Trend data counter

• US 10,325,274 B2
• Filed: 01/31/2014
• Issued: 06/18/2019
• Est. Priority Date: 01/31/2014
• Status: Active Grant

• Current Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Original Assignee: Walmart Apollo, LLC (WalMart Inc.)
• Inventors: Tuteja, Bhupesh, Khanchi, Amit, Kamble, Abhay Maruti
• Primary Examiner(s): Feacher, Renae
• Application Number:
  US14/170,479
• Publication Number:
  US 20150220940A1
• Time in Patent Office: 1,964 Days
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0631 Item recommendations

Abstract

Systems and methods are provided for collecting, sorting, and reporting data sets representing transactions, product reviews, social media product mentions, or the like. According to embodiments of the present disclosure, a trend aggregation system includes a backend data collector, a trend database, and a trend server. Data may be gathered from heterogeneous sources such as transaction records, product reviews posted by consumers on web sites, and product mentions posted on social network platforms. The data may be sorted and stored in a way to provide recall of trend data segments filtered according to selected parameters.

Claims

1. A computer-implemented method comprising:

at a backend data collector, searching two or more heterogeneous sources from two or more external third parties for one or more mentions of a product associated with a trend action of one or more trend actions;

at the backend data collector, identifying text strings from the two or more heterogeneous sources from the two or more external third parties associated with the one or more mentions of the product associated with the trend action of the one or more trend actions;

at the backend data collector, aggregating the text strings from the two or more heterogeneous sources from the two or more external third parties;

at the backend data collector, analyzing the text strings comprising the one or more mentions of the product associated with the trend action of the one or more trend actions to determine when each of the one or more mentions of the product associated with the trend action of the one or more trend actions comprises;

a positive mention;

or

a negative mention;

at the backend data collector, assigning a score to the each of the one or more mentions of

the product associated with the trend action of the one or more trend actions based at least in part on when the each of the one or more mentions of the product associated with the trend action of the one or more trend actions comprises the positive mention or the negative mention;

at the backend data collector, receiving a dataset regarding the trend action of the one or more trend actions, the dataset comprising;

a product identifier;

a category identifier;

a trend type action; and

the score of the each of the one or more mentions of the product associated with the trend action of the one or more trend actions;

at the backend data collector, sorting the dataset to populate a product column of one or more product columns in a category row of a trend database by inserting at least the product column of the one or more product columns in the category row, wherein;

a key for the category row comprises the category identifier;

a value for the product column comprises the product identifier;

the category identifier represents a product category; and

the product identifier represents a product of one or more trending products within the product category, the one or more trending products comprising the product;

at the backend data collector, sorting the dataset to populate a category column of one or more category columns in a trend type row of the trend database by inserting at least the category column of the one or more category columns in the trend type row, wherein;

a key for the trend type row comprises the trend type action;

a value for the category column comprises the category identifier; and

each category column of the one or more category columns comprises a corresponding product column of the one or more product columns, the each category column of the one or more category columns and the corresponding product column of the one or more product columns represent the trend action of the one or more trend actions;

at the backend data collector, sorting the dataset to populate an update column in an update row of the trend database by inserting at least the update column in the update row, wherein;

a key for the update row comprises the trend type action; and

a value for the update column comprises a count of trend actions of the one or more trend actions that fall under the trend type action;

at the backend data collector, sorting the dataset to populate a category update column in a category update row of the trend database by inserting at least the category update column in the category update row, wherein;

a key for the category update row comprises the trend type action and the category identifier; and

a value for the category update column comprises a count of trend actions that fall under the trend type action and the category identifier;

receiving a query from an application on a user device of a user for the one or more trending products;

at the trend database, searching the category column of the one or more category columns, thereby identifying the product category;

at the trend database, searching the product column of the one or more product columns, thereby identifying the product;

at the trend database, searching at least one of the update column or the category update column to calculate a trend action count associated with the trend type action;

at a trend server, filtering the trend action count associated with the trend type action by applying a trend type action filter or a product category filter previously selected by the user, wherein;

when a particular product category filter is applied, filtering the category update column in the category update row within a pre-determined time interval by the particular product category filter;

or

when a particular trend type action filter is applied, filtering the update column in the update row for a particular trend type within the pre-determined time interval by the particular trend type action filter;

from the trend server, transmitting the trend action count, as filtered and as associated with the trend type action and a set of trending products of the one or more trending products, to the user in response to the query, the set of trending products comprising the product identifier;

from the trend server, displaying on a graphical user interface of the user device of the user a counter comprising an on-screen number indicating a count of the one or more trend actions accumulated during a period of time;

after displaying the one or more trend actions on the graphical user interface of the user device, from the trend server, re-setting the counter of the on-screen number to zero to restart the count of the one or more trend actions;

from the trend server, displaying the one or more trending products of the one or more trend actions on the graphical user interface, wherein the user scrolls through the one or more trending products on a screen of the graphical user interface; and

after the user scrolls through an end of the one or more trending products on the screen of the graphical user interface, from the trend server, displaying one or more other trend actions counted during at least one or more previous periods of time on the screen of the graphical user interface, wherein the user continues to scroll through one or more other trending products from the one or more other trend actions displayed.

2. The computer-implemented method of claim 1, wherein:

the dataset further comprises a timestamp.

3. The computer-implemented method of claim 2, wherein:

a key for the product column of the one or more product columns comprises the timestamp;

a key for the category column of the one or more category columns comprises the timestamp;

a key for the update column comprises the timestamp; and

a key for the category update column comprises the timestamp.

4. The computer-implemented method of claim 1, wherein:

a key for the product column of the one or more product columns comprises a counter value;

a key for the category column of the one or more category columns comprises the counter value; and

the computer-implemented method further comprises;

initializing the product column of the one or more product columns and setting

the counter value to one; and

initializing the category column of the one or more category columns and setting the counter value to one.

5. The computer-implemented method of claim 1, wherein:

the trend action of the one or more trend actions comprises one of a group consisting of;

best-rated products;

newly pinned products;

best seller products;

recently reviewed products;

just sold products; and

products mentioned in other social platforms.

6. The computer-implemented method of claim 1, wherein:

a key for the product column of the one or more product columns comprises a concatenation of a timestamp and a counter value;

a key for the category column of the one or more category columns matches the key for the product column;

the counter value corresponds to a count of occurrences of products in the dataset during the pre-determined time interval, the products comprising the product; and

the timestamp reflects a start of the pre-determined time interval.

14. The computer-implemented method of claim 1, wherein:

transmitting the set of trending products to the user comprises;

determining the set of trending products based at least in part on at least one of;

past website browsing behavior of the user;

one or more demonstrated interests of the user;

or

one or more prior purchases by the user.

15. The computer-implemented method of claim 1, wherein:

the trend action of the one or more trend actions comprises one of a group consisting of;

best-rated products;

newly pinned products;

best seller products;

recently reviewed products;

just sold products; and

products mentioned in other social platforms;

transmitting the set of trending products to the user comprises;

determining the set of trending products based at least in part on at least one of;

past website browsing behavior of the user;

one or more demonstrated interests of the user;

or

one or more prior purchases by the user;

the dataset further comprises a timestamp;

a key for the product column of the one or more product columns comprises a concatenation of the timestamp and a counter value;

a key for the update column comprises the timestamp;

a key for the category column of the one or more category columns matches the key for the product column;

a key for the category update column comprises the timestamp;

the counter value corresponds to a count of occurrences of products in the dataset during the pre-determined time interval, the products comprising the product;

the timestamp reflects a start of the pre-determined time interval; and

the computer-implemented method further comprises;

initializing the product column of the one or more product columns and setting the counter value to one; and

initializing the category column of the one or more category columns and setting the counter value to one.

7. A system comprising:

a trend database comprising;

a trend type row, comprising at least one category column of one or more category columns;

a category row, comprising at least one product column of one or more product columns;

an update row, comprising at least one update column of one or more update columns;

a category update row, comprising at least one category update column of one or more category update columns;

a backend data collector adapted to;

search two or more heterogeneous sources from two or more external third parties for one or more mentions of a product associated with a trend action of one or more trend actions;

identify text strings from the two or more heterogeneous sources from the two or more external third parties associated with the one or more mentions of the product associated with the trend action of the one or more trend actions;

aggregating the text strings from the two or more heterogeneous sources from the two or more external third parties;

analyze the text strings comprising the one or more mentions of the product associated with the trend action of the one or more trend actions to determine when each of the one or more mentions of the product associated with the trend action of the one or more trend actions comprises;

a positive mention;

or

a negative mention;

assign a score to the each of the one or more mentions of the product associated with the trend action of the one or more trend actions based at least in part on when the each of the one or more mentions of the product associated with the trend action of the one or more trend actions comprises the positive mention or the negative mention;

receive a dataset regarding the trend action of the one or more trend actions, the dataset comprising;

a product identifier;

a category identifier;

a trend type action; and

the score of the each of the one or more mentions of the product associated with the trend action of the one or more trend actions;

sort the dataset to populate the at least one product column of the one or more product columns in the category row of the trend database by inserting the at least one product column of the one or more product columns in the category row, wherein;

a key for the category row comprises the category identifier and a trend type;

a value for the at least one product column comprises the product identifier;

the update row has a key comprising the trend type;

the category identifier represents a product category;

the product identifier represents a product within the product category; and

the category update row has a key comprising the trend type and the category identifier;

sort the dataset to populate the at least one category column of the one or more category columns in the trend type row of the trend database by inserting the at least one category column of the one or more category columns in the trend type row, wherein;

a key for the trend type row comprises the trend type action;

a value for the category column comprises the category identifier;

the at least one update column of the one or more update columns has a value comprising a count of trend actions that fall under the trend type; and

the at least one category update column has a value comprising a count of trend actions that fall under the trend type and the category identifier;

each category column of the one or more category columns comprises a corresponding product column of the one or more product columns, the each category column of the one or more category columns and the corresponding product column of the one or more product columns represent the trend action of the one or more trend actions; and

a trend server adapted to;

receive a query from an application on a user device of a user for one or more trending products, the one or more trending products comprising the product;

search the trend database to identify at least one trending product of the one or more trending products and calculate a trend action count associated with the trend type action by;

search the category column of the one or more category columns, thereby identifying the product category; and

search a product column of the one or more product columns, thereby identifying the product;

filter the trend action count associated with the trend type action by applying a trend type action filter or a product category filter previously selected by the user, wherein;

when a particular product category filter is applied, filtering a category update column in the category update row within a pre-determined time interval by the particular product category filter;

or

when a particular trend type action filter is applied, filtering an update column in the update row for a particular trend type within the pre-determined time interval by the particular trend type action filter;

transmit the trend action count, as filtered and as associated with the trend type action and a set of trending products of the one or more trending products, to the user in response to the query, the set of trending products comprising the product identifier;

from the trend server, displaying on a graphical user interface of the user device of the user a counter comprising an on-screen number indicating a count of the one or more trend actions accumulated during a period of time;

after displaying the one or more trend actions on the graphical user interface of the user device, from the trend server, re-setting the counter of the on-screen number to zero to restart the count of the one or more trend actions;

from the trend server, displaying the one or more trending products of the one or more trend actions on the graphical user interface, wherein the user scrolls through the one or more trending products on a screen of the graphical user interface; and

after the user scrolls through an end of the one or more trending products on the screen of the graphical user interface, from the trend server, displaying one or more other trend actions counted during at least one or more previous periods of time on the screen of the graphical user interface, wherein the user continues to scroll through one or more other trending products from the one or more other trend actions displayed.

8. The system of claim 7, further comprising a web server adapted to:

receive the product identifier from the trend server;

receive a sum of at least one count of trend actions;

and

transmit the graphical user interface to the user device for display to the user.

9. The system of claim 7, wherein:

the trend type comprises one of a group consisting of;

best-rated products;

newly pinned products;

best seller products;

recently reviewed products;

just sold products; and

products mentioned in other social platforms.

16. The system of claim 7, wherein:

the trend server adapted to transmit the set of trending products to the user comprises;

determining the set of trending products based at least in part on at least one of;

past website browsing behavior of the user;

one or more demonstrated interests of the user;

or

one or more prior purchases by the user.

17. The system of claim 7, wherein:

the dataset further comprises a timestamp;

a key for the at least one product column of the one or more product columns comprises the timestamp;

a key for the at least one category column of the one or more category columns comprises the timestamp;

a key for the at least one update column of the one or more update columns comprises the timestamp; and

a key for the at least one category update column of the one or more category update columns comprises the timestamp.

18. The system of claim 7, wherein:

a key for the at least one product column of the one or more product columns comprises a counter value; and

a key for the at least one category column of the one or more category columns comprises the counter value.

19. The system of claim 7, wherein:

the trend server adapted to transmit the set of trending products to the user comprises;

determining the set of trending products based at least in part on at least one of;

past website browsing behavior of the user;

one or more demonstrated interests of the user;

or

one or more prior purchases by the user,

the dataset further comprises a timestamp;

a key for the at least one product column of the one or more product columns comprises the timestamp;

a key for the at least one category column of the one or more category columns comprises the timestamp;

a key for the at least one update column of the one or more update columns comprises the timestamp;

a key for the at least one category update column of the one or more category update columns comprises the timestamp;

the trend type comprises one of a group consisting of;

best-rated products;

newly pinned products;

best seller products;

recently reviewed products;

just sold products; and

products mentioned in other social platforms; and

the system further comprises a web server adapted to;

receive the product identifier from the trend server;

receive a sum of at least one count of trend actions; and

transmit the graphical user interface to the user device for display to the user.

10. A method comprising:

at a backend data collector, searching two or more heterogeneous sources from two or more external third parties for one or more mentions of a product associated with a trend action of one or more trend actions;

at the backend data collector, identifying text strings from the two or more heterogeneous sources from the two or more external third parties associated with the one or more mentions of the product associated with the trend action of the one or more trend actions;

at the backend data collector, aggregating the text strings from the two or more heterogeneous sources from the two or more external third parties;

at the backend data collector, analyzing the text strings comprising the one or more mentions of the product associated with the trend action of the one or more trend actions to determine when each of the one or more mentions of the product associated with the trend action of the one or more trend actions comprises;

a positive mention;

or

a negative mention;

at the backend data collector, assigning a score to the each of the one or more mentions of the product associated with the trend action of the one or more trend actions based at least in part on when the each of the one or more mentions of the product associated with the trend action of the one or more trend actions comprises the positive mention or the negative mention;

at a trend database, receiving a dataset regarding the trend action of the one or more trend actions, the dataset comprising;

a product identifier;

a category identifier;

a trend type; and

the score of the each of the one or more mentions of the product associated with the trend action of the one or more trend actions;

at the trend database, generating a product column in a category row in the trend database by inserting at least the product column in the category row, wherein;

the category row has a key comprising the trend type and the category identifier;

the product column has a name comprising a timestamp and a counter value; and

the product column has a value comprising the product identifier;

at the trend database, generating a category column in a trend type row in the trend database by inserting at least the category column in the trend type row, wherein;

the trend type row has a key comprising the trend type;

the category column has a name comprising the timestamp and the counter value;

the category column has a value comprising the category identifier; and

the category column corresponds to the product column, and the category column and the product column represent the trend action of the one or more trend actions;

at the trend database, generating an update column in an update row in the trend database by inserting at least the update column in the update row, wherein;

the update row has a key comprising the trend type;

the update column has a name comprising the timestamp; and

the update column has a value comprising a count of trend actions of the one or more trend actions that fall under a trend type action;

at the trend database, generating a category update column in a category update row in the trend database by inserting at least the category update column in the category update row, wherein;

the category update row has a key comprising the trend type and the category identifier;

the category update column has a name comprising the timestamp; and

the category update column has a value comprising a count of trend actions that fall under the trend type and the category identifier

receiving a query from an application on a user device of a user for one or more trending products, the one or more trending products comprising the product;

at the trend database, searching the category column, thereby identifying a product category;

at the trend database, searching the product column, thereby identifying the product;

at the trend database, searching at least one of the update column or the category update column to calculate a trend action count associated with the trend type;

at a trend server, filtering the trend action count associated with the trend type action by applying a trend type action filter or a product category filter previously selected by the user, wherein;

when a particular product category filter is applied, filtering the category update column in the category update row within a pre-determined time interval by the particular product category filter;

or

when a particular trend type action filter is applied, filtering the update column in the update row for a particular trend type within the pre-determined time interval by the particular trend type action filter;

from a trend server, transmitting the trend action count, as filtered and as associated with the trend type and a set of trending products, to the user in response to the query, the set of trending products comprising the product identifier;

from the trend server, displaying on a graphical user interface of the user device of the user a counter comprising an on-screen number indicating a count of the one or more trend actions accumulated during a period of time;

after displaying the one or more trend actions on the graphical user interface of the user device, from the trend server, re-setting the counter of the on-screen number to zero to restart the count of the one or more trend actions;

from the trend server, displaying the one or more trending products of the one or more trend actions on the graphical user interface, wherein the user scrolls through the one or more trending products on a screen of the graphical user interface; and

after the user scrolls through an end of the one or more trending products on the screen of the graphical user interface, from the trend server, displaying one or more other trend actions counted during at least one or more previous periods of time on the screen of the graphical user interface, wherein the user continues to scroll through one or more other trending products from the one or more other trend actions displayed.

11. The method of claim 10, wherein:

the key of the category row further comprises a date stamp; and

the key of the trend type row further comprises the date stamp.

12. The method of claim 10, wherein:

the trend type comprises one of a group consisting of;

best-rated products;

newly pinned products;

best seller products;

recently reviewed products;

just sold products; and

products mentioned in other social platforms.

13. The method of claim 10, further comprising:

transmitting a set of product identifiers to the user device of the user for display at the user device in response to the query.

20. The method of claim 10, wherein:

the key of the category row further comprises a date stamp; and

the key of the trend type row further comprises the date stamp;

the trend type comprises one of a group consisting of;

best-rated products;

newly pinned products;

best seller products;

recently reviewed products;

just sold products; and

products mentioned in other social platforms; and

the method further comprises;

transmitting a set of product identifiers to the user device of the user for display at the user device in response to the query.