Digital Assistant Text Generation With Context-Aware Prompts
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Solution Overview
Problem
Existing techniques for generating and editing text using digital assistants and language models are cumbersome and inefficient, requiring multiple key presses and wasting user time and device energy, particularly in battery-operated devices.
Innovation Solution
Implementing methods and interfaces that reduce the number and nature of user inputs by determining the type of additional information needed for text generation, using placeholders, and leveraging remote language models to conserve processing power and memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing techniques are used for generating and editing text using digital assistants, then text generation functionality is provided, but the process becomes cumbersome and inefficient requiring multiple key presses
Solution Approach 1:
The system performs preliminary classification of information types and prepares appropriate handling strategies before actual text generation. By pre-categorizing what information can be retrieved from context versus what requires user input, the system avoids unnecessary processing steps and directly retrieves or generates appropriate content, reducing the number of user interactions required.
Solution Approach 2:
The system introduces an intermediary classification mechanism that sits between user input and text generation. This intermediary layer automatically determines whether additional information is needed and handles retrieval or placeholder insertion, mediating between the user's intent and the language model's requirements without requiring direct user intervention for each information gap.
2Productivity
If existing techniques are used for generating and editing text, then text generation is achieved, but device energy is wasted particularly in battery-operated devices
Solution Approach 1:
The system segments the text generation process into distinct phases: context analysis, information type classification, and selective execution (retrieval vs. placeholder insertion). By dividing the workflow this way, the system avoids unnecessary processing of all information types through the same path, reducing overall computational load and energy consumption while maintaining productivity.
Solution Approach 2:
The system changes the parameter of information handling by categorizing information into types that determine different processing paths. Instead of uniformly processing all additional information through resource-intensive retrieval, the system adjusts processing parameters based on information type, retrieving only when necessary and using placeholders otherwise, thereby reducing energy consumption while maintaining text generation efficiency.
3Reliability
If the system requests additional information for text generation, then text accuracy is improved, but user cognitive burden increases
Solution Approach 1:
The system applies local quality by treating different types of additional information differently based on their characteristics. Rather than uniformly requesting all missing information from users, the system locally adapts its information handling strategy: retrieving information that can be automatically obtained and using placeholders for information requiring user input, thereby maintaining text accuracy while reducing cognitive burden.
Solution Approach 2:
The system performs partial action by selectively retrieving only the specific additional information needed for accurate text generation rather than all possible information. By using placeholders for information that cannot be automatically retrieved and only retrieving essential contextual information, the system achieves sufficient accuracy without overwhelming the user with excessive information requests.
4Quantity of substance
If remote language models are used for text generation, then processing power and memory usage are reduced, but system complexity increases
Solution Approach 1:
The system extracts the heavy language model processing from the local device and places it on remote servers. By taking out the computationally intensive language model generation task and replacing it with a lightweight local interface that handles classification and coordination, the system dramatically reduces local processing power and memory requirements while managing the complexity of the distributed architecture through clear separation of concerns.
Data Source
AI summary
The present disclosure generally relates to generating and/or modifying text using a digital assistant. This disclosure encompasses a system for requesting information before generating text using models based on the type of information needed. This disclosure further encompasses a system for a remote model requesting additional information before generating text. This disclosure encompasses user interfaces for text generation/modification. This disclosure encompasses a system for proofreading text using a model. This disclosure encompasses a system for generating a response to a received communication with operations for updating the generated response.