# US 2021/0240787 A1 · Work income visualization and optimization platform > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2021/0240787 A1 - Document ID: US20210240787A1 - Status: Published application - Kind: A1 - Application number: 17/161,747 - Filed: 2021-01-29 - Published: 2021-08-05 - Earliest claimed: 2020-01-31 - Assignees: Steady Platform, Inc. - Applicants: Steady Platform LLC - Inventors: Amanda Miguel; Winn Martin; Jeff Chelko; Marcel Crudele; Jasmine Hentschel; Antonea Nabors; Kabir Shukla; Tyler Howard - Web page: https://patentgrep.com/patent/US20210240787A1 ## CPC - G06F 16/9535: Search customisation based on user profiles and personalisation - G06F 16/9538: Presentation of query results - G06N 20/00: Machine learning - G06N 20/20: Ensemble learning - G06Q 10/063116: {Schedule adjustment for a person or group} - G06Q 40/12: Accounting - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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 ## Abstract Provided are systems and methods that rely on machine learning to recommend employment opportunities. In one example, a method may include identifying, via execution of a first machine learning model, income data and spending data of a user, identifying, via execution of a second machine learning model, skill attributes of the user, determining, via execution of a third machine learning model, a recommended job for the user, where the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model, and displaying, via a user interface, a description of the recommended job. ## Claims ### Claim 1 A computing system comprising: a processor configured to: identify, via execution of a first machine learning model, income data and spending data of a user, wherein the processor inputs financial data of the user into the first machine learning model which outputs the income data and the spending data; identify, via execution of a second machine learning model, skill attributes of a job category, wherein the processor inputs job a description of the user into the second machine learning model which outputs the skill attributes; determine, via execution of a third machine learning model, a recommended job for the user, wherein the processor inputs the outputs from the first and second machine learning models into the third machine learning model which outputs an identifier of the recommended job; and display, via a user interface, a description of the recommended job. ### Claim 2 (depends on 1) The computing system of claim 1, wherein the first machine learning model comprises an ensemble that includes a natural language processing (NLP) algorithm and a decision tree algorithm. ### Claim 3 (depends on 1) The computing system of claim 1, wherein the second machine learning model comprises an ensemble that includes a natural language processing (NLP) algorithm and a clustering algorithm. ### Claim 4 (depends on 1) The computing system of claim 1, wherein the third machine learning model comprises a collaborative filtering model that receives as input vectors output from the first and second machine learning models, and which outputs a vector that identifies the recommended job. ### Claim 5 (depends on 1) The computing system of claim 1, wherein the processor is further configured to receive a schedule of work of the user, and determine, via execution of a fourth machine learning model, a recommended change to the schedule of work of the user, wherein the processor inputs the output of the first machine learning model and the work schedule of the user into the fourth machine learning model and outputs an optimized work schedule. ### Claim 6 (depends on 1) The computing system of claim 1, wherein the processor is further configured to select a template description from among a plurality of template descriptions based on a job category of the recommend job, and insert a description of the recommended job into a blank space within the selected template description. ### Claim 7 (depends on 7) The computing system of claim 7, wherein the processor is configured to at least one of output the filled-in template via a mobile application and transmit the filled-in template via an electronic message. ### Claim 8 A method comprising: identifying, via execution of a first machine learning model, income data and spending data of a user, wherein the identifying the income data and spending data comprises inputting financial data of the user into the first machine learning model which outputs the income data and the spending data; identifying, via execution of a second machine learning model, skill attributes of the user, wherein the identifying the skill attributes comprises inputting a job description of the user into the second machine learning model which outputs the skill attributes; determining, via execution of a third machine learning model, a recommended job for the user, wherein the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model which outputs an identifier of the recommended job; and displaying, via a user interface, a description of the recommended job. ### Claim 9 (depends on 8) The method of claim 8, wherein the first machine learning model comprises an ensemble that includes a natural language processing (NLP) algorithm and a decision tree algorithm. ### Claim 10 (depends on 8) The method of claim 8, wherein the second machine learning model comprises an ensemble that includes a natural language processing (NLP) algorithm and a clustering algorithm. ### Claim 11 (depends on 8) The method of claim 8, wherein the third machine learning model comprises a collaborative filtering model that receives as input vectors output from the first and second machine learning models, and which outputs a vector that identifies the recommended job. ### Claim 12 (depends on 8) The method of claim 8, further comprises receiving a schedule of work of the user, and determining, via execution of a fourth machine learning model, a recommended change to the schedule of work of the user, wherein the fourth machine learning model receives as input the output of the first machine learning model and the work schedule of the user, and outputs an optimized work schedule. ### Claim 13 (depends on 8) The method of claim 8, further comprising selecting a template description from among a plurality of template descriptions based on a job category of the recommend job, and inserting a description of the recommended job into a blank space within the selected template description. ### Claim 14 (depends on 13) The method of claim 13, wherein the displaying comprises at least one of outputting the filled-in template via a mobile application and transmitting the filled-in template via an electronic message. ### Claim 15 A non-transitory computer-readable medium comprising instructions which when executed by a processor cause a computer to perform a method comprising: identifying, via execution of a first machine learning model, income data and spending data of a user, wherein the identifying comprises inputting financial data of the user into the first machine learning model which outputs the income data and the spending data; identifying, via execution of a second machine learning model, skill attributes of the user, wherein the identifying the skill attributes comprises inputting a job description of the user into the second machine learning model which outputs the skill attributes; determining, via execution of a third machine learning model, a recommended job for the user, wherein the determining comprises inputting the outputs from the first and second machine learning models into the third machine learning model which outputs an identifier of the recommended job; and displaying, via a user interface, a description of the recommended job. ### Claim 16 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the first machine learning model comprises an ensemble that includes a natural language processing (NLP) algorithm and a decision tree algorithm. ### Claim 17 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the second machine learning model comprises an ensemble that includes a natural language processing (NLP) algorithm and a clustering algorithm. ### Claim 18 (depends on 15) The non-transitory computer-readable medium of claim 15, wherein the third machine learning model comprises a collaborative filtering model that receives as input vectors output from the first and second machine learning models, and which outputs a vector that identifies the recommended job. ### Claim 19 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprises receiving a schedule of work of the user, and determining, via execution of a fourth machine learning model, a recommended change to the schedule of work of the user, wherein the fourth machine learning model receives as input the output of the first machine learning model and the work schedule of the user, and outputs an optimized work schedule. ### Claim 20 (depends on 15) The non-transitory computer-readable medium of claim 15, further comprising selecting a template description from among a plurality of template descriptions based on a job category of the recommend job, and inserting a description of the recommended job into a blank space within the selected template description, wherein the displaying comprises at least one of outputting the filled-in template via a mobile application and transmitting the filled-in template via an electronic message.