Quantum Random Number Generator for Chatbot Response Selection

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Solution Overview

Problem

Current chatbots fall short in providing a user experience akin to conversing with a human due to revealing underlying algorithms, leading to an impression that interactions are with machines rather than humans.

Innovation Solution

Employing a quantum random number generator to select responses among hypotheses based on confidence values, leveraging true randomness to enhance natural language processing and emulate human-like dialogue behavior.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If conventional stochastic techniques and deep neural networks are used in chatbots, then the chatbot can process natural language and generate responses, but the responses reveal algorithmic patterns and repetitive behavior that make the interaction feel mechanical rather than human-like

Engineering Contradiction:
Improvehuman-like interaction qualityVSAvoidalgorithmic artifacts and repetitive patterns
Core Design Contradiction:
ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The patent replaces conventional pseudo-random number generators (mechanical/computational systems) with quantum random number generators (quantum physical systems) to introduce true randomness into the chatbot's response selection process. This substitution eliminates predictable patterns while preserving the ability to generate diverse, human-like responses from the same input

Inventive Principle:
Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent changes the fundamental parameter of randomness from pseudo-random (deterministic but unpredictable) to truly random (fundamentally unpredictable via quantum processes). This parameter change in the random number generation mechanism transforms the response selection process, making it impossible to predict which response will be chosen even when confidence values are known

Inventive Principle:
Principle #35Parameter changes

2Reliability

If deterministic algorithms are used to select responses based on confidence values, then the chatbot provides consistent and reliable responses, but the responses become predictable and lack the variability characteristic of human dialogue

Engineering Contradiction:
Improveresponse consistencyVSAvoidresponse variability and unpredictability
Core Design Contradiction:
ReliabilityVSAdaptability or versatility

Solution Approach 1:

The patent introduces dynamic randomness into the previously static deterministic selection process. By using quantum random number generators, the system dynamically adjusts response selection based on truly random values while still respecting confidence value rankings, creating a hybrid approach that maintains consistency bounds while introducing necessary variability

Inventive Principle:
Principle #15Dynamics

Solution Approach 2:

The quantum random number generator acts as an intermediary between the deterministic confidence value ranking and the final response selection. It mediates the selection process by introducing quantum randomness that influences which response is chosen from the ranked list, balancing determinism and randomness

Inventive Principle:
Principle #24Intermediary (Mediator)

Applied Scientific Principles

This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.

Function Achieved in This Case

The approach significantly improves chatbot responses by avoiding repetitive patterns and incorporating unpredictable behavior, providing a more human-like interaction experience.

Implementation Method 1

Quantum objects are known to constitute the only carriers of true randomness, and quantum random number generators may demonstrate truly unpredictable behavior

Methodology Applied
Scientific EffectQuantum randomness:

Data Source

PatentEP4261735A1Natural language processing by means of a quantum random number generator
Publication Date: 2023.10.18 TERRA QUANTUM AG
  • EP4261735A1 patent drawingFigure 1
  • EP4261735A1 patent drawingFigure 2
  • EP4261735A1 patent drawingFigure 3

AI summary

A method for natural language processing comprises: receiving a sample comprising natural language; processing the sample, wherein processing the sample comprises generating a plurality of response hypotheses and generating a plurality of confidence values, wherein each response hypothesis is associated with the corresponding confidence value; and selecting a response, comprising selecting the response randomly among the plurality of response hypotheses based at least in part on the corresponding confidence value by means of a quantum random number generator.