Brain operating system

Abstract

Embodiments may provide an intelligent adaptive system that combines input data types, processing history and objectives, research knowledge, and situational context to determine the most appropriate mathematical model, choose the computing infrastructure, and propose the best solution for a given problem. For example, a method implemented in a computer may comprise receiving, at the computer system, data relating to a problem to be solved, generating, at the computer system, a description of the problem, wherein the description conforms to defined format, obtaining, at the computer system, at least one machine learning model relevant to the problem, selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model relevant to the problem, and executing, at the computer system, the at least one machine learning model relevant to the problem using the selected computing infrastructure to generate at least one recommendation relevant to the problem.

What is claimed is: 
     
         1 . A method implemented in a computer comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the method comprising:
 receiving, at the computer system, data relating to a problem to be solved;   generating, at the computer system, a description of the problem, wherein the description conforms to defined format;   obtaining, at the computer system, at least one machine learning model relevant to the problem;   selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model relevant to the problem; and   executing, at the computer system, the at least one machine learning model relevant to the problem using the selected computing infrastructure to generate at least one recommendation relevant to the problem.   
     
     
         2 . The method of  claim 1 , wherein the data relating to the problem to be solved comprises at least one of data from sensors, data from devices, data from servers, data from robots, and data from humans. 
     
     
         3 . The method of  claim 2 , wherein the at least one machine learning model relevant to the problem is obtained by at least one of:
 selecting, at the computer system, at least one model from among previously used processed models stored at the computer system;   selecting, at the computer system, at least one model from among models obtained from public sources, proprietary sources, or both; and   generating, at the computer system, a new model based on type, morphology, and parameter information.   
     
     
         4 . The method of  claim 2 , wherein the at least one machine learning model relevant to the problem is further obtained by:
 determining, at the computer system, a combination of the selected and generated models that produces higher accuracy results than the selected and generated models; and   assembling, at the computer system, a combination of the selected and generated models based on the determination of the combination of the selected and generated models that produces higher accuracy results than the selected and generated models.   
     
     
         5 . The method of  claim 4 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics. 
     
     
         6 . The method of  claim 5 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics. 
     
     
         7 . The method of  claim 5 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by a machine learning model. 
     
     
         8 . A system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor to perform:
 receiving data relating to a problem to be solved;   generating a description of the problem, wherein the description conforms to defined format;   obtaining at least one machine learning model relevant to the problem;   selecting computing infrastructure upon which to execute the at least one machine learning model relevant to the problem; and   executing the at least one machine learning model relevant to the problem using the selected computing infrastructure to generate at least one recommendation relevant to the problem.   
     
     
         9 . The system of  claim 8 , wherein the data relating to the problem to be solved comprises at least one of data from sensors, data from devices, data from servers, data from robots, and data from humans. 
     
     
         10 . The system of  claim 9 , wherein the at least one machine learning model relevant to the problem is obtained by at least one of:
 selecting at least one model from among previously used processed models stored at the computer system;   selecting at least one model from among models obtained from public sources, proprietary sources, or both; and   generating a new model based on type, morphology, and parameter information.   
     
     
         11 . The system of  claim 9 , wherein the at least one machine learning model relevant to the problem is obtained by at least two of:
 selecting at least one model from among previously used processed models stored at the computer system;   selecting at least one model from among models obtained from public sources, proprietary sources, or both;   generating a new model based on type, morphology, and parameter information;   determining a combination of the selected and generated models that produces higher accuracy results than the selected and generated models; and   assembling a combination of the selected and generated models based on the determination of the combination of the selected and generated models that produces higher accuracy results than the selected and generated models.   
     
     
         12 . The system of  claim 11 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics. 
     
     
         13 . The system of  claim 12 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics. 
     
     
         14 . The system of  claim 12 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by a machine learning model. 
     
     
         15 . A computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising:
 receiving, at the computer system, data relating to a problem to be solved;   generating, at the computer system, a description of the problem, wherein the description conforms to defined format;   obtaining, at the computer system, at least one machine learning model relevant to the problem;   selecting, at the computer system, computing infrastructure upon which to execute the at least one machine learning model relevant to the problem; and   executing, at the computer system, the at least one machine learning model relevant to the problem using the selected computing infrastructure to generate at least one recommendation relevant to the problem.   
     
     
         16 . The computer program product of  claim 15 , wherein the data relating to the problem to be solved comprises at least one of data from sensors, data from devices, data from servers, data from robots, and data from humans. 
     
     
         17 . The computer program product of  claim 16 , wherein the at least one machine learning model relevant to the problem is obtained by at least one of:
 selecting, at the computer system, at least one model from among previously used processed models stored at the computer system;   selecting, at the computer system, at least one model from among models obtained from public sources, proprietary sources, or both; and   generating, at the computer system, a new model based on type, morphology, and parameter information.   
     
     
         18 . The computer program product of  claim 16 , wherein the at least one machine learning model relevant to the problem is obtained by at least two of:
 selecting, at the computer system, at least one model from among previously used processed models stored at the computer system;   selecting, at the computer system, at least one model from among models obtained from public sources, proprietary sources, or both;   generating, at the computer system, a new model based on type, morphology, and parameter information;   determining, at the computer system, a combination of the selected and generated models that produces higher accuracy results than the selected and generated models; and   assembling, at the computer system, a combination of the selected and generated models based on the determination of the combination of the selected and generated models that produces higher accuracy results than the selected and generated models.   
     
     
         19 . The computer program product of  claim 18 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics. 
     
     
         20 . The computer program product of  claim 19 , wherein the combination of the selected and generated models that produces higher accuracy results than the selected and generated models may be determined by selected and trained heuristics. or by a machine learning model.