Bayesian Object Modeling With Gradient-Based Probabilistic Inference
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Solution Overview
Problem
Existing technologies require significant computing resources for modeling complex objects, leading to inefficiencies in memory and time usage.
Innovation Solution
A Bayesian object model (BOM) is implemented using a differentiable probabilistic program that encodes structural and kinodynamic attributes of objects, allowing for efficient and automated Bayesian inference through gradient-based updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object modeling methods are used, then modeling accuracy is maintained, but computing resources and memory usage increase significantly
Solution Approach 1:
The patent changes the parameter representation from dense continuous values to discrete symbolic tokens. Object properties are encoded as sequences of tokens representing different attribute categories (e.g., shape, size, material), enabling efficient processing while maintaining modeling fidelity. This parameter transformation reduces computational complexity without sacrificing accuracy.
Solution Approach 2:
The patent replaces traditional geometric and physics-based modeling mechanisms with language model-based probabilistic reasoning. Instead of using computational geometry algorithms and physics engines, the system uses trained language models to infer object properties and relationships from token sequences, significantly reducing computational resource requirements.
2Manufacturing precision
If detailed object modeling is performed, then modeling precision is improved, but time consumption increases
Solution Approach 1:
The patent performs preliminary encoding of object properties into token sequences before detailed analysis. By pre-processing and structuring object attributes as discrete tokens during data preparation, the system enables faster query processing and inference while maintaining precise modeling capabilities during the actual analysis phase.
Solution Approach 2:
The patent segments object properties into distinct categorical tokens (e.g., separate tokens for shape, color, size, material). This segmentation allows the system to process and analyze specific attributes independently and in parallel, reducing overall processing time while maintaining comprehensive modeling precision through the combination of all segmented properties.
3Reliability
If complex object structures are modeled, then completeness of representation is improved, but device complexity increases
Solution Approach 1:
The patent employs a universal token-based representation system that can encode diverse object properties and relationships using the same framework. The same token sequence structure and language model architecture handle different object types, attributes, and spatial relationships, reducing system complexity by eliminating the need for separate modeling mechanisms for different object categories.
Solution Approach 2:
The patent introduces token sequences as an intermediary layer between raw object data and the language model processing. This intermediary representation simplifies the interface between complex object structures and the modeling system, enabling complete representation of object properties while keeping the system architecture manageable through standardized token encoding and decoding processes.
Data Source
AI summary
Apparatuses, systems, and techniques to update a machine learning model associated with an object. In at least one embodiment, the machine learning model is updated based at least in part on, for example, one or more distributions associated with the machine learning model.