Two-Way Chat UI Communication with Contextual API Responses
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Solution Overview
Problem
Existing chat applications require users to communicate using plain-text and express their intent in lengthy prose, leading to inefficient processing and user experience.
Innovation Solution
A system that enhances chat interaction by allowing users to select elements on a web application's interface, capturing context data, and sending queries to a language processor for relevant APIs, presenting cohesive answers and data objects directly on the interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users communicate using plain-text in chat applications, then users can express their intent freely, but the communication becomes inefficient and requires lengthy prose
Solution Approach 1:
The patent segments the communication process into distinct components: UI element selection (structured input) and natural language query (intent expression). This segmentation allows users to efficiently specify context through element selection while maintaining freedom in expressing intent through natural language, resolving the contradiction between ease of expression and time efficiency.
Solution Approach 2:
The system performs preliminary action by automatically capturing and providing context data from selected UI elements before the user formulates their query. This preliminary provision of structured context eliminates the need for users to write lengthy prose describing the context, thereby reducing time while maintaining ease of intent expression.
2Reliability
If chat applications process lengthy prose using NLU and NLP techniques, then the system can understand user intent, but the processing resources are consumed inefficiently
Solution Approach 1:
The patent segments the information processing into two parts: structured data from UI element selection (which requires minimal NLP processing) and the user's natural language query (which requires NLU/NLP processing). This segmentation reduces the amount of text that needs intensive processing while maintaining accurate intent understanding, thereby reducing processing resource consumption.
Solution Approach 2:
The selected UI element acts as an intermediary that bridges the user's intent and the system's understanding. Instead of processing lengthy prose to extract context, the system uses the structured data from the selected element as an intermediary representation, significantly reducing NLU/NLP processing requirements while maintaining reliability.
3Productivity
If chat applications use pre-defined conversation flows and algorithms, then the system can formulate responses efficiently, but the user experience lacks personalization and context awareness
Solution Approach 1:
The system performs preliminary action by capturing and providing context data from selected UI elements before formulating responses. This pre-provision of structured context allows the response formulation process to be both efficient (using pre-defined flows) and adaptive (with accurate context awareness), resolving the contradiction between productivity and versatility.
Data Source
AI summary
A computerized method enhances chat interaction through 2-way user interface (UI) communication. Data associated with an element, selected by a user from a UI of a web application, is received in a chat box. A query corresponding to the selected element is received in the chat box. A request is sent to a language processor for a list of application programming interfaces (APIs) relevant to the query and the data. For each API, the query and the data are sent to the API, and an answer including a data text and a data object is received from the API. A request including the data texts for a cohesive answer is sent to the language processor that returns the cohesive answer. If the web application can render the data object, the data object and the cohesive answer are presented on the UI of the web application.