Privacy-Preserving Text Snippet Generation via Hash Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In domains like business and technical writing, repetitive writing tasks are physically and mentally taxing due to the need for manual repetition of similar clauses or phrases, and existing solutions like pre-populating snippet libraries are inadequate as they require extensive manual effort and expertise, failing to scale effectively.
Innovation Solution
A computer-implemented text suggestion process that automatically detects frequently used text units by executing a hash algorithm and similarity checks, suggesting the creation of text snippets, thereby reducing user involvement and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual creation of snippet libraries is used, then snippets can be customized for specific industries or business functions, but extensive manual effort and expertise are required, and it does not scale effectively
Solution Approach 1:
The system automatically analyzes user writing patterns and autonomously generates snippet recommendations without requiring manual curation. The algorithm detects frequently used text sequences and automatically creates snippet candidates, allowing the system to serve itself rather than relying on manual input from users or administrators.
Solution Approach 2:
The manual mechanical process of creating and curating snippet libraries is replaced with an automated computational system. The algorithm processes writing data, identifies patterns, and generates snippet recommendations through computational analysis rather than human effort, enabling scalable deployment across multiple users and organizations.
2Ease of operation
If pre-populated generic snippet libraries are used, then the cold start problem is partially addressed, but the snippets sound canned and require significant manual effort to create useful libraries
Solution Approach 1:
The system performs preliminary analysis of user writing patterns automatically as users begin typing, without requiring upfront manual configuration. By the time users need snippets, the system has already analyzed their writing style and prepared personalized recommendations, eliminating the need for manual library setup while providing customized rather than generic snippets.
Solution Approach 2:
The snippet library is dynamic and adapts to each user's writing patterns rather than being static and generic. The system continuously learns from user behavior and adjusts snippet recommendations in real-time, transforming the snippet library from a fixed manual creation task into a dynamic, self-adapting system that improves automatically with use.
3Adaptability or versatility
If actual user text is stored for snippet creation, then personalized snippet recommendations can be generated, but user privacy is compromised
Solution Approach 1:
The system extracts only the essential pattern information needed for snippet generation while leaving out the actual user text content. By analyzing writing patterns without storing the underlying text, the system separates the useful information (patterns and structures) from the sensitive information (actual user content), enabling personalization while preserving privacy.
Solution Approach 2:
The system uses an intermediary processing layer that analyzes writing patterns without directly storing or exposing user text. This intermediary mechanism processes the data in a privacy-preserving manner, allowing the system to generate personalized recommendations while acting as a buffer that protects user privacy from direct exposure or misuse.
Data Source
AI summary
In one embodiment, a method receives, by a computing device, a text change in a text entry which includes one or more text units. The method may access, using a data store, a record associated with a user identifier which includes one or more top N similar hash values associated with the user identifier. The method may determine one or more hash values by applying a hash function over the one or more text units of the text change. The method may compare each of the one or more hash values to the one or more top N similar hash values. In response to determining a match between at least one of the one or more hash values and the one or more top N similar hash values, the method may determine a phrase suggestion using the text change to visually present on the computing device.