Neural Network Message Suggestion System
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Solution Overview
Problem
The process of entering messages, especially on mobile devices, is cumbersome and prone to errors due to the need for manual typing or speech recognition, leading to ambiguity and inefficiency in communication, particularly in contexts like customer support where automated processing can be hindered by incomplete or unclear messages.
Innovation Solution
A system that uses a neural network and prefix tree to process incomplete messages and suggest pre-defined messages from a designated message corpus, reducing the need for users to type fully formed messages by presenting relevant suggestions as they type, thereby improving clarity and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual typing or speech recognition is used to enter messages, then message composition is possible, but the process becomes cumbersome and error-prone
Solution Approach 1:
The system pre-processes and stores a corpus of designated messages that are likely to be needed. When a user starts typing, the system has already prepared relevant suggestions based on the initial input, allowing the user to select from pre-computed options rather than composing from scratch or relying on error-prone speech recognition.
Solution Approach 2:
The system introduces an intermediary layer between the user's partial input and the final message selection. Instead of direct typing or speech-to-text conversion, the system uses a suggestion mechanism that processes the partial input and presents refined options, acting as a mediator that improves both ease of operation and reliability.
2Productivity
If users type complete messages manually, then message clarity is maintained, but time and effort are significantly increased
Solution Approach 1:
The system allows users to perform partial action by typing only a few initial characters or words rather than complete messages. The suggestion system generates complete message options based on this partial input, enabling users to achieve full message composition with minimal typing effort, thus dramatically reducing composition time while maintaining productivity.
Solution Approach 2:
The system pre-computes and stores a comprehensive corpus of designated messages. When users provide partial input, the system quickly retrieves and presents relevant pre-prepared suggestions, eliminating the need for users to spend time composing messages from scratch and significantly reducing messaging time.
3Productivity
If suggested messages are provided to users, then composition time is reduced, but the system complexity increases due to neural network processing
Solution Approach 1:
The system performs complex neural network processing in advance by pre-processing and storing a corpus of designated messages. During actual message composition, the system only needs to compare user input against this pre-processed corpus and present matching suggestions, significantly reducing the computational complexity required during runtime while maintaining high messaging efficiency.
Solution Approach 2:
The suggestion system serves itself by using the pre-processed corpus to automatically generate suggestions without requiring complex real-time processing. The system compares user input against the pre-computed designated messages and presents matches, allowing the system to maintain simplicity during operation while achieving high productivity.
4Ease of operation
If incomplete messages are submitted, then typing effort is reduced, but automated processing accuracy decreases due to ambiguity
Solution Approach 1:
The system introduces an intermediary suggestion layer between the user's incomplete input and automated processing. Instead of directly processing ambiguous incomplete messages, the system presents refined complete message options that the user can select, ensuring that the final processed message is clear and unambiguous while the user still only needs to type partially.
Solution Approach 2:
The system pre-processes a comprehensive corpus of designated messages to create a reference database of complete, unambiguous messages. When users submit incomplete input, the system matches this against the pre-processed corpus to suggest complete messages, ensuring that automated processing receives clear, accurate input while users maintain ease of operation.
Data Source
AI summary
Text of an incomplete message entered by a user may be processed using a neural network to suggest messages similar to the message the user is in the process of entering. Word embeddings may be obtained for the words of the text that represent the words in a first vector space. The word embeddings may then be processed by the neural network to compute an input message feature vector that represents the incomplete message in a second vector space. The input message feature vector may be used to select a first designated message as a suggestion from a set of designated messages, and the first designated message may be selected using a similarity score computed from the input message feature vector and a first designated message feature vector corresponding to the first designated message. The first designated message may then be presented as a suggestion to the user.