Systems and methods for determining recommended aspects of future content, actions, or behavior

• US 9,396,483 B2
• Filed: 09/07/2015
• Issued: 07/19/2016
• Est. Priority Date: 08/28/2014
• Status: Active Grant

• Current Assignee: Vizit Labs Incorporated
• Original Assignee: Jehan Hamedi
• Inventors: Hamedi, Jehan
• Primary Examiner(s): LU, KUEN S
• Application Number:
  US14/846,920
• Publication Number:
  US 20160080485A1
• Time in Patent Office: 316 Days
• Field of Search: 707/772
• US Class Current: 1/1
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/90324 using system suggestions
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/1072 Discovery involving ranked list compilation of candidate peers

Abstract

A method includes providing, by a processor of a computing device, potential requests. Each of the potential requests include an indication of at least one recommended aspect type for a future content. The method further includes receiving a selection of at least one of the potential requests for the at least one recommended aspect type for the future content. The selection is associated with a unique author in at least one social network, website, application software, or mobile application software (app). The method further includes automatically determining, in response to the selection, the recommended aspect for the future content. The recommended aspect is determined at least in part based on activity data that indicates aspects of other content authored by or interacted with by a plurality of authors in the at least one social network, website, application software, or mobile application software (app) prior to receipt of the selection.

Claims

1. A system comprising:

a memory; and

a processor coupled to the memory, wherein the processor is configured to;

provide potential requests on a user interface, wherein each of the potential requests comprise an indication of at least one recommended aspect type for a future content to be posted, action to be taken, or behavior to engage in;

receive a selection through a user interface of at least one of the potential requests for the at least one recommended aspect type for the future content, action, or behavior, wherein the selection is associated with a unique author in at least one social network, website, application software, or mobile application software (app); and

automatically determine, in response to the selection, a recommended aspect for the future content, action, or behavior according to the selected recommended aspect type, wherein;

the recommended aspect is further determined at least in part based on activity data that indicates aspects of other content authored by or interacted with by a plurality of authors in the at least one social network, website, application software, or mobile application software (app) prior to receipt of the selection.

2. The system of claim 1, wherein the recommended aspect is determined based on how the plurality of authors have interacted with the other content in order to optimize a response of the plurality of authors to the future content, action, or behavior.

3. The system of claim 1, wherein the other content was authored by the unique author.

4. The system of claim 1, wherein the other content was authored by authors other than the unique author.

5. The system of claim 4, wherein the other content was authored by a related author to the unique author, and further wherein the related author and the unique author represent competitors for a similar good or service.

6. The system of claim 1, wherein the selection comprises a single input to the user interface, such that the recommended aspect is determined in response to the single input.

7. The system of claim 1, wherein the potential requests are automatically generated by the processor.

8. The system of claim 1, wherein the processor is further configured to receive selections of the potential requests to be provided.

9. The system of claim 1, wherein the recommended aspect is at least one of:

a social network, website, application software, or mobile application software (app) in which the future content is recommended to be authored,

a time of day in which the future content is recommended to be authored,

a length that the future content is recommended to be,

a subject matter that the future content is recommended to be related to,

a type of content that the future content is recommended to be,

a tag that the future content is recommended to include,

an indication that the future content is recommended to be in a certain color, style, or design,

an indication whether to use paid advertising services on a social network, website, application software, or mobile application software (app) in which the future content is recommended to be authored,

an author to engage with the future content, and

a universal resource indicator (URI) that the future content is recommended to include.

10. A method comprising:

providing, by a processor of a computing device, potential requests on a user interface, wherein each of the potential requests comprise an indication of at least one recommended aspect type for a future content to be posted, action to be taken, or behavior to engage in;

receiving, by the processor, a selection through a user interface of at least one of the potential requests for the at least one recommended aspect type for the future content, action, or behavior, wherein the selection is associated with a unique author in at least one social network, website, application software, or mobile application software (app); and

automatically determining, by the processor, in response to the selection, a recommended aspect for the future content, action, or behavior according to the selected recommended aspect type, wherein;

the recommended aspect is further determined at least in part based on activity data that indicates aspects of other content authored by or interacted with by a plurality of authors in the at least one social network, website, application software, or mobile application software (app) prior to receipt of the selection.

11. The method of claim 10, further comprising automatically generating content according to the recommended aspect.

12. The method of claim 10, further comprising sending the recommended aspect to a third party server.

13. The method of claim 10, wherein the activity data indicates that the other content has been viewed, shared, or interacted with at least a predetermined number of times.

14. The method of claim 10, wherein the activity data indicates a fluctuation related to the other content that meets a predetermined threshold.

15. The method of claim 10, further comprising posting, by the processor, the future content according to the recommended aspect to the at least one social network, website, application software, or mobile application software (app).

16. The method of claim 10, further comprising receiving, by the processor, a search criteria that specifies the plurality of authors in the at least one social network, website, application software, or mobile application software (app).

17. The method of claim 16, wherein the search criteria comprises a demographic trait or user profile trait of the plurality of authors, a subject matter of a social media post authored by the plurality of authors, a related subject matter of a predetermined number of social media posts authored by the plurality of authors, a group association of the plurality of authors, an interaction by the plurality of authors with the unique author, an engagement with a content by the plurality of authors, an amount of time spent viewing a webpage or screen by the plurality of authors, accessing a webpage or screen by the plurality of authors, the selection of a universal resource identifier (URI) by the plurality of authors, or an affirmative or negative activity executed through the at least one social network, website, application software, or mobile application software (app).

18. The method of claim 16, further comprising sending, by the processor, an alert only if a size of the plurality of authors reaches a predetermined threshold, wherein the alert comprises the recommended aspect.

19. The method of claim 16, further comprising sending, by the processor, an alert if a fluctuation of the plurality of authors'"'"' content, behavior, or activity reaches a predetermined threshold.

20. The method of claim 19, further comprising generating a recommended post in response to the fluctuation.

21. The method of claim 10, wherein the recommended aspect is determined at least in part based on the unique author'"'"'s advertising budget, predetermined number of target future contents, available time to spend on marketing activities, available marketing content, available marketing services, or size or characteristics of a target market.

22. The method of claim 10, wherein the recommended aspect is delivered via a browser extension.

23. A non-transitory computer readable medium having instructions stored thereon that, upon execution by a computing device, cause the computing device to perform operations comprising:

providing potential requests on a user interface, wherein each of the potential requests comprise an indication of at least one recommended aspect type for a future content to be posted, action to be taken, or behavior to engage in;

receiving a selection through a user interface of at least one of the potential requests for the at least one recommended aspect type for the future content, action, or behavior, wherein the selection is associated with a unique author in at least one social network, website, application software, or mobile application software (app); and

automatically determining, in response to the selection, a recommended aspect for the future content, action, or behavior according to the selected recommended aspect type, wherein;

the recommended aspect is further determined at least in part based on activity data that indicates aspects of other content authored by or interacted with by a plurality of authors in the at least one social network, website, application software, or mobile application software (app) prior to receipt of the selection.

24. The non-transitory computer readable medium of claim 23, wherein the instructions further cause the computing device to perform operations comprising determining that the future content was posted according to the recommended aspect.

25. The non-transitory computer readable medium of claim 24, wherein the instructions further cause the computing device to perform operations comprising determining an impact of the posted future content.