# US 12,008,080 B1 · Apparatus and method for directed process generation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,008,080 B1 - Document ID: US12008080B1 - Status: Granted - Kind: B1 - Application number: 18/142,656 - Filed: 2023-05-03 - Granted: 2024-06-11 - Earliest claimed: 2023-05-03 - Assignees: The Strategic Coach Inc. - Applicants: Strategic Coach - Inventors: Barbara Sue Smith; Daniel J. Sullivan - Web page: https://patentgrep.com/patent/US12008080B1 ## CPC - G06F 18/2415: based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate - G06N 3/043: based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS] - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/048: Activation functions - G06N 20/00: Machine learning - G06V 10/945: {User interactive design; Environments; Toolboxes} - G06V 30/191: {Design or setup of recognition systems or techniques; Extraction of features in feature space; Clustering techniques; Blind source separation} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract An apparatus and method. The apparatus including a least a processor configured to: receive a user profile from a profile database, identify a plurality of tasks using the user profile, determine at least an assignable task, receive internal personnel assignment data from a personnel database, determine internal personnel additional tasks, receive posting data, determine external personnel assignment data as a function of the posting data, generate a personnel list as a function of the internal personnel assignment data and the external personnel assignment data, generate at least one personnel assignment for the assignable task as a function of the personnel list; and transmit the at least a personnel assignment to a user device. ## Claims ### Claim 1 An apparatus for directed process generation, wherein the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory containing instructions configuring the at least a processor to: receive a user profile from a user; determine a plurality of user goals as a function of the user profile; receive conditions data from a conditions database; identify, for each of the plurality of user goals, one or more obstacle datum as a function of a corresponding user goal and the conditions data, wherein identifying the one or more obstacle datum comprises: training an obstacle classifier using obstacle training data, wherein the obstacle training data comprises obstacle data inputs correlated to plurality of goals outputs; classifying at least a condition datum of the conditions data to the corresponding user goal using the obstacle classifier; and generating the one or more obstacle datum as a function of the classification; rank the plurality of user goals as a function of a quantity of the one or more obstacle datum associated with each of the plurality of user goals; generate a directed process as a function of the one or more obstacle datum and the plurality of user goals; and display the directed process at a user device. ### Claim 2 (depends on 1) The apparatus of claim 1, wherein the processor is further configured to determine the plurality of user goals as a function of a goal setting machine learning model. ### Claim 3 (depends on 2) The apparatus of claim 2, wherein the processor is further configured to generate a modified goal as a function of the goal setting machine learning model. ### Claim 4 (depends on 3) The apparatus of claim 3, wherein the processor is further configured to identify the one or more obstacle datum as a function of the modified goal. ### Claim 5 (depends on 2) The apparatus of claim 2, wherein the goal setting machine learning model is a neural network. ### Claim 6 (depends on 1) The apparatus of claim 1, wherein the processor is further configured to generate the directed process as a function of the ranking. ### Claim 7 (depends on 1) The apparatus of claim 1, wherein the at least a processor is configured to receive the user profile using an optical character reader. ### Claim 8 (depends on 1) The apparatus of claim 1, wherein the processor is further configured to receive the user profile as a function of an interaction between a user and a chatbot. ### Claim 9 (depends on 1) The apparatus of claim 1, wherein the processor is further configured to generate, using a goal setting machine learning model, a modified goal if a step of the directed process cannot be achieved, wherein the goal setting machine learning model is trained using a goals training set, which includes educational level inputs correlated with change of employment outputs. ### Claim 10 (depends on 1) The apparatus of claim 1, wherein the directed process comprises a plurality of tasks that the user must complete to achieve each of the plurality of user goals. ### Claim 11 A method for directed process generation, wherein the method comprises: receiving, by at least a processor, a user profile from a user; determining, by the at least a processor, at least a user goal as a function of the user profile; receiving, by the at least a processor, conditions data from a conditions database; identifying, by the at least a processor, for each of the plurality of user goals, one or more obstacle datum as a function of a corresponding user goal and the conditions data, wherein identifying the one or more obstacle datum comprises: training an obstacle classifier using an obstacle training data, wherein the obstacle training data comprises obstacle data inputs correlated to plurality of goals outputs; classifying at least a condition datum of the conditions data to the corresponding user goal using the obstacle classifier; and generating the one or more obstacle datum as a function of the classification; ranking the plurality of user goals as a function of a quantity of the one or more obstacle datum associated with each of the plurality of user goals; generating, by the at least a processor, a directed process as a function of the one or more obstacle datum and the plurality of user goals; and transmitting, by the at least a processor, the directed process to a user device. ### Claim 12 (depends on 11) The method of claim 11, wherein the method further comprises determining the plurality of user goals as a function of a goal setting machine learning model. ### Claim 13 (depends on 12) The method of claim 12, wherein the method further comprises generating a modified goal as a function of the goal setting machine learning model. ### Claim 14 (depends on 13) The method of claim 13, wherein the method further comprises identifying the one or more obstacle datum as a function of the modified goal. ### Claim 15 (depends on 12) The method of claim 12, wherein the goal setting machine learning model is a neural network. ### Claim 16 (depends on 11) The method of claim 11, wherein the method further comprises generating the directed process as a function of the ranking. ### Claim 17 (depends on 11) The method of claim 11, wherein the method further comprises receiving the user profile using an optical character reader. ### Claim 18 (depends on 11) The method of claim 11, wherein the method further comprises receiving the user profile as a function of an interaction between a user and a chatbot. ### Claim 19 (depends on 11) The method of claim 11, further comprising generating, using a goal setting machine learning model, a modified goal if a step of the directed process cannot be achieved, wherein the goal setting machine learning model is trained using a goals training set, which includes educational level inputs correlated with change of employment outputs. ### Claim 20 (depends on 11) The method of claim 11, wherein the directed process comprises a plurality of tasks that the user must complete to achieve each of the plurality of user goals.