Cross-Domain Structural Mapping With Encoder-Decoder Embeddings
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Solution Overview
Problem
The scalability and efficiency of cross-domain innovation are hindered by the need for expert knowledge and time-consuming manual processes in identifying analogous patterns across different domains, which are both costly and labor-intensive.
Innovation Solution
A computing device employs encoder-decoder models to interrelate dissimilar data corpuses, computing identity mappings, distribution distance metrics, and updating models to streamline interdisciplinary source discovery and matching, using techniques like MRLT distribution distance metrics and joint loss functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual expert processes are used to identify analogous patterns across domains, then mapping precision can be maintained, but productivity and time consumption deteriorate significantly
Solution Approach 1:
The patent introduces an intermediary system consisting of encoder-decoder models and distribution distance metric computations that mediate between dissimilar data corpuses from different domains. This intermediary automatically identifies analogous patterns by computing embeddings and comparing distribution distances, replacing manual expert processes while maintaining mapping precision through sophisticated algorithmic approaches.
Solution Approach 2:
The patent replaces the mechanical system of manual expert analysis with an automated computational system. The encoder-decoder models and distribution distance metrics constitute a mechanical-substitution system that processes data corpuses automatically, eliminating the need for human experts to manually identify analogous patterns across domains.
2Measurement precision
If expert knowledge is required for cross-domain pattern identification, then mapping accuracy is maintained, but device complexity and operational difficulty increase
Solution Approach 1:
The system performs self-service by automatically computing embeddings and distribution distances without requiring external expert knowledge. The encoder-decoder models and metric computations constitute a self-service mechanism that autonomously identifies analogous patterns, eliminating the need for human experts while maintaining accuracy through algorithmic sophistication.
Solution Approach 2:
The patent transforms the problem from requiring expert knowledge to using computational parameters. By changing from manual expert judgment to automated parameter-based computations (embeddings, distribution distances), the system maintains pattern identification accuracy while reducing operational complexity and accessibility barriers.
3Measurement precision
If manual processes are used for interdisciplinary source discovery, then mapping quality can be ensured, but time consumption and loss of time increase
Solution Approach 1:
The automated system enables continuous processing of data corpuses without the interruptions inherent in manual expert processes. The encoder-decoder models and distribution distance computations can operate continuously and parallelly, maintaining mapping quality while dramatically reducing the time required for interdisciplinary source discovery compared to sequential manual analysis.
Data Source
AI summary
A method of using a computing device executing to interrelate two or more corpuses of dissimilar data that includes receiving input data from each of two or more corpuses of dissimilar data. The computing device computes a pass for each of the input data into two or more encoder-decoder models. The computing device further obtains a prediction of an identity mapping for each of different domains of knowledge from each of the two or more encoder-decoder models. The computing device additionally computes a distribution distance metric as an output from each of a low-dimensional embedding vector representation from each of the two or more encoder-decoder models. The computing device still further computes a function based on each of the predictions from each of the two or more encoder-decoder models and the distribution distance metrics. The computing device additionally updates the two or more encoder-decoder models.