Apparatus and method for directed process generation

Patent metadata

Field	Value
Publication number	US-12399956-B2
Application number	US-202318527598-A
Country	US
Kind code	B2
Filing date	Dec 4, 2023
Priority date	May 3, 2023
Publication date	Aug 26, 2025
Grant date	Aug 26, 2025

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.

First claim

What is claimed is:

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 contains instructions configuring the at least a processor to:
communicate, utilizing a user interface, an inquiry to a user;
receive, using the user interface, a user profile from a user, wherein the user profile comprises information related to a vocational goal of the user;
generate a training data classifier as a function of unfiltered training data using a classification algorithm;
filter elements of the unfiltered training data using the training data classifier to generate a plurality of training data sets each containing a plurality of data entries correlating a plurality of user profile data and a plurality of user goal categories;
select at least one filtered training data set of the plurality of training data sets as a function of the vocational goal of the user using the training data classifier;
train a machine-learning model as a function of the at least one filtered training data set;
determine a plurality of user goals as a function of the user profile using the trained machine-learning model;
receive conditions data from a conditions database;
identify, for each of the plurality of user goals, at least an obstacle datum as a function of the plurality of user goals and conditions data;
rank the plurality of user goals as a function of a quantity of each at least an obstacle datum associated with each of the plurality of user goals;
generate a directed process as a function of each at least an obstacle datum and each associated user goal; and
display the directed process at a user device.

2. The apparatus of claim 1 , wherein the user interface includes a graphical user interface, the graphical user interface including a subsection for receiving user goal data.

3. The apparatus of claim 1 , wherein the user interface further includes a chatbot, and the at least a processor is further configured to receive the user profile as a function of an interaction between the user and the chatbot.

4. The apparatus of claim 1 , wherein the at least a processor is configured to receive the user profile using an optical character reader.

5. The apparatus of claim 1 , wherein the at least a processor is configured to receive the use profile using automatic speech recognition.

6. The apparatus of claim 1 , wherein the machine-learning model comprises a goal setting machine-learning model.

7. The apparatus of claim 6 , wherein the at least a processor is further configured to generate a modified goal as a function of the goal setting machine-learning model.

8. The apparatus of claim 7 , wherein the at least a processor is further configured to identify the at least an obstacle datum as a function of the modified goal.

9. The apparatus of claim 6 , wherein the goal setting machine-learning model is a neural network.

10. The apparatus of claim 1 , wherein the at least a processor is further configured to generate the directed process as a function of the ranking.

11. A method for directed process generation, wherein the method comprises:
communicating, by at least a processor and utilizing a user interface, an inquiry to a user;
receiving, by the at least a processor and using the user interface, a user profile from a user, wherein the user profile comprises information related to a vocational goal of the user;
generating, by the at least a processor, a training data classifier as a function of unfiltered training data using a classification algorithm;
filtering, by the at least a processor, elements of the unfiltered training data using the training data classifier to generate a plurality of training data sets each containing a plurality of data entries correlating a plurality of user profile data and a plurality of user goal categories;
selecting, by the at least a processor, at least one filtered training data set of the plurality of training data sets as a function of the vocational goal of the user using the training data classifier;
training, by the at least a processor, a machine-learning model as a function of the at least one filtered training data set;
determining, by the at least a processor, a plurality of user goals as a function of the user profile using the trained machine-learning model;
receiving, by the at least a processor, conditions data from a conditions database;
identifying, by the at least a processor and for each of the plurality of user goals, at least an obstacle datum as a function of the plurality of user goals and conditions data;
ranking, by the at least a processor, the plurality of user goals as a function of a quantity of each 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 each at least an obstacle datum and each associated user goal; and
displaying, by the at least a processor, the directed process at a user device.

12. The method of claim 11 , wherein the user interface includes a graphical user interface, the graphical user interface including a subsection for receiving user goal data.

13. The method of claim 11 , wherein the user interface further includes a chatbot, and the at least a processor is further configured to receive the user profile as a function of an interaction between the user and the chatbot.

14. The method of claim 11 , wherein the at least a processor is configured to receive the user profile using an optical character reader.

15. The method of claim 11 , wherein the at least a processor is configured to receive the use profile using automatic speech recognition.

16. The method of claim 11 , wherein the machine-learning model comprises a goal setting machine-learning model.

17. The method of claim 16 , wherein the at least a processor is further configured to generate a modified goal as a function of the goal setting machine-learning model.

18. The method of claim 17 , wherein the at least a processor is further configured to identify the at least an obstacle datum as a function of the modified goal.

19. The method of claim 16 , wherein the goal setting machine-learning model is a neural network.

20. The method of claim 11 , wherein the at least a processor is further configured to generate the directed process as a function of the ranking.

Assignees

• Strategic Coach
• The Strategic Coach Inc

Inventors

• Smith Barbara Sue
• Sullivan Daniel J

Classifications

• G06N20/00

  Machine learning · CPC title

• G06N3/043

  based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS] · CPC title

• G06N3/048

  Activation functions · CPC title

• G06N3/0464

  Convolutional networks [CNN, ConvNet] · CPC title

• G06V30/191

  Design or setup of recognition systems or techniques; Extraction of features in feature space; Clustering techniques; Blind source separation · CPC title