Conversational Agent for Cognitive Model Optimization
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Solution Overview
Problem
Non-expert users lack the knowledge and skills to optimize and fine-tune cognitive models due to the complexity of cognitive technologies and software development requirements.
Innovation Solution
A system that uses natural-language conversation to guide users in optimizing cognitive models, performing diagnostics, and recommending fixes, incorporating a conversational agent with natural language understanding and generation, experiment management, and algorithm selection to assist users in improving model performance without requiring programming expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cognitive models are fine-tuned by software developers and cognitive technology experts, then model performance is optimized, but user accessibility is limited to those with specialized education and knowledge
Solution Approach 1:
The patent introduces a conversational agent as an intermediary between non-expert users and the cognitive model optimization process. The agent translates user-friendly natural language inputs into technical optimization actions, bridging the knowledge gap without requiring users to have specialized education in cognitive technologies or software development
Solution Approach 2:
The system enables non-expert users to independently optimize cognitive models through conversational interaction without requiring external expert intervention. The conversational agent guides users through diagnostics and optimization steps, allowing them to self-service the complex model tuning process that previously required specialized knowledge
2Ease of operation
If a conversational interface is implemented to guide non-expert users, then user accessibility improves, but system complexity increases due to natural language understanding requirements
Solution Approach 1:
The conversational agent serves as an intermediary layer that handles the complexity of natural language understanding and translation into technical operations. This mediator absorbs the system complexity while presenting a simple interface to users, isolating the complexity from the user side of the system
Solution Approach 2:
The patent replaces traditional mechanical or manual interfaces (such as configuration files, command-line interfaces, or graphical parameter settings) with a natural language conversational interface. This substitution allows users to interact using everyday language rather than requiring them to navigate complex technical interfaces
Data Source
AI summary
Systems and methods to generate a cognitive model are described. A particular example of a system includes a memory including program code having an application programming interface and a user interface, and a processor configured to access the memory and to execute the program code to generate a cognitive model, to run analysis on the cognitive model to determine a factor that is impacting a performance of the cognitive model, to determine an action based on the factor, to report at least one of the factor and the action to a user, and to use the action to generate a second cognitive model.