# US 12,008,409 B1 · Apparatus and a method for determining resource distribution > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,008,409 B1 - Document ID: US12008409B1 - Status: Granted - Kind: B1 - Application number: 18/142,481 - Filed: 2023-05-02 - Granted: 2024-06-11 - Earliest claimed: 2023-05-02 - Assignees: The Strategic Coach Inc. - Applicants: Strategic Coach - Inventors: Barbara Sue Smith; Daniel J. Sullivan - Web page: https://patentgrep.com/patent/US12008409B1 ## CPC - G06F 9/5027: {the resource being a machine, e.g. CPUs, Servers, Terminals} - G06N 20/00: Machine learning - G06Q 10/06311: {Scheduling, planning or task assignment for a person or group} - G06Q 10/06312: {Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling} - 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 An apparatus for determining resource distribution is disclosed. The apparatus comprise at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to extract an entity profile from a user, wherein the entity profile comprises a plurality of function data. The memory instructs the processor to identify a plurality of resource data as a function of the entity profile. The memory instructs the processor to calculate apportionment data associated with the plurality of function data as a function of the plurality of resource data. The memory instructs the processor to generate optimized apportionment data as a function of the apportionment data and the plurality of resource data. The memory instructs the processor to determine a resource distribution as a function of the apportionment data. The memory instructs the processor to present the resource distribution using a display device. ## Claims ### Claim 1 An apparatus for determining resource distribution, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to: receive an entity profile from a user, wherein the entity profile comprises a plurality of function data; identify a plurality of resource data as a function of the entity profile, wherein the plurality of resource data comprises outlier clusters which represent an area of competence; determine apportionment data associated with the plurality of function data as a function of the plurality of resource data; generate optimized apportionment data as a function of the apportionment data and the plurality of resource data, wherein generating the optimized apportionment data comprises: identifying at least one constraint associated with the apportionment data and the plurality of resource data using a constraint machine learning model, wherein identifying the at least one constraint further comprises: receive constraint training data, wherein the constraint training data is a data set that correlates apportionment data to historical constraints; train, iteratively, the constraint machine learning model using the constraint training data, wherein training the constraint machine learning model includes retraining the constraint machine learning model with feedback from previous iterations of the constraint machine learning model; and identify, as a function of the trained constraint machine learning model, the at least one constraint; generating the optimized apportionment data as a function of the at least one constraint; predict a breakaway point as a function of the optimized apportionment data, wherein predicting the breakaway point comprises: receiving breakaway training data correlating optimized apportionment data to a plurality of breakaway points: training a breakaway machine-learning model using the breakaway training data: outputting, by the breakaway machine-learning model, the breakaway point, wherein the breakaway point determines a point in time a goal of an entity will be achieved; determine a resource distribution as a function of the optimized apportionment data and the breakaway point, wherein determining the resource distribution comprises unitizing a distribution machine-learning model trained to correlate the optimized apportionment data and the breakaway a plurality of resource distributions; and present the resource distribution using a display device. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the memory further instructs the processor to generate a function rank for each function associated with the plurality of function data. ### Claim 3 (depends on 1) The apparatus of claim 1, wherein generating optimized apportionment data comprises generating optimized apportionment data as a function of the function rank. ### Claim 4 (depends on 1) The apparatus of claim 1, wherein generating the optimized apportionment data comprises generating the optimized apportionment data using an optimization classifier. ### Claim 5 (depends on 4) The apparatus of claim 4, wherein generating the optimized apportionment data using an optimization classifier comprises: training the optimization classifier using optimization training data, wherein the optimization training data contains a plurality of data entries containing the apportionment data and the plurality of resource data as inputs correlated to the optimized apportionment data as outputs; and generate optimized apportionment data as a function of the apportionment data and the entity model using the trained optimization classifier. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein extracting the entity profile comprises extracting the entity profile using a web crawler. ### Claim 7 (depends on 1) The apparatus of claim 1, wherein extracting the entity profile comprises extracting the entity profile using a chatbot. ### Claim 8 (depends on 1) The apparatus of claim 1, wherein the plurality of resource data comprises time associated with personnel of the entity. ### Claim 9 (depends on 1) The apparatus of claim 1, wherein the apportionment data comprises an estimated completion time associated with the plurality of function data. ### Claim 10 A method for determining resource distribution, wherein the method comprises: receiving, using at least a processor, an entity profile from a user, wherein the entity profile comprises a plurality of function data; identifying, using the at least a processor, a plurality of resource data as a function of the entity profile, wherein the plurality of resource data comprises outlier clusters which represent an area of competence; determining, using the at least a processor, apportionment data associated with the plurality of function data as a function of the plurality of resource data; generating, using the at least a processor, optimized apportionment data as a function of the apportionment data and the plurality of resource data, wherein generating the optimized apportionment data comprises: identifying at least one constraint associated with the apportionment data and the plurality of resource data using a constraint machine learning model, wherein identifying the at least one constraint further comprises: receive constraint training data, wherein the constraint training data is a data set that correlates apportionment data to historical constraints; train, iteratively, the constraint machine learning model using the constraint training data, wherein training the constraint machine learning model includes retraining the constraint machine learning model with feedback from previous iterations of the constraint machine learning model; and identify, as a function of the trained constraint machine learning model, the at least one constraint; generating the optimized apportionment data as a function of the at least one constraint; predicting, using the at least a processor, a breakaway point as a function of the optimized apportionment data, wherein predicting the break point comprises: receiving breakaway training data correlating optimized apportionment data to a plurality of breakaway points; training a breakaway machine-learning model using the breakaway training data; outputting, by the breakaway machine-learning model, the breakaway point, wherein the breakaway point determines a point in time a goal of an entity will be achieved; determining, using the at least a processor, a resource distribution as a function of the optimized apportionment data and the breakaway point, wherein determining the resource distribution comprises unitizing a distribution machine-learning model trained to correlate the optimized apportionment data and the breakaway a plurality of resource distributions; and presenting using the display device the resource distribution. ### Claim 11 (depends on 10) The method of claim 10, wherein the memory further instructs the processor to generate a function rank for each function associated with the plurality of function data. ### Claim 12 (depends on 10) The method of claim 10, wherein generating optimized apportionment data comprises generating optimized apportionment data as a function of the function rank. ### Claim 13 (depends on 10) The method of claim 10, wherein generating the optimized apportionment data comprises generating the optimized apportionment data using an optimization classifier. ### Claim 14 (depends on 13) The method of claim 13, wherein generating the optimized apportionment data using an optimization classifier comprises: training the optimization classifier using optimization training data, wherein the optimization training data contains a plurality of data entries containing the apportionment data and the plurality of resource data as inputs correlated to the optimized apportionment data as outputs; and generate optimized apportionment data as a function of the apportionment data and the entity model using the trained optimization classifier. ### Claim 15 (depends on 10) The method of claim 10, wherein extracting the entity profile comprises extracting the entity profile using a web crawler. ### Claim 16 (depends on 10) The method of claim 10, wherein extracting the entity profile comprises extracting the entity profile using a chatbot. ### Claim 17 (depends on 10) The method of claim 10, wherein the plurality of resource data comprises time associated with age of the entity. ### Claim 18 (depends on 10) The method of claim 10, wherein the apportionment data comprises an estimated completion time associated with the plurality of function data.