Examination Forecast Workflow Using External LLM Mediation
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Solution Overview
Problem
The integration and operation of large language models (LLM) for natural language processing are hindered by facility and cost constraints, necessitating reliance on external services, which limits convenience and reliability in handling tasks such as generating forecasts and argument drafts.
Innovation Solution
A data processing system comprising components that utilize a large language model to generate forecasts and argument drafts based on examination records, allowing users to forecast decisions and create argument drafts effectively, leveraging a database and search engine to extract relevant records and prompts for improved convenience and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a large language model is incorporated and operated locally, then natural language processing capability is significantly increased, but facility requirements and costs increase
Solution Approach 1:
The patent introduces an external service as an intermediary between the user and the large language model. Instead of directly incorporating the LLM locally, the system accesses it through an external service interface, thereby obtaining advanced NLP capabilities without the complexity of local deployment facilities
2Reliability
If a large language model is incorporated and operated locally, then natural language processing capability is significantly increased, but costs increase
Solution Approach 1:
By using an external service as a mediator, the system accesses powerful LLM capabilities without bearing the full cost of local deployment. The external service absorbs the infrastructure costs, allowing users to benefit from advanced NLP at a lower operational cost
3Device complexity
If an external service is used for large language model processing, then facility and cost constraints are satisfied, but convenience and reliability in handling tasks are limited
Solution Approach 1:
The patent merges the external service's LLM processing capability with local data processing functions. The system combines remote AI processing with local data management, creating an integrated workflow that maintains convenience while leveraging external resources
Solution Approach 2:
The system segments tasks between local processing (data preparation, result handling) and external processing (LLM inference). This division allows each component to operate in its optimal environment, maintaining ease of operation while utilizing external services
4Device complexity
If an external service is used for large language model processing, then facility and cost constraints are satisfied, but reliability in handling tasks is limited
Solution Approach 1:
The system merges external LLM processing with local data validation and processing controls. By combining remote AI capabilities with local reliability checks, the system maintains task handling reliability while avoiding the need for full local LLM deployment
Data Source
AI summary
A data processing system including three components is provided. A first component receives a scope of claims, a notice of reasons for refusal, an argument draft, and a forecast. A second component receives a prompt, receives and transmits the forecast to a third component, and performs processing using a large language model. The third component receives the scope of claims, the notice of reasons for refusal, and the argument draft, and shares them. The third component including two subcomponents receives and transmits the forecast to the first component. A first subcomponent performs processing using a database including an examination record and a search engine. The search engine extracts an examination record list in accordance with a query. The system has a function of creating a forecast of an examination result and an argument draft in view of the scope of claims, an examiner, a technical field, and the like.