# US 11,443,286 B1 · Methods and systems for exploiting value in certain domains > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,443,286 B1 - Document ID: US11443286B1 - Status: Granted - Kind: B1 - Application number: 17/492,003 - Filed: 2021-10-01 - Granted: 2022-09-13 - Earliest claimed: 2021-10-01 - Assignees: Flourish Worldwide, LLC - Applicants: Flourish Worldwide, LLC - Inventors: Joseph J Janiczek - Web page: https://patentgrep.com/patent/US11443286B1 ## CPC - G06Q 10/1097: {using calendar-based scheduling for task assignment} - G06N 7/02: using fuzzy logic (computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00) - G06N 3/04: Architecture, e.g. interconnection topology - G06N 3/08: Learning methods - G06N 20/00: Machine 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 - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract Aspects relate to methods and systems for exploiting value within certain domains. An exemplary method includes interrogating, using a remote device, a user for scheduling data and at least a domain, wherein the at least a domain includes at least one domain and no more than a predetermined maximum number of domains, receiving, using the remote device, the at least a domain from the user, interrogating, using the remote device, the user for domain-specific data associated with the at least a domain, receiving, using the remote device, the domain-specific data from the user, generating, using a computing device, a domain target for the at least a domain as a function of the domain-specific data, generating, using the computing device, a user schedule as a function of the domain target and the scheduling data, and displaying, using the remote device, the user schedule and the domain target to the user. ## Claims ### Claim 1 A method of exploiting value within certain domains, the method comprising: interrogating, by a computing device, a remote device associated with a user for: scheduling data; and at least a domain, wherein a quantity of domains in the at least a domain is between one and a predetermined maximum number of domains; receiving, by the computing device and from the remote device, the scheduling data and the at least a domain from the user; interrogating, by the computing device, the remote device associated with the user for domain-specific data as a function of the at least a domain; receiving, by the computing device and from the remote device, the domain-specific data from the user; generating, by the computing device, at least a domain target for the at least a domain as a function of the domain-specific data and domain target data generated from a previous user; generating, iteratively, by the computing device, at least a user schedule as a function of the at least a domain target, and the scheduling data wherein generating the at least a user schedule comprises: receiving scheduling training data, wherein the training data correlates a plurality of domain targets with schedule components; sorting the scheduling training data using natural language processing; training a neural network as a function of the sorted scheduling training data; generating the at least a user schedule as a function of the neural network, wherein the neural network receives the plurality of domain targets as inputs and outputs the user schedule; and optimizing the expected loss between the generated user schedule and the scheduling training data as a function of an error function; and displaying, by the computing device and at the remote device, the at least a user schedule and the at least a domain target to the user. ### Claim 2 (depends on 1) The method of claim 1, wherein generating the at least a domain target comprises: inputting the domain-specific data to a target-setting machine learning model; and generating the at least a domain target as a function of the domain-specific data and the target-setting machine learning model. ### Claim 3 (depends on 2) The method of claim 2, further comprising training the target-setting machine learning model, wherein training the target-setting machine learning model comprises: inputting target-setting training data to a machine learning algorithm, wherein the target-setting training data comprises a plurality of domain-specific data correlated to a domain target; and training the target-setting machine learning model as a function of the machine-learning algorithm. ### Claim 4 (depends on 1) The method of claim 1, further comprising training the scheduling machine learning model, wherein training the scheduling machine learning mode comprises: inputting scheduling training data to a machine learning algorithm, wherein the scheduling training data comprises a plurality of domain targets correlated to schedule components; and training the scheduling machine learning model as a function of the machine-learning algorithm. ### Claim 5 (depends on 1) The method of claim 1, further comprising: interrogating, using the remote device, the user for update data; evaluating, using the computing device, the update data as a function of one or more of the at least a user schedule; and displaying, using the remote device, evaluation results to the user. ### Claim 6 (depends on 5) The method of claim 5, wherein the update data includes one or more of objective update data and subjective update data. ### Claim 7 (depends on 5) The method of claim 5, wherein evaluating the update data comprises: inputting the update data and the at least a user schedule to an evaluating machine learning model; and generating the evaluation results as a function of the evaluating machine learning model. ### Claim 8 (depends on 7) The method of claim 7, further comprising training the evaluating machine learning model, wherein training the evaluating machine learning model comprises: inputting evaluating training data to a machine learning algorithm, wherein the evaluating training data comprises a plurality of update data correlated to evaluations; and training the evaluating machine learning model as a function of the machine-learning algorithm. ### Claim 9 (depends on 5) The method of claim 5, further comprising notifying, using the remote device, the user as a function of the evaluation results. ### Claim 10 (depends on 9) The method of claim 9, wherein notifying the user comprises a text message. ### Claim 11 (depends on 9) The method of claim 9, wherein notifying the user comprises an authorized notification. ### Claim 12 (depends on 1) The method of claim 1, wherein the at least a domain target includes a quarterly target. ### Claim 13 (depends on 1) The method of claim 1, wherein the at least a user schedule includes a weekly schedule. ### Claim 14 (depends on 1) The method of claim 1, further comprising: receiving, using the user device, at least a schedule change request from the user; and modifying, using the computing device, the at least a user schedule as a function of the schedule change request. ### Claim 15 A system for exploiting value within certain domains, the system comprising a computing device configured to: interrogate a remote device associated with a user for: scheduling data; and at least a domain, wherein a quantity of domains in the at least a domain is between one and a predetermined maximum number of domains, receive, from the remote device, the scheduling data and the at least a domain from the user; interrogate the remote device associated with the user for domain-specific data as a function of the at least a domain receive, from the remote device, the domain-specific data from the user; generate at least a domain target for the at least a domain as a function of the domain-specific data generating, by the computing device, at least a domain target for the at least a domain as a function of the domain-specific data and domain target data generated from a previous user; generate iteratively at least a user schedule as a function of the at least a domain target, and the scheduling data wherein generating the at least a user schedule comprises: receiving scheduling training data, wherein the training data correlates a plurality of domain targets with schedule components; sorting the scheduling training data using natural language processing; training a neural network as a function of the sorted scheduling training data; generating the at least a user schedule as a function of the neural network, wherein the neural network receives the plurality of domain targets as inputs and outputs the user schedule; and optimizing the expected loss between the generated user schedule and the scheduling training data as a function of an error function; and display, at the remote device, the at least a user schedule and the at least a domain target to the user. ### Claim 16 (depends on 15) The system of claim 15, wherein generating the at least a domain target comprises: inputting the domain-specific data to a target-setting machine learning model; and generating the at least a domain target as a function of the domain-specific data and the target-setting machine learning model. ### Claim 17 (depends on 15) The system of claim 15, wherein the computing device is further configured to: interrogate, using the remote device, the user for update data; evaluate the update data as a function of one or more of the at least a user schedule; and display, using the remote device, evaluation results to the user. ### Claim 18 (depends on 15) The system of claim 15, wherein the computing device is further configured to: receive, using the user device, at least a schedule change request from the user; and modify the at least a user schedule as a function of the schedule change request.