Targeted Natural Language Response Generation for Messaging
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Solution Overview
Problem
Users face frustration when responding to messages due to the time-consuming nature of entering responses, especially in situations like riding in a bouncing car or on public transportation, where it is difficult to do so effectively with existing messaging applications.
Innovation Solution
A computing device generates targeted natural language responses based on the association of content in an electronic communication with the user, allowing for user selection and sending of appropriate responses, thereby simplifying the response process and improving user interface operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users manually enter their desired response in messaging applications, then the response can be personalized and appropriate, but it is time-consuming and difficult especially in situations like riding in a bouncing car or on public transportation
Solution Approach 1:
The system automatically generates multiple candidate responses without requiring manual input from the user. The computing device analyzes the received electronic communication and autonomously creates personalized response options tailored to the user's association with the content, allowing users to simply select from pre-generated choices rather than typing responses themselves.
Solution Approach 2:
The system performs the time-consuming response generation action before the user needs to respond. By pre-generating multiple candidate responses based on the incoming communication and the user's associations, the system eliminates the need for users to spend time creating responses manually when they are in situations where manual input is difficult.
2Productivity
If generic natural language responses are generated for all recipients, then the response generation process is simple and fast, but the responses may not be appropriate or relevant to each specific user's association with the content
Solution Approach 1:
The system generates responses with local quality by tailoring each candidate response to the specific user's association with the content. Instead of using a single generic response for all recipients, the computing device analyzes individual user associations and generates customized response options for each user, making the responses locally adapted to their specific context and relationship with the content.
Solution Approach 2:
The system changes the parameters of response generation by incorporating user association data as an additional variable. The computing device adjusts the response generation process based on parameters such as the user's role, relationship to the sender, and contextual associations with the content, thereby producing personalized responses while maintaining efficient automated generation.
Data Source
AI summary
A device receives an electronic communication from another device, such as an email. The communication is addressed to multiple recipients and the device determines an association of content in the communication to a user of the device. This association of content to a user can be determined in various manners, such as by identifying one of multiple portions of the communication that is directed to the user rather than other recipients of the communication, determining whether the user is a primary recipient or a secondary recipient of the communication, and so forth. The device generates a set of natural language responses to the communication for the user based at least in part on this association. The device displays the set of natural language responses and receives user selection of one of the natural language responses, then sends the selected natural language response to at least the other device.