Artificial Intelligence Analyzer and Generator

• US 20070282768A1
• Filed: 05/30/2006
• Published: 12/06/2007
• Est. Priority Date: 05/30/2006
• Status: Active Grant

• Current Assignee: Zaracom Technologies Inc.
• Original Assignee: Zaracom Technologies Inc.
• Inventors: Chang, Yuan-Lung, Chang, Yuan-Huei
• Granted Patent: US 7,512,570 B2
• Time in Patent Office: Days
• US Class Current: 706/16
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation

Abstract

This invention relates to using artificial intelligence for analyzing real-life collected data from an operation system, modeling the collected data to identify characteristics of events, analyzing the models to conclude an optimal solution for maximizing the performance of the operation system.

Claims

1. An artificial intelligence system, comprising:

a data partitioner receives first set of plurality types of data, wherein

the data partitioner partitions the first set of data and groups the partitioned

data into plurality of data files, and wherein

the data partitioner generates indices according to the first set of data;

a data modeler receives second set of plurality types of data, wherein the data modeler generates plurality of models according to the first set of data and the second set of data.

2. The artificial intelligence system of claim 1, comprising:

a model analyzer receives third set of plurality types of data wherein the model analyzer analyzes the plurality of models according to the third set of plurality types of data.

3. The artificial intelligence system of claim 2, wherein the indices indicate the data files and the partitioned data in the data files.

4. The artificial intelligence system of claim 3, wherein the indices are generated by using indexing algorithms comprising of R-Tree Indexing, Hash Code Indexing, Reverse Indexing, and Suffix Indexing.

5. The artificial intelligence system of claim 2, wherein the data partitioner partitions the first set of data by selecting an Average value of the first set of data, and by selecting data from eighty percent of normal distribution of the first set of data, and by selecting data from twenty percent of the first set of data wherein the twenty percent of the first set of data were not selected by the selection of the eighty percent of the normal distribution of the first set of data, and by selecting data of maximum values and minimum values from the first set of data, and by selecting data from common occurrence data of the first set of data, and by selecting data from non-common occurrence data of the first set of data.

6. The artificial intelligence system of claim 5, wherein the plurality of models are generated by Finite Impulse Response (FIR) algorithm, and Infinite Impulse Response (IIR) algorithm, and Segmentation algorithm, and Vectorization algorithm, and Fast Fourier Transform (FFT) algorithm, and Z-Transform algorithm, and Differential Run-length Huffman algorithm, and Down Sampling algorithm, and Geometrical Prediction algorithm, and Boundary algorithm, and Hierarchical Linear Modeling algorithm.

7. The artificial intelligence system of claim 6, wherein the model analyzer analyzes the plurality of models by using Singular Value Decomposition (SVD) algorithm, and Vertical Vectorization algorithm, and Vertical Grouping algorithm, and Horizontal Grouping algorithm, and Gravity-Velocity Analysis algorithm, and Bayesian Analysis algorithm, and Error Diffuse Iteration algorithm to generate an optimal model.

8. The artificial intelligence system of claim 7, wherein the model analyzer generates Dynamic Constraints;

and

the model analyzer generates modified optimal models by new Dynamic Constraints.

9. The artificial intelligence system of claim 8, comprising:

an Optimizer, in accordance with the modified optimal models, generates an optimal system performance solution.

10. An artificial intelligence system, comprising:

a data partitioner receives first set of plurality types of data, wherein

the data partitioner partitions the first set of data and groups the partitioned data into plurality of data files, and

the data partitioner generates indices according to the first set of data;

a model analyzer receives second set of data and generates optimal models and Dynamic Constraints.

11. The artificial intelligence system of claim 10, comprising,

a data modeler receives third set of plurality types of data, wherein the data modeler generates plurality of models according to the first set of data and the third set of data.

12. The artificial intelligence system of claim 11, wherein the indices indicates data files and the partitioned data in the data files, and wherein

the indices are generated by using replicate tree indexing, hash code indexing, reverse indexing, and suffix indexing.

13. The artificial intelligence system of claim 11, wherein the data partitioner partitions the first set of data by selecting an Average of the first set of data, and by selecting data from eighty percent of normal distribution of the first set of data, and by selecting data from twenty percent of the first set of data wherein the twenty percent of the first set of data were not selected by the selection of the eighty percent of the normal distribution of the first set of data, and by selecting data of maximum values and minimum values from the first set of data, and by selecting data from common occurrence data of the first set of data, and by selecting data from non-common occurrence data of the first set of data.

14. The artificial intelligence system of claim 13, wherein the plurality of models are generated by Finite Impulse Response (FIR) algorithm, and Infinite Impulse Response (IIR) algorithm, and Segmentation algorithm, and Vectorization algorithm, and Fast Fourier Transform (FFT) algorithm, and Z-Transform algorithm, and Differential Run-length Huffman algorithm, and Down Sampling algorithm, and Geometrical Prediction algorithm, and Boundary algorithm, and Hierarchical Linear Modeling algorithm.

15. The artificial intelligence system of claim 14, wherein the model analyzer analyzes the plurality of models by using Singular Value Decomposition (SVD) algorithm, and Vertical Vectorization algorithm, and Vertical Grouping algorithm, and Horizontal Grouping algorithm, and Gravity-Velocity Analysis algorithm, and Bayesian Analysis algorithm, and Error Diffuse Iteration algorithm to generate optimal models.

16. The artificial intelligence system of claim 15, wherein the model analyzer generates optimal models by Dynamic Constraints;

and

the artificial intelligent system further comprises an Optimizer generates an optimal performance solution based on one of the optimal models.

17. An artificial intelligence system, comprising:

a data partitioner receives first set of plurality types of data, wherein

the data partitioner partitions the first set of data and groups the partitioned data into plurality of data files, and

the data partitioner generates indices according to the first set of data;

a data modeler receives second set of plurality types of data, wherein the data modeler generates plurality of models according to the first set of data and the second set of data; and

a model analyzer receives third set of data wherein the model analyzer analyzes the plurality of models according to the third set of data.

18. The artificial intelligence system of claim 17, wherein the indices indicates data files and the partitioned data in the data files;

and

the indices are generated by using replicate tree indexing, hash code indexing, reverse indexing, and suffix indexing.

19. The artificial intelligence system of claim 18, wherein the data partitioner partitions the first set of data by selecting an Average of the first set of data, and by selecting data from eighty percent of normal distribution of the first set of data, and by selecting data from twenty percent of the first set of data wherein the twenty percent of the first set of data were not selected by the selection of the eighty percent of the normal distribution of the first set of data, and by selecting data of maximum values and minimum values from the first set of data, and by selecting data from common occurrence data of the first set of data, and by selecting data from non-common occurrence data of the first set of data;

and the plurality of models are generated by Finite Impulse Response (FIR) algorithm, and Infinite Impulse Response (IIR) algorithm, and Segmentation algorithm, and Vectorization algorithm, and Fast Fourier Transform (FFT) algorithm, and Z-Transform algorithm, and Differential Run-length Huffman algorithm, and Down Sampling algorithm, and Geometrical Prediction algorithm, and Boundary algorithm, and Hierarchical Linear Modeling algorithm.

20. The artificial intelligence system of claim 19, wherein the model analyzer analyzes the plurality of models by using Singular Value Decomposition (SVD) algorithm, and Vertical Vectorization algorithm, and Vertical Grouping algorithm, and Horizontal Grouping algorithm, and Gravity-Velocity Analysis algorithm, and Bayesian Analysis algorithm, and Error Diffuse Iteration algorithm to generate Vendor Independent Models, and Pattern-Relationship;

and the model analyzer generates optimal models and Dynamic Constraints; and

the artificial intelligent system further comprises an Optimizer wherein the Optimizer generates an optimal solution based on one of the optimal models.