AI User Assistance for Semantic Feature Discovery on Platforms
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Solution Overview
Problem
Existing computer-implemented platforms present users with unclear and time-consuming interfaces, particularly for novice users, leading to inefficiency in accessing platform functionalities.
Innovation Solution
A computer-implemented method that utilizes databases to determine relatedness scores between user inputs and static or dynamic computer files, executing relevant files or providing insights based on these scores, and employing natural language processing to assist users effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional user interfaces are used, then platform functionality is available, but user efficiency and ease of operation deteriorate due to unclear and time-consuming navigation
Solution Approach 1:
An AI assistant is introduced as an intermediary between the user and the complex platform interface. The assistant receives natural language queries from users, processes them through embedding models to understand intent, and returns simplified responses or direct actions, eliminating the need for users to navigate complex menus and interfaces manually.
Solution Approach 2:
The patent replaces traditional mechanical navigation (clicking through menus, searching interfaces) with an intelligent information retrieval system. Vector embeddings and similarity scoring algorithms substitute for manual interface exploration, automatically matching user intent with relevant platform features and information.
2Adaptability or versatility
If comprehensive platform features are provided, then functionality is enhanced, but interface complexity increases making it difficult for novice users to discover features
Solution Approach 1:
The AI assistant serves as an intermediary that handles the complexity of comprehensive platform features. Users can query about any feature in natural language without needing to understand the platform's complex structure, as the assistant translates queries into appropriate platform actions or information retrieval.
Solution Approach 2:
The system implements self-service through automated feature discovery and recommendation. The embedding-based retrieval system automatically identifies and presents relevant features based on user context and query, eliminating the need for users to manually explore or discover available functionalities.
3Loss of information
If detailed documentation is provided for all features, then information completeness is improved, but information retrieval time increases for users seeking specific help
Solution Approach 1:
The patent replaces traditional keyword-based search with semantic search using vector embeddings. User queries are transformed into embedding vectors that capture meaning and intent, allowing the system to retrieve relevant documentation sections based on semantic similarity rather than exact keyword matches, significantly improving retrieval accuracy and speed.
Solution Approach 2:
The documentation system is segmented into multiple embedding models covering different feature categories. This allows the system to route queries to specialized documentation sections efficiently, retrieving only relevant information rather than searching through all documentation, thus reducing retrieval time while maintaining completeness.
Data Source
AI summary
Method and systems for assisting a user on a computer-implemented user platform. The method includes receiving a user input from a user and determining an input category of the user input. In response to the input category being a first input category, the method includes accessing a first database hosting static computer files associated with document embeddings, determining a relatedness score for each static computer file and returning an indication of at least one of the static computer files based on the relatedness scores to the user. In response to the input category being a second input category, the method includes accessing a second database hosting executable dynamic computer files associated with service embeddings mapping a function thereof, determining a relatedness score for each dynamic computer file, executing at least one dynamic computer file and returning an indication of an output of the execution to the user.