# US 2014/0195544 A1 · Demographic and media preference prediction using media content data analysis > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0195544 A1 - Document ID: US20140195544A1 - Status: Published application - Kind: A1 - Application number: 14/208,363 - Filed: 2014-03-13 - Published: 2014-07-10 - Earliest claimed: 2012-03-29 - Assignees: The Echo Nest Corporation - Applicants: The Echo Nest Corporation - Inventors: Brian Whitman - Web page: https://patentgrep.com/patent/US20140195544A1 ## CPC - G06Q 30/02: Marketing; Price estimation or determination; Fundraising - G06F 16/435: Filtering based on additional data, e.g. user or group profiles - G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract Methods, systems and computer program products are provided for predicting data. A name or title is obtained from a taste profile. There is an index into a data set based on the name or title, and a set of terms and corresponding term weights associated with the name or title are retrieved. A sparse vector is constructed based on the set of terms and term weights. The sparse vector is input to a training model including target data. The target data includes a subset of test data which has a correspondence to a predetermined target metric of data. A respective binary value and confidence level is output for each term, corresponding to an association between the term and the target metric. ## Claims ### Claim 1 A system for predicting data, comprising: a processor configured to: obtain a name or title from a taste profile; index into a data set based on the name or the title, and retrieve a set of terms and corresponding term weights associated with the name or the title; construct a sparse vector based on the set of terms and term weights; identify a target brand or segment of interest; generate a first list of accounts who follow the brand or segment of interest by examining social media data, and a second list of additional entities followed by accounts in the first list; filter the second list through a space mapping that maps entities to names or titles from taste profiles, to generate a subset of test data having a correspondence to the target brand or segment of interest; input the sparse vector to a training model including target data, wherein the target data includes the subset of test data having a correspondence to the target brand or segment of interest, and output a respective binary value and confidence level for each term, corresponding to an association between the term and the target brand or segment of interest. ### Claim 2 (depends on 1) The system according to claim 1, wherein the first list of accounts is generated by selecting accounts who follow the target brand or segment of interest in a random sampling fashion. ### Claim 3 (depends on 1) The system according to claim 1, wherein the processor is further configured to identify and eliminate false or suspicious accounts from the first list. ### Claim 4 (depends on 3) The system according to claim 3, wherein an account is identified as false or suspicious when the account has less than a threshold level of activity. ### Claim 5 (depends on 1) The system according to claim 1, wherein the taste profile includes multiple names or titles, and wherein the sparse vector is constructed from terms in the data set corresponding to all names or titles in the taste profile. ### Claim 6 (depends on 1) The system according to claim 1, wherein the processor is further configured to filter the output to only those terms with a confidence level above a set threshold. ### Claim 7 (depends on 1) The system according to claim 1, wherein the test data comprises a set of data determined to be associated with the target metric, as a ground truth for the learning model. ### Claim 8 (depends on 1) The system according to claim 1, wherein the processor is further configured to resolve a name or title from multiple different textual representations. ### Claim 9 (depends on 1) The system according to claim 1, wherein one entity can have multiple taste profiles, or multiple entities can share a single taste profile. ### Claim 10 A method for predicting data, comprising: obtaining a name or title from a taste profile; indexing into a data set based on the name or the title, and retrieve a set of terms and corresponding term weights associated with the name or the title; constructing a sparse vector based on the set of terms and term weights; identifying a target brand or segment of interest; generating a first list of accounts who follow the brand or segment of interest by examining social media data, and a second list of additional entities followed by accounts in the first list; filtering the second list through a space mapping that maps entities to names or titles from taste profiles, to generate a subset of test data having a correspondence to the target brand or segment of interest; inputting the sparse vector to a training model including target data, wherein the target data includes the subset of test data having a correspondence to the target brand or segment of interest, and outputting a respective binary value and confidence level for each term, corresponding to an association between the term and the target brand or segment of interest. ### Claim 11 (depends on 10) The method according to claim 10, wherein the first list of accounts is generated by selecting accounts who follow the target brand or segment of interest in a random sampling fashion. ### Claim 12 (depends on 10) The method according to claim 10, further comprising identifying and eliminating false or suspicious accounts from the first list. ### Claim 13 (depends on 12) The method according to claim 12, wherein an account is identified as false or suspicious when the account has less than a threshold level of activity. ### Claim 14 (depends on 10) The method according to claim 10, wherein the taste profile includes multiple names or titles, and wherein the sparse vector is constructed from terms in the data set corresponding to all names or titles in the taste profile. ### Claim 15 (depends on 10) The method according to claim 10, wherein the method further includes filtering the output to only those terms with a confidence level above a set threshold. ### Claim 16 (depends on 10) The method according to claim 10, wherein the test data comprises a set of data determined to be associated with the target metric, as a ground truth for the learning model. ### Claim 17 (depends on 10) The method according to claim 10, wherein the method further includes resolving a name or title from multiple different textual representations. ### Claim 18 (depends on 10) The method according to claim 10, wherein one entity can have multiple taste profiles, or multiple entities can share a single taste profile. ### Claim 19 A computer-readable medium having stored thereon sequences of instructions, the sequences of instructions including instructions which when executed by a computer system causes the computer system to perform a method for predicting data, the method comprising: obtaining a name or title from a taste profile; indexing into a data set based on the name or the title, and retrieve a set of terms and corresponding term weights associated with the name or the title; constructing a sparse vector based on the set of terms and term weights; identifying a target brand or segment of interest; generating a first list of accounts who follow the brand or segment of interest by examining social media data, and a second list of what additional entities are followed by accounts in the first list; filtering the second list through a space mapping that maps entities to names or titles from taste profiles, to generate a subset of test data having a correspondence to the target brand or segment of interest; inputting the sparse vector to a training model including target data, wherein the target data includes the subset of test data having a correspondence to the target brand or segment of interest, and outputting a respective binary value and confidence level for each term, corresponding to an association between the term and the target brand or segment of interest. ### Claim 20 (depends on 19) The computer-readable medium according to claim 19, wherein the first list of accounts is generated by selecting accounts who follow the target brand or segment of interest in a random sampling fashion.