US-20260127493-A1 - System and Methods of Training and Operation for a General Artificial Intelligence System for Data Generation

Abstract

An artificial intelligence system which, when trained on sample data, may be used to subsequently generate data of arbitrary structure, such as image, geometry, musical or decision logic structures, in a rule based and statistically abiding manner. A method for training the system on sample data is presented, and a method for employing the system for the generation of data when provided additional input data is presented. The produced data is generated by creating a set of valid values with assigned statistical probabilities and reduced to a single value by an arbitrary choosing function. The system and methods are particularly useful for rule-based generation in resource and training data constrained environments requiring rule-abiding dynamic generation solutions.

Inventors

• Joseph Juma

Assignees

• Joseph Juma

Dates

Publication Date

20260507

Application Date

20250804

Claims (5)

1. 1 . An adaptive artificial intelligence system for data model processing, comprising: a data input module configured to receive raw data; a preprocessing module operatively coupled to the data input module and configured to preprocess the raw data to generate preprocessed data; a model training module operatively coupled to the preprocessing module and configured to train a model using the preprocessed data, wherein the model training module comprises an adaptive learning engine configured to adjust training parameters based on performance metrics; an image restoration module operatively coupled to the model training module and configured to apply the trained data model to restore image data; and a final product generation module operatively coupled to the image restoration module and configured to produce a final product based on the restored image data.
2. 2 . The system of claim 1 wherein the data model comprises a plurality of elements with each element comprising an identifier and value of arbitrary data types, the data model further comprising a plurality of functions, with the plurality of functions comprising a fuzzy equality function, a strict equality function, a not equality function and a distance function.
3. 3 . The system of claim 2 wherein the AI system comprises an associative container with the associative container comprising a second data model with the second data model in communication with a weighted table and the AI system further comprising a selection function and a collapse function.
4. 4 . The system of claim 3 wherein the data input module is further configured to accept input from a training sample the training sample comprising a third data model the third data model in communication with an expected value function.
5. 5 . The system of claim 4 wherein the system further comprises a computer, the computer comprising a processor, non volatile computer readable media, non volatile memory and a database of functions, the database of functions.

Description

RELATED PATENT APPLICATION AND INCORPORATION BY REFERENCE This utility application claims the benefit of and priority of application 63/680,062 filed on Aug. 6, 2024, the contents of which are incorporated herein as if restated herein. COPYRIGHT AND TRADEMARK NOTICE This application includes material which is subject or may be subject to copyright and/or trademark protection. The copyright and trademark owner(s) has no objection to the facsimile reproduction by any of the patent disclosure, as it appears in the Patent and Trademark Office files or records, but otherwise reserves all copyright and trademark rights whatsoever. BACKGROUND OF THE INVENTION (1) Field of the Invention The disclosed embodiments relate to the field of artificial intelligence used with servers, processors, video graphic processors, specialized database structures and non-volatile computer readable media. More particularly, the embodiments relate to structures of artificial intelligence systems and process of training and employing an artificial intelligence system for the generation of data, such as but not limited to, image, voxel, musical, graph data, and dynamic decision making logics, such as those found in video games, procedural generation tools and simulation software. BRIEF SUMMARY OF THE INVENTION Disclosed embodiments include systems and methods for training an artificial intelligence system on data to define relationships which may then be used to generate data, or perform dynamic decision making, in accordance with those relationships. One aspect of the invention provides a data structure for storing associations between input data and output statistical distributions. Another aspect of the invention is a process for extracting data from samples and inserting them into the associative system. In another aspect, a method for employing the associative data structure for rule-based generation of data is described. Additional aspects, applications and advantages of the invention will become apparent in view of the following description and associated figures. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a software architecture diagram of the structure of a Data Model object as it pertains to the invention. FIG. 2 is a software architecture diagram of the structure of an AI object as it pertains to the invention. FIG. 3 is a software architecture diagram of the structure of a Training Sample object as it pertains to the invention. FIG. 4A is a flowchart of the general process of training the AI model. FIG. 4B is a flowchart of the specific operations of the AI object when being provided a training sample. FIG. 5A is a flowchart of the general process of employing the AI model for data generation. FIG. 5B is a flowchart of the specific operations of the AI object, for the purpose of generating data. FIG. 6 is a software architecture diagram of the structure of an AI Cache, an optional acceleration structure in the AI. FIG. 7A is a flowchart of one embodiment of the Selection Function, referred to herein as Fuzzy Match. FIG. 7B is a flowchart of one embodiment of the Selection Function, referred to herein as Distance Threshold. FIG. 7C is a flowchart of one embodiment of the Selection Function, referred to herein as Distance Ordering. FIG. 7D is a flowchart, continuing the description of the Distance Ordering (740) Selection function, referenced by FIG. 7C. FIG. 7E is a flowchart, continuing the description of the Distance Ordering (740) Selection function, referenced by FIG. 7D. FIG. 7F is a flowchart of one embodiment of the Selection Function, referred to herein as Distance Gradient. FIG. 7G is a flowchart, continuing the description of the Distance Gradient (760) Selection function, referenced by FIG. 7F. FIG. 7H is a flowchart, continuing the description of the Distance Gradient (760) Selection function, referenced by FIG. 7F. FIG. 8A is a flowchart of one embodiment of the Collapse Function, referred to herein as Random Collapse. FIG. 8B is a flowchart of one embodiment of the Collapse Function, referred to herein as Max Collapse. FIG. 9A is an alteration of the Data Model object (100), for use in an embodiment of the invention in the application of image synthesis, referred to herein as an Image AI. FIG. 9B is an alteration of the Training Model object (300), for use in an embodiment of the invention in the application of image synthesis, referred to herein as an Image AI. FIG. 9C is a flowchart for the general steps for training the Image AI embodiment. FIG. 9D is a flowchart of one embodiment of employing the Image AI embodiment. FIG. 9E is an image with a sample being extracted for training. FIG. 9F is an example of a damaged image. FIG. 9G is the damaged image (970) after having been fixed by application of the Image AI. FIG. 9H is an example of an arbitrary image. FIG. 9I is an example of an image (980) after an application of the Image AI. FIG. 10A is an alteration of the Data Model object (100), for use in an embod