Trend data aggregation

US 10,332,127 B2
Filed: 01/31/2014
Issued: 06/25/2019
Est. Priority Date: 01/31/2014
Status: Active Grant

First Claim

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1. A computer-implemented method comprising:

receiving, with a trend aggregation computer system using one or more processors, a query from a mobile device of a user requesting trend data for at least one or more products, the trend data comprising product reviews, social media message mentions, or product transactions, wherein the trend aggregation computer system comprises a backend data collector, a trend database, a trend server, and a web server, wherein the web server is in data communication with the trend server to query the trend server for one or more trending products determined to be trending at a particular point in time based on the query;

transmitting, by the trend aggregation computer system, a graphical user interface through an app on the mobile device of the user, wherein the graphical user interface comprises a webpage encoded in a markup language to display a representation of the trend data to the user for the one or more trending products at the particular point in time;

retrieving, with the trend aggregation computer system, the trend data from one or more trend actions related to the at least one or more products, the one or more trend actions being from one or more online social networks, transaction databases, product review databases, or webpages;

at the backend data collector, searching the one or more online social networks for the social media message mentions of the at least one or more products associated with the trend data related to the query of the user;

at the backend data collector, analyzing text strings from the social media message mentions for a positive term or a negative term of the at least one or more products to determine whether a product of the at least one or more products is a trending product of the one or more trending products;

at the backend data collector, analyzing transaction records of the at least one or more products purchased from the transaction databases, the transaction records comprising product identification codes, pricing information, and transaction dates;

at the backend data collector, analyzing the product reviews of the at least one or more products from the product review databases with quantifiable ratings given by one or more reviewers;

at the backend data collector, assigning a score to each of one or more of the social media message mentions of the at least one or more products associated with the one or more trend actions based at least in part on whether each of the one or more of the social media message mentions of the at least one or more products associated with the one or more trend actions have the positive term or the negative term;

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

a product identifier;

a category identifier;

a trend type action; and

the score of each of the one or more of the social media message mentions of the at least one or more products associated with 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 the trend database, 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 single product category;

the product identifier represents the product of the at least one or more products within the single product category; and

a new category row is created each day in the trend database;

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 of the one or more category columns comprises 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 a trend action of the one or more trend actions; and

a new category column is created each day in the trend database;

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 of the at least one or more products;

from the trend server, reading timestamps on the one or more category columns and the one or more product columns;

from the trend server, constructing a depiction of the at least one or more products that are trending at the particular point in time;

from the trend server, analyzing known information stored in a user database about the user to determine a set of trending products from the at least one or more products trending at the particular point in time to display, the set of trending products being associated with the query of the user, the known information comprising a predetermined set of filters determined by the user comprising specific options to view specific trend types or product categories; and

at the web server, sending computer-readable data with instructions to display to the graphical user interface of the app on the mobile device of the user, a first display list of the set of trending products with trending product information, wherein as the user scrolls to a bottom of the first display list, a second display list from an earlier time than the first display list is loaded onto the graphical user interface, the trending product information comprises product specific information, a price, stock availability, or the product reviews.

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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.

25 Citations

20 Claims

1. A computer-implemented method comprising:
- receiving, with a trend aggregation computer system using one or more processors, a query from a mobile device of a user requesting trend data for at least one or more products, the trend data comprising product reviews, social media message mentions, or product transactions, wherein the trend aggregation computer system comprises a backend data collector, a trend database, a trend server, and a web server, wherein the web server is in data communication with the trend server to query the trend server for one or more trending products determined to be trending at a particular point in time based on the query;
  
  transmitting, by the trend aggregation computer system, a graphical user interface through an app on the mobile device of the user, wherein the graphical user interface comprises a webpage encoded in a markup language to display a representation of the trend data to the user for the one or more trending products at the particular point in time;
  
  retrieving, with the trend aggregation computer system, the trend data from one or more trend actions related to the at least one or more products, the one or more trend actions being from one or more online social networks, transaction databases, product review databases, or webpages;
  
  at the backend data collector, searching the one or more online social networks for the social media message mentions of the at least one or more products associated with the trend data related to the query of the user;
  
  at the backend data collector, analyzing text strings from the social media message mentions for a positive term or a negative term of the at least one or more products to determine whether a product of the at least one or more products is a trending product of the one or more trending products;
  
  at the backend data collector, analyzing transaction records of the at least one or more products purchased from the transaction databases, the transaction records comprising product identification codes, pricing information, and transaction dates;
  
  at the backend data collector, analyzing the product reviews of the at least one or more products from the product review databases with quantifiable ratings given by one or more reviewers;
  
  at the backend data collector, assigning a score to each of one or more of the social media message mentions of the at least one or more products associated with the one or more trend actions based at least in part on whether each of the one or more of the social media message mentions of the at least one or more products associated with the one or more trend actions have the positive term or the negative term;
  
  at the backend data collector, receiving a dataset regarding the one or more trend actions, the dataset comprising;
  
  a product identifier;
  
  a category identifier;
  
  a trend type action; and
  
  the score of each of the one or more of the social media message mentions of the at least one or more products associated with 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 the trend database, 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 single product category;
  
  the product identifier represents the product of the at least one or more products within the single product category; and
  
  a new category row is created each day in the trend database;
  
  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 of the one or more category columns comprises 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 a trend action of the one or more trend actions; and
  
  a new category column is created each day in the trend database;
  
  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 of the at least one or more products;
  
  from the trend server, reading timestamps on the one or more category columns and the one or more product columns;
  
  from the trend server, constructing a depiction of the at least one or more products that are trending at the particular point in time;
  
  from the trend server, analyzing known information stored in a user database about the user to determine a set of trending products from the at least one or more products trending at the particular point in time to display, the set of trending products being associated with the query of the user, the known information comprising a predetermined set of filters determined by the user comprising specific options to view specific trend types or product categories; and
  
  at the web server, sending computer-readable data with instructions to display to the graphical user interface of the app on the mobile device of the user, a first display list of the set of trending products with trending product information, wherein as the user scrolls to a bottom of the first display list, a second display list from an earlier time than the first display list is loaded onto the graphical user interface, the trending product information comprises product specific information, a price, stock availability, or the product reviews.
- View Dependent Claims (2, 3, 4, 5, 6, 13, 14)
- - 2. The computer-implemented method of claim 1, wherein:
    - the dataset further comprises a timestamp.
  - 3. The computer-implemented method of claim 2, wherein:
    - the value for the product column comprises the timestamp; and
      
      the key for the category row comprises the timestamp.
  - 4. The computer-implemented method of claim 1, wherein:
    - the value for the product column comprises a counter value;
      
      the key for the category row comprises the counter value; and
      
      the computer-implemented method further comprises;
      
      initializing the product column and setting the counter value to one; and
      
      initializing the category column and setting the counter value to one.
  - 5. The computer-implemented method of claim 1, wherein the trend action 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:
    - the value for the product column comprises a concatenation of a timestamp and a counter value;
      
      the key for the category row matches the value for the product column;
      
      the counter value corresponds to a count of occurrences of products in the dataset during a selected time interval, the products comprising the product;
      
      the timestamp reflects a start of a time interval.
  - 13. The computer-implemented method of claim 1, wherein:
    - transmitting, with the trend aggregation computer system, 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;
      
      orone or more purchases by the user.
  - 14. The computer-implemented method of claim 1, wherein:
    - transmitting, with the trend aggregation computer system, 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;
      
      orone or more purchases by the user;
      
      the dataset further comprises a timestamp;
      
      the key for the product column comprises the timestamp and a counter value;
      
      the key for the category column comprises the timestamp and the counter value;
      
      the key for the category column matches the key for the product column;
      
      the trend action 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;
      
      the counter value corresponds to a count of occurrences of products in the dataset during a selected time interval, the products comprising the product;
      
      the timestamp reflects a start of a time interval; and
      
      the method further comprises;
      
      initializing the product column and setting the counter value to one; and
      
      initializing the category column and setting the counter value to one.

7. A trend aggregation computer system for providing trending product information, comprising:
- a trend database comprising;
  
  a trend type row, comprising at least one category column; and
  
  a category row, comprising at least one product column;
  
  a backend data collector adapted to;
  
  search one or more online social networks for one or more social media message mentions of a product associated with a trend action of one or more trend actions, the one or more trend actions comprising online social networks, transaction databases, product review databases, or webpages;
  
  analyze text strings for a positive term or negative term comprising the one or more social media message mentions of the product associated with the trend action of the one or more trend actions to determine whether the product is a trending product;
  
  assign a score to each of the one or more social media message mentions of the product associated with the trend action of the one or more trend actions based at least in part on whether each of the one or more social media message mentions of the product associated with the trend action of the one or more trend actions comprises the positive term or the negative term;
  
  retrieve transaction records of the product in the transaction databases comprising product identification codes, pricing information, or transaction dates;
  
  retrieve product reviews from one or more product review databases;
  
  receive a dataset regarding the one or more trend actions, the dataset comprising;
  
  a product identifier;
  
  a category identifier;
  
  a trend type action; and
  
  the score of each of the one or more social media message mentions of the product associated with the one or more trend actions;
  
  sort the dataset to populate the at least one product column of one or more product columns in the category row of the trend database, wherein;
  
  a key for the category row comprises the category identifier;
  
  a value for the at least one product column comprises the product identifier;
  
  the category identifier represents a single product category; and
  
  the product identifier represents the product of at least one or more products within the single product category;
  
  sort the dataset to populate the at least one category column of 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; and
  
  a value for the at least one category column of the one or more category columns 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 a trend action of the one or more trend actions;
  
  a new category column is created each day in the trend database;
  
  a trend server adapted to;
  
  receive a query from a mobile device of a user for one or more trending products, the one or more trending products comprising the product;
  
  transmit a graphical user interface through as an app on the mobile device of the user, wherein the graphical user interface comprises a webpage encoded in a markup language to display a representation of trend data to the user for the one or more trending products at a particular point in time;
  
  retrieving, with the trend aggregation computer system, the trend data from the one or more trend actions related to the at least one or more products, the one or more trend actions being 3from the one or more online social networks, the transaction databases, the product review databases, or the webpages;
  
  search the trend database to identify at least one trending product of the one or more trending products by;
  
  searching the at least one category column, thereby identifying a product category;
  
  searching the at least one product column, thereby identifying the product; and
  
  analyzing known information stored in a user database about the to determine a set of trending products from the at least one or more products trending at the particular point in time to display, the one or more trending products comprising the set of trending products being associated with the query of the user the known information comprising a predetermined set of filters determined by the user comprising specific options to view specific trend types or product categories;
  
  construct a depiction of at least one or more trending products that are trending at the particular point in time;
  
  a web server adapted to;
  
  query the trend server for the one or more trending products determined to be trending at the particular point in time based on the query; and
  
  send computer-readable data with instructions to display to the graphical user interface of the app on the mobile device of the user, a first display list of the set of trending products with the trending product information in response to the query by the user, the trending product information, wherein as the user scrolls to a bottom of the first display list, a second display list from an earlier time than the first display list is loaded onto the graphical user interface, comprising product specific information, a price, stock availability, or the product reviews.
- View Dependent Claims (8, 9, 15, 16, 17, 18)
- - 8. The trend aggregation computer system of claim 7, further comprising a web server adapted to:
    - receive the product identifier from the trend server;
      
      construct a user interface, the user interface comprising the at least one trending product of the one or more trending products; and
      
      transmit the user interface to a user computing device for display to the user.
  - 9. The trend aggregation computer 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.
  - 15. The trend aggregation computer 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;
      
      orone or more purchases by the user.
  - 16. The trend aggregation computer system of claim 7, wherein:
    - the dataset further comprises a timestamp;
      
      a value for the at least one product column comprises the timestamp; and
      
      the key for the category row comprises the timestamp.
  - 17. The trend aggregation computer system of claim 7, wherein:
    - a value for the at least one product column comprises a counter value; and
      
      the key for the category row comprises the counter value.
  - 18. The trend aggregation computer 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;
      
      orone or more purchases by the user;
      
      the dataset further comprises a timestamp;
      
      the key for the at least one product column comprises the timestamp and a counter value;
      
      the key for the at least one category column comprises the timestamp and the counter value;
      
      the trend action 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 trend aggregation computer system further comprises a web server adapted to;
      
      receive the product identifier from the trend server;
      
      construct a user interface, the user interface comprising the at least one trending product of the one or more trending products; and
      
      transmit the user interface to a user computing device for display to the user.

10. A method comprising:
- receiving, with a trend aggregation computer system using one or more processors, a query from a mobile device of a user requesting trend data for at least one or more products, the trend data comprising product reviews, social media message mentions, or product transactions, wherein the trend aggregation computer system comprises a backend data collector, a trend database, a trend server, and a web server, wherein the web server is in data communication with the trend server to query the trend server for one or more trending products determined to be trending at a particular point in time based on the query;
  
  transmitting, by the trend aggregation computer system, a graphical user interface through an app on the mobile device of the user, wherein the graphical user interface comprises a webpage encoded in a markup language to display a representation of the trend data to the user for the one or more trending products at the particular point in time;
  
  retrieving, with the trend aggregation computer system, the trend data from one or more trend actions related to the at least one or more products, the one or more trend actions being from one or more online social networks, transaction databases, product review databases, and webpages;
  
  at the backend data collector, searching the one or more online social networks for the social media message mentions of the at least one or more products related to the query of the user associated with a trend action of the one or more trend actions;
  
  at the backend data collector, analyzing text strings from the social media message mentions for a positive term or negative term of a product of the at least one or more products to determine whether the product is a trending product of the one or more trending products;
  
  at the backend data collector, analyzing transaction records of the at least one or more products purchased in the transaction databases, the transaction databases comprising product identification codes, pricing information, and transaction dates;
  
  at the backend data collector, analyzing product reviews of the at least one or more products from the product review databases with quantifiable ratings given by one or more reviewers;
  
  at the backend data collector, assigning a score to each of one or more of the social media message mentions of the at least one or more products associated with the one or more trend actions based at least in part on whether each of the one or more of the social media message mentions of the at least one or more products associated with the one or more trend actions that have the positive term or the negative term;
  
  at the 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 each of the one or more of the social media message mentions of the product of the at least one or more products 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, 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;
  
  the product column has a value comprising the product identifier; and
  
  a new product column is created each day in the category row;
  
  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;
  
  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; and
  
  a new category column is created each day in the trend database;
  
  at the trend database, searching the category column, thereby identifying the product category;
  
  at the trend database, searching the product column, thereby identifying the at least one or more products;
  
  from the trend server, reading timestamps on one or more category columns and one or more product columns;
  
  from the trend server, constructing a depiction of the at least one or more products that are trending at the particular point in time;
  
  from the trend server, analyzing known information stored in a user database about the user to determine a set of trending products from the at least one or more products trending at the particular point in time to display the set of trending products being associated with the query of the user, the known information comprising a predetermined set of filters determined by the user comprising specific options to view specific trend types or product categories; and
  
  at the web server, sending computer-readable data with instructions to display to the graphical user interface of the app on the mobile device of the user, a first display list of the set of trending products with trending product information, wherein as the user scrolls to a bottom of the first display list, a second display list from an earlier time than the first display list is loaded onto the graphical user interface, the trending product information comprising at least product specific information, a price, stock availability, or the product reviews.
- View Dependent Claims (11, 12, 19, 20)
- - 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.
  - 19. The method of claim 10, wherein:
    - transmitting, with the trend aggregation computer system, 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;
      
      orone or more purchases by the user.
  - 20. The method of claim 10, wherein:
    - transmitting, with the trend aggregation computer system, 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;
      
      orone or more purchases by the user;
      
      the key of the category row further comprises a date stamp;
      
      the key of the trend type row further comprises the date stamp; and
      
      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.

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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

US14/169,780
Publication Number

US 20150220938A1
Time in Patent Office

1,971 Days
Field of Search

705 729
US Class Current
CPC Class Codes

G06Q 30/0201 Market modelling; Market an...

Trend data aggregation

First Claim

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Trend data aggregation

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