Assistant System Detecting Entity Information in Messaging
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Solution Overview
Problem
Users face challenges in efficiently retrieving and remembering information shared during messaging conversations, such as meeting details or contact information, as they often need to sift through previous messages, which is time-consuming and inconvenient.
Innovation Solution
An assistant system integrated with messaging applications that uses natural-language understanding to identify and store entity information from conversations, allowing users to save and retrieve this information proactively, with features like setting reminders and providing suggestions to store important details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search through messaging conversation history to retrieve information, then they can find the information, but it is time-consuming and inconvenient
Solution Approach 1:
The assistant system performs preliminary actions by automatically detecting, extracting, and storing entity information from messaging conversations as they occur. The system proactively saves important information like contact details, meeting times, and addresses to a database without waiting for users to manually search for them later, thus resolving the time loss and convenience issue.
2Productivity
If the assistant system automatically stores all entity information from conversations, then information retrieval becomes efficient, but the system complexity increases
Solution Approach 1:
The assistant system acts as an intermediary between the messaging application and the database. It uses natural language understanding to analyze conversation content, identifies relevant entity information, and stores it in a structured format. This intermediary layer automates the information management process, improving retrieval efficiency while managing system complexity through modular design.
3Ease of operation
If the system proactively provides suggestions to store information, then user convenience is enhanced, but the processing overhead increases
Solution Approach 1:
The assistant system provides self-service by automatically analyzing conversation content, identifying important entity information, and proactively suggesting storage options to users. The system serves itself by managing information flow and presenting relevant storage suggestions without requiring users to manually initiate the process, thereby enhancing convenience while optimizing processing overhead through intelligent filtering.
Data Source
AI summary
In one embodiment, a method includes receiving, from a first client system associated with a first user, a message sent from the first user to a second user, analyzing the message from the first user to identify one or more intents and one or more slots of the received message, computing a confidence score for the intent to offer entity information based on user behavior history records associated with the second user, sending, to a second client system associated with the second user, if the confidence score exceeds a threshold score, instructions for presenting a suggestion to the second user to store the values for entity information in association with a profile record for the entity, and receiving, from the second client system associated with the second user, an indication from the second user confirming the values for entity information should be stored with the profile record for the entity.