments. Such pre-computed segments are typically determined from marketing survey data. Once the user is assigned to the segment, additional associated characteristics of the pre-computed segment may be associated to the user. In addition, ads that have been specifically designed to target the pre-computed segment may be delivered to the user.
In one embodiment, an ad/offer/content that a persona may be interested in receiving may be matched with the persona based on said persona vector. Typically an ad comes with tags such as coffee, sale, spa, dancing lessons etc. Here, an ad/offer'"'"'s tag values may be assigned based on marketing data taken from consumer surveys such as a probability distribution that a certain demographic (age, sex, income etc.) would likely desire to receive ads with a given ad tag. The composite of ad tag values represent a vector for the ad. Each of these offer tag values may therefore be considered as an ad vector dimension. In one embodiment, tags related to the ad tags may be assigned along with their associated values to aid in ad-persona matching.
Once a persona is defined, a plurality of ads can be ordered for presentation to the user according to likely persona affinity. By calculating the distance between the persona vector and the ad vector, such as their distances in N tag space, ads can be ranked in order of likely persona desire. The result of this distance calculation may be a ranked list of ads in order of affinity (i.e. the distance between the vectors) for a particular persona vector. In this manner, instead of filtering out ads, a relative ranking of ads is produced. Alternately, other distances between the ad and persona vectors (or any of their components) can be calculated to produce a ranking. Various other methods of ad filtering and ad sorting to match the appropriate ads to the persona may also be used. In some embodiments, location, past purchases, sale times/items, membership in customer loyalty programs, percentage off and other factors may be used to aid in ad ordering/selection. In one embodiment, the calculated affinity for a particular ad is displayed to the user as stars (e.g., an ad with a highly calculated affinity is four our of four stars etc.). In another embodiment, the ordering/filtering may consider the ratio of the geographic distance to an offer and the percentage off. For instance, if an ad is only 10% off and the distance is several hundred miles from the user, this ad would have a lower ordering then an ad that is 90% off and one mile away from the user. Here, the distance and percentage off etc., may be displayed to the user as well. In yet another embodiment, the persona may keep track of ads that resulted in a purchase by the consumer. After a purchase, the user will not be shown the ad on the persona that made a purchase or on ail her personas.
Optionally, the dimensions on the persona vector and/or the ad vector can be normalized by multiplying the dimension by a scalar between for instance, zero and one, to prevent particularly strong tag dimensions from skewing the results.
In one embodiment, the composite persona demographic information is determined locally on the user'"'"'s computing device with which they indicate their preference or opinion regarding various brands. In another embodiment, the opinion information such as like/dislike indications about presented brands are sent to a remote computing device, such a web server that determines the composite persona demographic information. If sent to a remote computer, the remote computer can return a persona back to the user'"'"'s device.
In one embodiment, once a user has created or adopted one or more personas, the personas are registered with a server computer that maps a persona to one or more addresses or other identifiers to which ads should be delivered. As discussed above, the address may be an e-mail address, IP address, device id., web site or another logical address that can be used to direct ads to the user.
As shown in
Ads may be displayed to users on the same device on which brand sorting occurred or on multiple different devices. The ads may be shown on these devices within a specified amount of time or upon an event trigger such as proximity to a merchant'"'"'s store, the start of a sale, another user expressing interest in the ad etc.
Audiences and personas may be accessed and transmit data to the audience engine 1220 on multiple applications across multiple platforms and devices. Typically each type of these interactions may communicate with the audience engine 1220 using an identifier that represents the user'"'"'s persona. As such, simultaneously use of a single persona may be permitted. Advertisers 1240 may be charged for varying access to personas or audiences across various devices, platforms and applications. For instance, an advertiser may be only permitted and thus only charged to access certain personas in an audience using an iPhone™ or access can be restricted to audiences using certain iPhone applications.
In one embodiment, the audience engine 1220 tracks the active time a user spends on each persona, actions/choices/votes/location/sharing of ads of the persona, ads voted on, purchases, click-thrus, impressions, advertising effectiveness, which application was used with the persona and which device(s) was used with the persona. This tracking may be confidential and not revealed to third parties without consumer permission. The user may be offered a reward such as money, points, gift cards in return for sharing this or other data. In another embodiment, the user may chose to share this data with selected personas owned by others or herself which results in a real-time sharing of her actions.
In one embodiment, the demographic information associated with a persona is refined depending on how the user reacts to ads delivered to the persona or previous brand sortings. For example, if the user indicates that they do not like an ad, one or more tag values associated with the persona may be adjusted. In this way a persona'"'"'s determined demographic characteristics can be continually improved or updated. In one embodiment, ads can be shown as icons and displayed and assigned affinity/voted on in a manner similar to how brands are sorted as illustrated in
In one embodiment, the feedback from user assigned ad affinity may make very granular adjustments to a persona. In one embodiment, a simple vote on an ad may modify a plurality of aspects of a persona by considering the specific tag, subcategory tag and associated weights among other things. For example, an ad was voted “thumbs up” and the ad had the following tags and associated values: car=1, car/Ford=0.2 and car/Toyota=−1 wherein car is a category tag and Ford and Toyota are subcategory tags. The persona could be modified in a plurality of ways. First, the persona would favor these tags and subcategory tags in a greater absolute magnitude than if the ad was voted “thumbs down”. This prevents undue voting down because users are more expressive about things they like as opposed to things they don'"'"'t like. Second, a variety of tuning factors may be applied to the tags “car” or subcategory tags “Ford” and “Toyota”. For example, categories may not all be weighted equally. In one example, categories may be weighted differently for different cultures. For instance, the automobile category may receive a higher weight in US culture as opposed to cultures where automobile ownership is lower.
If an ad was assigned a negative affinity, the tag and associated values may play a lessor role in assigning ads in the future. In one embodiment, no ads with those tags or related tags might be shown to the user. In another embodiment, ads with these tags and related tags might be decreased but reintroduced to the user at a gradual rate to ensure the user does not permanently omit herself from exposure. In another embodiment, the ads with said tags and related tags simply have their weights reduced accordingly. Similar approaches to the above can be applied to brand sorting.
A persona may be created by optionally choosing a name for the persona, associated interests/keywords (e.g. to help focus ad searches), social media accounts to tie the persona to and active locations/times the persona should be active among other parameters. Then, a brand sorting screen may be displayed at 1304. Upon sorting a number of brands, at 1306 and 1308 the brands that define the persona are transmitted to an audience engine 1310, which may be on a remote server.
The persona'"'"'s demographic characteristics are matched with ads, offers, coupons, services, products, content recommendations or other similar things. Typically, the audience engine 1310 is in communication with a third party ad server and/or ad bidding system (not shown). The ads may be pre-downloaded to the audience engine 1310 and analyzed. Analysis may be performed by assigning tags and associating statistical probabilities that particular demographics would be interested in the ads or assigning probabilities to existing tags or other data related to the ad. The ads are then optionally ordered in relevance to the characteristics of a particular persona'"'"'s vector as previously discussed. Here, in response to the persona creation, a plurality of ads are pushed to the mobile device at 1312 from the audience engine 1310. The ads are pushed into a local ad server 1316 on the user'"'"'s computing device. Here the local ad server is within the application 1314 that created the persona. Within the application 1314, is an ad tracker 318 with a ticket book. Each ticket may be used to request an ad from an in-application persona API 1322. In one embodiment, a ticket may contain information to display an ad to one or more personas and/or to different devices or applications associated with the persona.
The request for an ad may occur upon a user or a software request or on the occurrence of an event such as an arrival of the device at a physical location, keyword in communication, predetermined by an advertiser, event on a calendar, time of a TV show, a triggering event such as visiting a website, date of a product sale etc. API 1322 may start the ad request at 1324, which is transmitted to ad tracker 1318. Ad tracker 1318 returns a return ad ticket at 1320 to API 1322. API 1322 then submits the ad ticket and application ID at 1326 to the local ad server 316. The local ad server then displays the ad on the device or other connected devices at 1328. In one embodiment, the application ID at 1326 can be directed toward other applications on a plurality of connected devices in order for an ad to be shown on other devices. Optionally, upon display of the ad, at 1326 a request can be made to a connected device to display other content such as a website related to the displayed ad or the ad itself on other devices.
At the start operation, the in-app Advatar (persona) 1402 (typically stored on the user'"'"'s device within an application) has a Get Ad 1404 software module which requests a ticket (each ticket may contain a different GUID(s)) from an Advatar app 1406 on any desired device connected to a network. The Advatar app may cache a plurality of tickets in an ad ticket book 1408. The in-app Advatar 1402 is designed to request/receive and display ads via tickets and optionally designed to accept persona feedback on an ad and the persona'"'"'s actions.
The ticket requested by the in-app Advatar 1402 is sent from the Advatar app 1406 to the in-app Advatar 1402 with which the ticket is then associated with an application ID. The application ID is then sent to an advertiser'"'"'s ad server 1410, an ad exchange or real time bidding system. In one embodiment, different tickets may optionally correspond to tickets to show different personas ads. From there, the ad ticket and appID is passed to a secure third party server (e.g., audience engine) 1412 in which this sever, and optionally not the advertiser'"'"'s server, knows what the ticket GUID means in terms of the user'"'"'s identity or other sensitive information e.g., profile etc. Another use of the GUID is that users may appear simultaneously as different GUIDs on different devices in a secure manner. For example, advertising server A would see the GUID as 1234 and the same user is seen on advertising server B as user GUID 4567 but only the server 1412 would be able to determine the true identity of the user. The apparent GUID may even change periodically while accessing the same website (server 1412 will periodically assign a new GUID). The secure third party server 1412 would coordinate the information with the correct master ID as only it knows the corresponding GUIDs and identity/persona information. This protects the user from unwanted contact from advertisers such as SPAM as the advertiser has no email or other personally identifiable information. Although in one embodiment, the ad server 1410 has the user'"'"'s IP address in order to return an appropriate ad to the persona.
Given the persona profile on the secure third party server 1412, an appropriate ad or kind of ad is determined. The appropriate type of ad is then communicated to ad server 1410. The advertiser'"'"'s server 1410 then forwards the appropriate ad determined by the secure third party server 1412 to the in-app Advatar 1402 via an IP address that the in-app is hosted on. Once at Advatar 1402 a Show Ad module 1414 then displays or caches the ad for later display. Various other software embodiments are contemplated for masking a user'"'"'s identity.
Brand Sorting Embodiments
In the embodiment shown in
Upon brand sorting, ads and other recommendations can be displayed to a user. Upon ad feedback, the user may be displayed another series of brands (or ads) to vote on for a finer granularity of recommendations. In one embodiment, this ad voting may adjust values of a single persona vector or even multiple personas. For instance, a demographic dimension within the vector may be voted up or down by a desired amount depending on how an ad is voted. For instance, if many ads that are targeted to a certain demographic are voted up, then that demographic dimension in the persona may be adjusted up. However, to prevent a single dimension within a persona vector from unduly influencing the entire persona vector, dimensions can be optionally bounded.
In another embodiment of the brand sorter, different opinions can be asked depending on the desired context. The chart below illustrates some examples:
Different combinations and actions can be taken from the above chart. For instance, if a brand is “disliked” the brand'"'"'s associated values may simply be weighted down in the persona. However, if a brand is not liked, the brand'"'"'s associated values may be completely discarded. In addition, any associated tags may be flagged as not suitable for the consumer at all. Alternately, this “unsuitable” data may only be discarded for a short time and gradually be reintroduced to the user.
In other embodiments additional information may be displayed to the user during brand sorting during drag and drop selection. For example, as the icon 902b in
The advertiser or brand 1240 can then use the resulting persona data from the audience engine 1220 to analyze their products, ad performance, marketing strategy against any desired audience. Product ad effectiveness to a persona(s) in desired audiences can be ascertained by comparison of common and/or related tags between the persona and the ad tags and associated tag values. Analysis could comprise analyzing user votes on the ads, if the ad was clicked on by the user, if a product was purchased etc. A fee could be charged for such services to the advertiser 1240.
In one embodiment, under a single user account, each persona may be associated with a separate email address. This permits the user to have an email address focused specifically on a single persona. Each persona my have the ability to decline/filter communications according to keyword, sender, dates or other criteria to prevent the persona from being overwhelmed with unsolicited communications.
As illustrated in
The persona 1502 may access an email program as shown in
An arbitrary level of importance assignment may be featured in which high importance messages such as password assignments are given certain levels that are marked next to the domain “level 1” indication and lesser important emails are given lesser importance levels.
Embodiments of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus.
A non-transitory, computer storage medium can be, or can be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially-generated propagated signal. The computer storage medium also can be, or can be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices). The operations described in this specification can be implemented as operations performed by a data processing device using data stored on one or more computer-readable storage devices or received from other sources. A representative data processing device is shown in
The data processing device includes “processor electronics” that encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable microprocessor 1702, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing. The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). The apparatus also can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.
A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices 1704 for storing data, e.g., flash memory, magnetic disks, magneto-optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computing device can be embedded in another device, e.g., a mobile telephone (“smart phone”), a personal digital assistant (PDA), a mobile audio or video player, a handheld or fixed game console (e.g. Xbox 360), a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of volatile or non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device 1308, e.g., an LCD (liquid crystal display), LED (light emitting diode), or OLED (organic light emitting diode) monitor, for displaying information to the user and an input device 1706 such as a keyboard and a pointing device, e.g., a mouse or a trackball, track pad, temperature sensor, accelerometer, light sensor, audio sensor, wireless signal detection sensor etc., by which the user can provide input to the computer. In some implementations, a touch screen can be used to display information and to receive input from a user. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user'"'"'s client device in response to requests received from the web browser. The data processing apparatus 1700 may also include a wireless transceiver 1712 such a cellular radio, WiFi or WiMax transceiver, Bluetooth transceiver and a network connection 1714 etc. The data processing device may also include an output device such as a printer 1710. In addition, the device may include location sensing devices (GPS etc.), as well as clocks and other circuitry (not shown).
As shown in
The computing system can include any number of clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some embodiments, a server 1850 transmits data (e.g., an HTML page) to a client device 1700 (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server. In the embodiment shown in
From the foregoing, it will be appreciated that specific embodiments of the invention have been described herein for purposes of illustration, but that various modifications may be made without deviating from the spirit and scope of the invention. Accordingly, the invention is not limited except as by the appended claims.