Context Vector Assistant Suggestion in Communication GUI
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Solution Overview
Problem
Users face difficulties in organizing and quickly locating appropriate automated assistants in graphical user interfaces for communication sessions, as the number of available assistants grows, leading to distractions during critical multitasking situations.
Innovation Solution
A system that generates vectors describing current and past communication session contexts to suggest or insert automated assistants based on similarity thresholds, allowing for contextual matching and dynamic updating of assistant presence in the interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for automated assistants in a graphical user interface, then they can locate specific assistants, but the process requires additional steps that distract user focus during critical multitasking situations
Solution Approach 1:
The system pre-generates similarity vectors for automated assistants based on their functionality, metadata, and performance characteristics before users need to search. When a communication session occurs, the system immediately compares session characteristics against these pre-computed vectors and presents relevant assistants without requiring user search actions, thus eliminating the time loss while maintaining accurate matching.
Solution Approach 2:
The patent introduces a similarity vector computation mechanism as an intermediary between the automated assistants and the user interface. This intermediary automatically matches session characteristics with assistant capabilities through vector comparison, eliminating the need for users to manually search or navigate through assistant lists, thereby reducing both time loss and user distraction.
2Ease of operation
If the system provides a searchable alphabetical list or categorized list of automated assistants, then users can organize and locate assistants, but users still need to know what to search for or under which category a particular assistant would fall
Solution Approach 1:
The system performs self-service by automatically computing similarity vectors that capture the essential characteristics and functionality of each automated assistant. Instead of requiring users to know search terms or categories, the system autonomously matches session characteristics with assistant capabilities through vector comparison, presenting relevant assistants without user input beyond the session context.
Solution Approach 2:
The patent transforms the traditional search interface paradigm by changing from discrete categorical parameters (alphabetical lists, categories) to continuous similarity vectors. This parameter transformation enables semantic matching based on functional characteristics rather than requiring users to know specific search terms or categorization schemes, thereby improving ease of operation without increasing information requirements.
3Adaptability or versatility
If users accumulate more automated assistants for use in the call connection graph, then the system becomes more sophisticated and capable, but users encounter difficulty in organizing automated assistants, remembering their names, specific functionality, and how to quickly locate the appropriate automated assistant
Solution Approach 1:
The patent resolves the complexity issue by transitioning from one-dimensional organization (alphabetical lists, categorical folders) to multi-dimensional similarity vectors that capture multiple characteristics of automated assistants simultaneously. This dimensional transformation enables the system to handle a large number of diverse assistants without increasing user-facing complexity, as the vector-based matching automatically considers multiple attributes (functionality, metadata, performance) in parallel.
Data Source
AI summary
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for suggesting and inserting automated assistants in a graphical user interface for managing communication sessions. A system for suggesting an automated assistant generates a first vector describing a current context of a current communication session, and generates a comparison of the first vector and a second vector associated with a past context of an automated assistant in a past communication session. Then, if the comparison exceeds a similarity threshold, the system suggests the automated assistant to at least one user in the current communication session. Optionally, the system can predictively insert the automated assistant in a communication session if the comparison exceeds a similarity threshold. The graphical user interface for managing communication sessions displays automated assistants in a same manner as human participants.