Cognitive Automaton Spatial Temporal Abstraction for Unknown Environments
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Solution Overview
Problem
Current artificial intelligence systems require prior knowledge of the environment and are encumbered by midbrain distractions, limiting their ability to sense and analyze data effectively without prior knowledge, and they struggle to find relationships and correlations with modest computing resources.
Innovation Solution
A cognitive analysis computer device that senses the environment through arrays of sensor elements, performs spatial and temporal abstraction, and compares memory to input and output data to generate learned output pathways without prior knowledge, using context mapping and memory matching to infer accurate conclusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If prior knowledge of the environment is required for AI systems, then the system can make informed decisions, but the system cannot interact intelligently with unknown environments and requires extensive pre-programming
Solution Approach 1:
The patent segments environmental data into distinct data planes (visual, auditory, tactile, etc.) that are processed independently through separate neural pathways. This allows the system to handle multiple types of sensory information simultaneously without requiring comprehensive pre-programming of all possible environmental interactions, thereby improving adaptability while managing complexity.
Solution Approach 2:
The system performs preliminary organization of sensory data into structured data planes and establishes neural pathways in advance, but leaves the specific interpretation and response generation to occur during actual environmental interaction. This preliminary structuring enables efficient processing of unknown environments without requiring exhaustive pre-programming of all possible scenarios.
2Loss of information
If AI systems process all sensed data, then complete environmental information is available, but the system becomes encumbered by midbrain distractions and cannot filter relevant information efficiently
Solution Approach 1:
The patent divides comprehensive environmental data into separate data planes (visual, auditory, tactile, proprioceptive, etc.), each processed through dedicated neural pathways. This segmentation allows the system to retain complete environmental information while processing each type of data efficiently through specialized channels, avoiding the bottleneck of processing all data uniformly.
Solution Approach 2:
The patent introduces intermediate processing layers (thalamus, hippocampus, cortex) that act as mediators between raw sensory input and final decision-making. These intermediate structures filter, organize, and prioritize information from multiple data planes, preventing midbrain distractions from overwhelming the system while maintaining access to complete environmental information.
3Measurement precision
If extensive computing resources are allocated for data analysis, then accurate conclusions can be inferred, but the system cannot operate with modest computing resources
Solution Approach 1:
The patent segments data processing into hierarchical levels (brainstem, thalamus, hippocampus, cortex) with each level handling specific types of processing. This segmentation allows modest computing resources to be distributed across multiple specialized functions rather than requiring one massive processing unit, maintaining conclusion accuracy while reducing overall resource consumption.
Solution Approach 2:
The system performs preliminary filtering and organization of sensory data into structured data planes before detailed analysis. This preliminary action reduces the volume of data requiring intensive processing, enabling accurate conclusions to be inferred with modest computing resources by eliminating redundant information early in the processing pipeline.
4Loss of information
If the system accumulates and processes large volumes of streaming data, then comprehensive analysis is achieved, but the system cannot operate efficiently with limited processing capacity
Solution Approach 1:
The patent organizes large volumes of streaming data into separate data planes (visual, auditory, tactile, etc.), each processed through dedicated neural pathways with specific processing capacities. This segmentation allows the system to handle comprehensive data volumes by distributing the processing load across multiple specialized channels, reducing the complexity requirements of any single processing unit.
Solution Approach 2:
The patent adds the dimension of hierarchical processing levels (brainstem, thalamus, hippocampus, cortex) to the data processing architecture. This dimensional expansion allows the system to manage large data volumes by processing information at multiple levels of abstraction simultaneously, reducing the processing capacity requirements at any single level while maintaining overall data completeness.
Data Source
AI summary
A cognitive analysis computer device is programmed to a) receive a set of streaming input data from one or more sensors; b) perform spatial abstraction on the set of streaming input data to divide the set of streaming input data into a plurality of input pathways; c) route the set of streaming input data from a plurality of input pathways to a plurality of output pathways based on a context mapping; d) for each output pathway, perform temporal abstraction on the received input values to generate an output value for the corresponding output pathway by accumulating output values from a plurality of sets of streaming input data until at least one output value of the plurality of output values on the plurality of output pathways exceeds a predetermined threshold; and e) compare the plurality of output values to one or more stored sets of data to determine a match.