Information Content Score for Natural Language Data
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Solution Overview
Problem
Current speech recognition and natural language processing systems face challenges in determining the information content of user inputs, which can lead to potential privacy issues and data security risks, as they struggle to differentiate between generic and unique inputs effectively.
Innovation Solution
The system employs a method to calculate an information content score for natural language inputs by using a trie data structure and probability calculations, splitting inputs into high-frequency and low-frequency segments, and normalizing scores to determine the uniqueness and sensitivity of the input, thereby controlling routing and processing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech recognition and natural language processing systems process all user inputs, then comprehensive service coverage is achieved, but privacy issues and data security risks increase due to inability to differentiate between generic and unique inputs
Solution Approach 1:
The patent segments natural language inputs into high-frequency generic segments and low-frequency unique segments using a trie data structure. By dividing the input into these segments, the system can process generic parts normally while identifying and protecting unique parts, thus maintaining service coverage while reducing privacy risks.
Solution Approach 2:
The patent introduces an information content score as an intermediary metric to evaluate the uniqueness of input segments. This score acts as a mediator between the raw input data and the routing decision, enabling the system to objectively determine whether an input should be processed normally or protected without direct human intervention.
2Measurement precision
If the system calculates information content scores for all inputs using probability calculations, then identification accuracy of sensitive data improves, but computational complexity and processing time increase
Solution Approach 1:
The patent divides the input into segments based on frequency in the trie structure, allowing probability calculations to be performed only on relevant segments rather than the entire input. This segmentation reduces the computational burden while maintaining identification accuracy for sensitive portions.
Solution Approach 2:
The patent pre-builds the trie data structure during an offline phase, organizing common phrases and segments beforehand. This preliminary action eliminates the need for complex real-time analysis of entire inputs, reducing online computational complexity while preserving accurate identification capability.
3Object-affected harmful factors
If the system prevents all potentially sensitive inputs from being sent to external devices, then user privacy is enhanced, but loss of useful information and reduced processing efficiency occur
Solution Approach 1:
The patent applies different quality levels of protection to different segments of the input based on their information content scores. Generic high-frequency segments are processed normally without restriction, while unique low-frequency segments that exceed the threshold receive enhanced protection. This localized approach protects privacy where needed while preserving useful information flow.
Solution Approach 2:
The patent uses the information content score threshold as a adjustable parameter to control the balance between privacy protection and information flow. By changing this parameter, the system can adapt to different security requirements without losing useful information, allowing flexible optimization based on specific use cases.
4Measurement precision
If the system uses a trie data structure to segment inputs into high-frequency and low-frequency segments, then identification precision of unique inputs improves, but device complexity increases
Solution Approach 1:
The trie data structure is built offline in advance, organizing common phrases and segments before runtime. This preliminary action transfers complexity from the online processing phase to the offline preparation phase, maintaining high identification precision during actual operation while minimizing real-time computational overhead.
Data Source
AI summary
Devices and techniques are generally described for determining an information content score for a natural language input. In various examples, data representing a natural language input may be received. A first probability of the natural language input may be determined by comparing words of the natural language input to a first data structure representing a plurality of natural language phrases. A score for the natural language input may be determined based at least in part on the first probability. In some examples, the score may represent an amount of information content included in the natural language input. In various examples, the natural language input may be sent to a remote computing device based at least in part on the score.