Intelligent Assistant Content Retrieval for Contextual Chat Personalization
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Solution Overview
Problem
Conventional content provided by third-party systems is static and interactive conversations with intelligent assistants are linear and scripted, failing to provide personalized and contextually relevant information to users.
Innovation Solution
A computing system that integrates with applications presenting content associated with a third party system, a method is disclosed, where the computing system integrates with applications, a method is disclosed, where the computing system generates a profile for the user based on the user's activity data, the computing system generates a profile for the user based on the user's activity data, and the computing system determines that the message includes at least one key word triggering retrieval of relevant content, and the computing system causes a client device associated with the user to display the content alongside the chat interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static content is provided by third party systems, then information presentation is simple, but user engagement and information relevance deteriorate
Solution Approach 1:
An intelligent assistant acts as an intermediary between the user and the third-party content system. The assistant receives user activity data, generates personalized profiles, and retrieves relevant content dynamically, thereby enabling content personalization without requiring the third-party system itself to become complex. The assistant mediates the interaction by injecting code into the application to capture activity data and by querying content based on generated profiles.
Solution Approach 2:
The system segments the content delivery function into separate components: (1) activity data collection through injected code, (2) profile generation by the intelligent assistant, (3) content retrieval based on profile tags, and (4) content display. This segmentation allows each component to remain relatively simple while the overall system achieves high adaptability through the coordinated workflow.
2Adaptability or versatility
If linear and scripted conversations are used in chat interfaces, then conversation structure is simple, but contextual relevance and user engagement deteriorate
Solution Approach 1:
The chat interface transitions from a static, scripted conversation model to a dynamic, adaptive model. The intelligent assistant continuously updates user profiles based on real-time activity data and conversation context, allowing the conversation flow and content recommendations to adapt dynamically to user needs and preferences rather than following a predetermined script.
Solution Approach 2:
The system implements feedback loops where user activity data captured during chat interactions continuously updates the user profile, which in turn influences subsequent content retrieval and conversation direction. This feedback mechanism enables the chat system to adapt to user responses and maintain contextual relevance without requiring complex pre-programmed conversation trees.
3Measurement precision
If user activity data is collected and profiles are generated in real-time, then content relevance is improved, but data processing time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by continuously collecting and preprocessing user activity data in the background through injected code, building up user profiles incrementally before they are needed for content retrieval. This allows the profile generation to be spread out over time rather than computed entirely at the moment of content request, reducing immediate processing time while maintaining accuracy.
Data Source
AI summary
A computing system receives user activity data from a user interacting with content associated with a third party computing system. The computing system generates a profile for the user based on the user activity data. The profile includes tags indicating attributes about the user. The computing system establishes a communication session between the user and the intelligent assistant via a chat interface. The computing system receives a message from the user to the intelligent assistant. The computing system determines that the message includes at least one key word triggering retrieval of relevant content. Based on the determining, the computing system retrieves content that is associated with the key word and relevant to the user based on the tags associated with the user profile. The computing system causes a client device associated with the user to display the content alongside the chat interface.