LLM-Primed Calendar Querying for Faster Availability Replies
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face delays and resource consumption when checking calendar information to respond to queries about availability, leading to inefficient use of client device resources.
Innovation Solution
Utilizing a large language model (LLM) to generate responses to calendar-related queries after priming it with structured or unstructured calendar data, allowing for instant replies without requiring user device resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user checks calendar to confirm availability, then accurate availability information is obtained, but response time is delayed and client device resources are consumed
Solution Approach 1:
The system performs preliminary actions by automatically retrieving and analyzing calendar data before the user needs to respond. The automated assistant accesses the user's calendar, determines availability for the requested time period, and prepares the response information in advance, eliminating the need for the user to manually check their calendar when queried about availability.
Solution Approach 2:
The system enables self-service by having the automated assistant independently handle calendar checking and response formulation. The assistant autonomously accesses calendar data, processes the availability information, and generates appropriate responses without requiring user intervention or manual calendar review, thereby saving both time and device resources.
2Loss of information
If user searches through calendar and messages for availability information, then complete information is gathered, but processor resources and battery resources are consumed
Solution Approach 1:
The automated assistant independently performs the information gathering task by accessing calendar data and analyzing availability without requiring the user's device to execute search operations. This transfers the computational burden from the user's mobile device to the assistant's processing system, preserving the user's battery and processor resources while still gathering complete availability information.
Solution Approach 2:
The automated assistant acts as an intermediary between the user and their calendar data. Instead of the user's device directly querying and processing calendar information, the assistant serves as a mediator that retrieves, analyzes, and presents availability information, thereby reducing the computational load on the user's client device while ensuring accurate information is provided.
3Reliability
If receiving user checks calendar to formulate reply, then accurate response is provided, but receiving user's client device resources are utilized
Solution Approach 1:
The receiving user's automated assistant independently handles the entire process of checking the calendar and formulating an accurate response. The assistant autonomously accesses the calendar data, determines availability status, and generates appropriate reply messages without requiring the receiving user to manually check their calendar or use their device resources for processing this information.
Solution Approach 2:
The automated assistant serves as an intermediary that handles the resource-intensive tasks of calendar access and response formulation on behalf of the receiving user. This intermediary approach ensures accurate responses are generated while preventing the receiving user's client device from consuming excessive resources during the information gathering and response preparation process.
Data Source
AI summary
Some implementations process structured calendar data of an electronic calendar for a first user, to generate a natural language representation of the structured calendar data. Versions of those implementations further, in response to receiving a query determined to be relevant to the electronic calendar, prime a large language model (LLM) using a priming input (e.g., process the priming input using the LLM), where the priming input is based on the natural language representation of the structured calendar data. Following priming of the LLM using the priming input, some of those versions process, using the LLM, query input that is based on the query, to generate a LLM output and determine, based on the LLM output, a response to the query. The response can include a natural language response that can be rendered.