Agent Object Serialization for ML Context Portability
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Solution Overview
Problem
Machine learning models are stateless, limiting the portability and utility of user interactions, as context is confined to individual user sessions, making it difficult to transfer or share conversational agent sessions between users or across contexts.
Innovation Solution
The concept of machine learning instancing, where a conversational agent is encapsulated as an agent object that includes a persona definition and an object embedding memory, allowing for portability and versioning, enabling sharing and subsequent processing across users and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a machine learning model is stateless and processes input without regard for previous interactions, then the model can process input quickly and independently, but the context knowledge available to the model is limited to only the current input, reducing utility for users
Solution Approach 1:
The patent creates a copy of the machine learning model's state and interactions by serializing the model's internal state, conversation history, and context into an agent object that can be stored, shared, and restored. This allows the model to maintain context knowledge across sessions while preserving the ability to process input efficiently.
Solution Approach 2:
The patent segments the machine learning model's operational state into distinct components: the model itself, the conversation history, the context knowledge, and the agent object representation. This segmentation allows each component to be processed independently while maintaining overall system functionality and context awareness.
2Device complexity
If machine learning model interactions are confined to individual user sessions, then the system complexity remains low, but the portability and transferability of interactions between users and contexts is lost
Solution Approach 1:
The patent creates a portable copy of the entire agent state including conversation history, context, and model parameters through the agent object serialization mechanism. This copied agent object can be transferred between users and contexts while maintaining all relevant state information, enabling portability without requiring complex system architecture.
Solution Approach 2:
The patent performs preliminary serialization and packaging of the agent state into a self-contained agent object before the interaction ends. This preliminary action prepares the agent for future portability and restoration, allowing the system to maintain low complexity during operation while enabling high portability when needed.
3Productivity
If context knowledge is limited to current input, then the model processing remains simple and fast, but the utility and flexibility for subsequent processing and versioning is reduced
Solution Approach 1:
The patent segments the agent state into versionable components that can be tracked and managed separately. The agent object includes metadata about versions, allowing the system to maintain processing efficiency while enabling versioning and comparison of different agent states without requiring complex version management infrastructure.
Data Source
AI summary
Aspects of the present disclosure relate to machine learning instancing, where an instance of an agent (e.g., including processing of user input by a machine learning model to generate model output) is encapsulated as an agent object. In examples, an agent object is stored as a file, as a document, and/or in a database, among other examples. An agent object includes a persona definition and/or an object embedding memory, thereby defining various aspects of the agent. Thus, an agent object permits portability the agent, for example between users, across contexts, and/or for a variety of subsequent processing, among other examples.