Personal Assistant for Automated Interaction Routines
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Solution Overview
Problem
Existing interaction systems, such as those for checking airline flight status, require significant user effort due to varying mechanisms and systems, making routine transactions time-consuming and inefficient, especially for users unfamiliar with specific systems.
Innovation Solution
The implementation of a device and method that uses machine learning and natural language understanding to observe and detect patterns in human interactions, learning and storing interaction routines to minimize user involvement by providing automated responses to prompts in subsequent instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated systems are used to handle routine interactions, then user involvement is reduced, but the system complexity increases due to varying mechanisms and systems across different services
Solution Approach 1:
The patent creates a universal interaction assistant that can handle multiple types of interactions across different services (banking, flight status, retail purchases) through a single system. The assistant learns and adapts to various interaction patterns, making one system perform multiple functions rather than requiring separate automated systems for each service.
Solution Approach 2:
The interaction assistant automatically learns interaction routines by observing user behaviors and independently generates appropriate responses without requiring explicit programming for each scenario. The system serves itself by continuously improving its own capabilities through machine learning from observed interactions.
2Loss of time
If manual interaction handling is used, then system complexity remains low, but time consumption increases for routine transactions
Solution Approach 1:
The system performs preliminary learning of interaction routines by observing user behaviors in advance. This pre-learning phase allows the assistant to have interaction templates ready before actual transactions occur, enabling rapid automated responses during routine transactions without requiring complex real-time decision-making.
Solution Approach 2:
The interaction assistant continuously learns from observed user interactions and feedback, refining its response generation capabilities over time. This feedback mechanism allows the system to improve its performance automatically, reducing time consumption for routine transactions while managing complexity through adaptive learning rather than rigid programming.
3Ease of operation
If system-specific procedures are used, then interaction accuracy is high for familiar users, but ease of operation decreases for users unfamiliar with specific systems
Solution Approach 1:
The interaction assistant serves as an intermediary between the user and the various service systems. It translates user intents into system-specific commands and handles system-specific procedures transparently, making interactions easier for users while maintaining accuracy through the assistant's learned understanding of both user behaviors and system requirements.
Solution Approach 2:
The system performs preliminary learning of correct interaction procedures through observation of successful user interactions. By pre-learning accurate response patterns for different services, the assistant ensures interaction accuracy is maintained while presenting a simplified, consistent interface to users regardless of their familiarity with specific systems.
Data Source
AI summary
In one example, the present disclosure describes a device, computer-readable medium, and method for automatically learning and facilitating interaction routines involving at least one human participant. In one example, a method includes learning an interaction routine conducted between a human user and a second party, wherein the interaction routine comprises a series of prompts and responses designed to identify and deliver desired information, storing a template of the interaction routine based on the learning, wherein the template includes at least a portion of the series of prompts and responses, detecting, in the course of a new instance of the interaction routine, at least one prompt from the second party that requests a response from the human user, and using the template to provide a response to the prompt so that involvement of the human user in the new instance of the interaction routine is minimized.