Architecture for predicting network access probability of data files accessible over a computer network

• US 10,621,137 B2
• Filed: 04/05/2016
• Issued: 04/14/2020
• Est. Priority Date: 04/05/2016
• Status: Active Grant

• Current Assignee: Shutterstock Incorporated
• Original Assignee: Shutterstock Incorporated
• Inventors: Chavez, Alexander, Chester, David, Hohwald, Heath, Hurst, Nathan, Lester, Kevin Scott, Lev-Tov, Manor
• Primary Examiner(s): Singh, Gurkanwaljit
• Application Number:
  US15/091,273
• Publication Number:
  US 20170286979A1
• Time in Patent Office: 1,470 Days
• Field of Search: None
• CPC Class Codes: G06F 15/16 Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs coordinating program control therefor G06F9/52; in regulating and control system G05B
• CPC Class Codes: G06F 16/288 Entity relationship models
• CPC Class Codes: G06F 16/435 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06N 7/06 Simulation on general purpose computers
• CPC Class Codes: G06Q 20/04 Payment circuits
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: H04L 9/40 Network security protocols
• CPC Class Codes: H04N 7/16 Analogue secrecy systems; Analogue subscription systems

Abstract

Methods for predicting network access probability of data files accessible over a computer network are provided. In one aspect, a method includes generating a primary data vector for a media file based on a stored data representation of the file, and providing the data vector for the file to an algorithm that uses past interaction information for at least one other media file from a collection of media files having a degree of similarity with the media file above a threshold similarity value. The method also includes receiving, as an output of the algorithm, a marketability score for the media file, the score indicative of a likelihood that a user will download the media file. Systems and machine-readable media are also provided.

Claims

1. A method comprising:

generating a primary data vector for a media file based on a stored data representation of the media file;

providing the data vector for the media file to an algorithm for predicting a marketability of the media file based on past interaction information for a plurality of other media files from a collection of media files having a degree of similarity with the media file above a threshold similarity value, wherein the algorithm for predicting a marketability of the media file is configured to;

generate a plurality of other data vectors for the media files in the collection of media files and a download indicator identifying of whether the media file corresponding to the data vector was previously downloaded;

provide the plurality of other data vectors and their corresponding download indicators to a supervised learning algorithm to generate a mapping function that maps a vector representation of an input media file to a probability that the input media file will be downloaded;

apply the mapping function generated by the supervised learning algorithm to the primary data vector for the media file to generate a probability that the media file will be downloaded; and

generate a marketability score for the media file based on the probability that the media file will be downloaded;

receiving, as an output of the algorithm, a marketability score for the media file, the marketability score indicative of a likelihood that a user will download the media file; and

providing the media file and media search results for display to another user, the media file ordered among the media search results based on the marketability score for the media file and marketability scores for the media search results.

2. The method of claim 1, wherein the other data vector corresponding to a media file in the collection and its corresponding download indicator is provided to the supervised learning algorithm according to the number of times the corresponding media file has been downloaded by a user.

3. A system comprising:

a memory comprising a media file; and

a processor configured to execute instructions to;

generate a primary data vector for a media file based on a stored data representation of the media file;

provide the data vector for the media file to an algorithm for predicting a marketability of the media file based on past interaction information for a plurality of other media files from a collection of media files having a degree of similarity with the media file above a threshold similarity value, wherein the algorithm for predicting a marketability of the media file is configured to;

generate a plurality of other data vectors for the media files in the collection of media files and a download indicator identifying of whether the media file corresponding to the data vector was previously downloaded;

provide the plurality of other data vectors and their corresponding download indicators to a supervised learning algorithm to generate a mapping function that maps a vector representation of an input media file to a probability that the input media file will be downloaded;

apply the mapping function generated by the supervised learning algorithm to the primary data vector for the media file to generate a probability that the media file will be downloaded; and

generate a marketability score for the media file based on the probability that the media file will be downloaded;

receive, as an output of the algorithm, a marketability score for the media file, the marketability score indicative of a likelihood that a user will download the media file; and

provide the media file and media search results for display to another user, the media file ordered among the media search results based on the marketability score for the media file and marketability scores for the media search results.

4. The system of claim 3, wherein the other data vector corresponding to a media file in the collection and its corresponding download indicator is provided to the supervised learning algorithm according to the number of times the corresponding media file has been downloaded by a user.

5. A non-transitory machine-readable storage medium comprising machine-readable instructions for causing a processor to execute a method comprising:

generating a primary data vector for a media file based on a stored data representation of the media file;

providing the data vector for the media file to an algorithm for predicting a marketability of the media file based on past interaction information for a plurality of other media files from a collection of media files having a degree of similarity with the media file above a threshold similarity value, wherein the algorithm for predicting a marketability of the media file is configured to;

generate a plurality of other data vectors for the media files in the collection of media files and a download indicator identifying of whether the media file corresponding to the data vector was previously downloaded;

provide the plurality of other data vectors and their corresponding download indicators to a supervised learning algorithm to generate a mapping function that maps a vector representation of an input media file to a probability that the input media file will be downloaded;

apply the mapping function generated by the supervised learning algorithm to the primary data vector for the media file to generate a probability that the media file will be downloaded; and

generate a marketability score for the media file based on the probability that the media file will be downloaded;

receiving, as an output of the algorithm, a marketability score for the media file, the marketability score indicative of a likelihood that a user will download the media file; and

provide the media file and media search results for display to another user, the media file ordered among the media search results based on the marketability score for the media file and marketability scores for the media search results.