Human-Machine Interface Key Encoding for Error-Resistant Data Entry
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Solution Overview
Problem
Human users often encounter difficulties in accurately entering key data, such as IP addresses or product codes, due to lack of context and susceptibility to typographical errors, leading to potential system failures.
Innovation Solution
A method of presenting key data as resilient human language statements, using error-resistant encoding and decoding processes that account for common human errors, including misspellings and partial data loss, by generating and processing grammatically correct phrases that can correct for errors during input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If random character strings (e.g., IP addresses, product codes) are used as key data, then the data can be compact and machine-processable, but human users cannot easily remember or enter them correctly
Solution Approach 1:
The patent introduces an intermediary encoding system that converts machine-oriented random character strings into human-oriented mnemonic phrases. The system uses a dictionary mapping words to character sequences, allowing users to remember and enter data through meaningful phrases rather than random characters. This intermediary layer resolves the contradiction by making data both human-memorable and machine-processable.
Solution Approach 2:
The patent transforms the parameter representation of key data from raw character sequences to encoded phrases with semantic meaning. By changing the parameter form from arbitrary characters to meaningful words and phrases, the system improves human memorability and entry accuracy while maintaining the underlying data integrity through reversible encoding.
2Reliability
If users are required to enter exact key data without errors, then system accuracy is maintained, but any typographical error or memory mistake causes complete failure
Solution Approach 1:
The patent implements beforehand cushioning by incorporating error tolerance into the encoding scheme. The system anticipates potential human errors (typos, wrong words) and designs the decoding process to handle these variations gracefully. By preparing error-correction mechanisms in advance, the system maintains reliability even when users make mistakes, cushioning against complete failure.
Solution Approach 2:
The patent uses feedback mechanisms where the system evaluates entered phrases against the encoded target data. When errors are detected, the system can provide corrective feedback or attempt error correction by comparing the entered phrase with the expected encoded form, allowing the system to recover from common human errors while maintaining accuracy requirements.
3Ease of operation
If mnemonic phrases are used to help users remember key data, then memorability improves, but the encoding and decoding process becomes more complex
Solution Approach 1:
The patent applies segmentation by breaking down the encoding and decoding process into distinct modular components: a dictionary mapping words to character sequences, an encoding function that converts phrases to data, and a decoding function that reverses the process. This segmentation makes the complex system more manageable and implementable while maintaining the mnemonic benefits.
Data Source
AI summary
Techniques for presenting a key to a human across a human-machine interface include receiving key data that indicates a key to be input to a target process. The key is reversibly converted to a first human-language statement. The human language statement is designed so that multiple variations in the human-language statement due to common human errors can be correctly reversed to regenerate the key data. The common human-language statement is presented to a human user for use in the target process. After receiving input data that indicates a second human language statement based on human memory of the first human language statement, the second human language statement is corrected for common human language errors. The corrected second human language statement is then converted to second key data and sent to the target process. Thus multiple human inputs are accepted and correctly converted to the key for the target process.