# US 2023/0410237 A1 · Dynamic education planning methods and systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0410237 A1 - Document ID: US20230410237A1 - Status: Published application - Kind: A1 - Application number: 18/094,931 - Filed: 2023-01-09 - Published: 2023-12-21 - Earliest claimed: 2021-05-25 - Assignees: Formation Labs Inc. - Applicants: Formation Labs Inc. - Inventors: Michael Lawrence Novati; Sophie Zhou Novati; David Tobias Silverman; Matthew Shawn Oates - Web page: https://patentgrep.com/patent/US20230410237A1 ## CPC - G06Q 50/2053: {Education institution selection, admissions, or financial aid} - G06F 16/9024: {Graphs; Linked lists (G06F16/9027 takes precedence)} - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 20/00: Machine learning - G06Q 10/063112: {Skill-based matching of a person or a group to a task} - G06Q 10/1097: {using calendar-based scheduling for task assignment} - G06N 3/088: Non-supervised learning, e.g. competitive learning - 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) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method includes receiving fellow skill graphs and electronic calendar objects; processing the fellow skill graphs and the electronic calendar objects using a trained machine learning model to predict sessions; and displaying the sessions. A computing system includes a processor; and a memory having stored thereon executable instructions that, when executed by a processor, cause the computing system to: receive fellow skill graphs and electronic calendar objects; process the fellow skill graphs and the electronic calendar objects using a trained machine learning model to predict sessions; and display the sessions. A non-transitory computer-readable medium includes executable instructions that, when executed, cause a computer to: receive fellow skill graphs and electronic calendar objects; process the fellow skill graphs and the electronic calendar objects using a trained machine learning model to predict sessions; and display the sessions. ## Claims ### Claim at position 1 1-19. (canceled) ### Claim 20 A computer-implemented method for generating a plurality of education plans for a plurality of fellows, comprising: receiving, via one or more processors, a set of fellow skill graphs, each including one or more respective ratings, and each corresponding to a respective one of the plurality of fellows; receiving, via one or more processors, a set of electronic calendar objects, each corresponding to a respective one of the plurality of fellows; processing, via one or more processors, the set of fellow skill graphs and the set of electronic calendar objects using a trained machine learning model to predict a set of sessions corresponding to the plurality of fellows; and causing, via one or more processors, the set of sessions to be displayed on a roadmap graphical user interface, wherein each of the set of sessions includes a respective session topic, a respective session type, a respective session date, a respective session start time and a respective session duration. ### Claim 21 (depends on 20) The computer-implemented method of claim 20, wherein processing, via the one or more processors, the set of the fellow skill graphs and the set of the electronic calendar objects using the trained machine learning model to predict the set of the sessions corresponding to the plurality of the fellows includes predicting the set of the sessions based on one or more features included within the set of fellow skill graphs. ### Claim 22 (depends on 21) The computer-implemented method of claim 21, wherein the one or more features include at least one of a session name, a session duration, a session mentor, or an indication of one or more fellow-mentees. ### Claim 23 (depends on 20) The computer-implemented method of claim 20, wherein processing, via one or more processors, the set of fellow skill graphs and the set of electronic calendar objects using a trained machine learning model to predict a set of sessions corresponding to the plurality of fellows includes matching one or more of the fellows to one or more of the set of sessions based on one or both of (i) a respective skill level of the one or more of the fellows, and (ii) a respective skill level of the one or more of the set of sessions. ### Claim 24 (depends on 23) The computer-implemented method of claim 23, wherein the matching is based on mentor feedback or fellow feedback. ### Claim 25 (depends on 20) The computer-implemented method of claim 20, wherein the trained machine learning model is an unsupervised machine learning model trained to cluster the set of fellow skill graphs according to skill ratings, and further comprising: processing the set of fellow skill graphs using the unsupervised machine learning model to predict the set of sessions. ### Claim 26 (depends on 20) The computer-implemented method of claim 20, wherein the trained machine learning model is a supervised machine learning model trained using historical skill ratings data, and further comprising: processing the set of fellow skill graphs using the trained machine learning model to generate the set of sessions. ### Claim 27 A computing system for generating a plurality of education plans for a plurality of fellows, comprising: one or more processors; and one or more memories having stored thereon executable instructions that, when executed by one or more processors, cause the computing system to: receive, via one or more processors, a set of fellow skill graphs, each including one or more respective ratings, and each corresponding to a respective one of the plurality of fellows; receive, via one or more processors, a set of electronic calendar objects, each corresponding to a respective one of the plurality of fellows; process, via one or more processors, the set of fellow skill graphs and the set of electronic calendar objects using a trained machine learning model to predict a set of sessions corresponding to the plurality of fellows; and cause, via one or more processors, the set of sessions to be displayed on a roadmap graphical user interface, wherein each of the set of sessions includes a respective session topic, a respective session type, a respective session date, a respective session start time and a respective session duration. ### Claim 28 (depends on 27) The computing system of claim 27, the one or more memories having stored thereon executable instructions that, when executed by one or more processors, cause the computing system to: predict the set of the sessions based on one or more features included within the set of fellow skill graphs. ### Claim 29 (depends on 28) The computing system of claim 28, wherein the one or more features include at least one of a session name, a session duration, a session mentor, or an indication of one or more fellow-mentees. ### Claim 30 (depends on 27) The computing system of claim 27, the one or more memories having stored thereon executable instructions that, when executed by one or more processors, cause the computing system to: match one or more of the fellows to one or more of the set of sessions based on one or both of (i) a respective skill level of the one or more of the fellows, and (ii) a respective skill level of the one or more of the set of sessions. ### Claim 31 (depends on 30) The computing system of claim 30, wherein the matching is based on mentor feedback or fellow feedback. ### Claim 32 (depends on 27) The computing system of claim 27, wherein the trained machine learning model is an unsupervised machine learning model trained to cluster the set of fellow skill graphs according to skill ratings, and the one or more memories having stored thereon executable instructions that, when executed by one or more processors, cause the computing system to: process the set of fellow skill graphs using the unsupervised machine learning model to predict the set of sessions. ### Claim 33 (depends on 27) The computing system of claim 27, wherein the trained machine learning model is a supervised machine learning model trained using historical skill ratings data, and the one or more memories having stored thereon executable instructions that, when executed by one or more processors, cause the computing system to: process the set of fellow skill graphs using the trained machine learning model to generate the set of sessions. ### Claim 34 A non-transitory computer-readable medium having stored thereon executable instructions that, when executed, cause a computer to: receive, via one or more processors, a set of fellow skill graphs, each including one or more respective ratings, and each corresponding to a respective one of a plurality of fellows; receive, via one or more processors, a set of electronic calendar objects, each corresponding to a respective one of the plurality of fellows; process, via one or more processors, the set of fellow skill graphs and the set of electronic calendar objects using a trained machine learning model to predict a set of sessions corresponding to the plurality of fellows; and cause, via one or more processors, the set of sessions to be displayed on a roadmap graphical user interface, wherein each of the set of sessions includes a respective session topic, a respective session type, a respective session date, a respective session start time and a respective session duration. ### Claim 35 (depends on 34) The non-transitory computer-readable medium of claim 34, having stored thereon executable instructions that, when executed, cause a computer to: predict the set of the sessions based on one or more features included within the set of fellow skill graphs. ### Claim 36 (depends on 35) The non-transitory computer-readable medium of claim 35, wherein the one or more features include at least one of a session name, a session duration, a session mentor, or an indication of one or more fellow-mentees. ### Claim 37 (depends on 34) The non-transitory computer-readable medium of claim 34, having stored thereon executable instructions that, when executed, cause a computer to: match one or more of the fellows to one or more of the set of sessions based on one or both of (i) a respective skill level of the one or more of the fellows, and (ii) a respective skill level of the one or more of the set of sessions. ### Claim 38 (depends on 37) The non-transitory computer-readable medium of claim 37, wherein the matching is based on mentor feedback or fellow feedback. ### Claim 39 (depends on 34) The non-transitory computer-readable medium of claim 34, wherein the trained machine learning model is an unsupervised machine learning model trained to cluster the set of fellow skill graphs according to skill ratings, and having stored thereon executable instructions that, when executed, cause a computer to: process the set of fellow skill graphs using the unsupervised machine learning model to predict the set of sessions.