Quantum Support Response Retrieval for Large Historical Datasets
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Solution Overview
Problem
Existing systems lack the requisite accuracy and efficiency to process and generate support request responses and recommendations at scale, particularly in handling large volumes of historical data and interactions.
Innovation Solution
A combined classical computing and quantum computing system utilizing generative machine-learning models to identify intents and named entities in support requests, determine relevant historical responses, and generate recommendations for service interactions based on historical learnings, leveraging quantum computing's parallel processing capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical computing systems are used to process historical support request data, then the system can operate with existing hardware, but the accuracy and efficiency of generating support responses deteriorates when handling large volumes of data
Solution Approach 1:
The patent combines classical computing systems with quantum computing systems to create a hybrid architecture. The classical system handles data preparation and quantum system performs the computationally intensive similarity search and response generation. This merging allows the system to achieve both high accuracy in processing historical data and improved efficiency through quantum parallel processing capabilities.
Solution Approach 2:
The patent replaces classical mechanical computing operations with quantum mechanical operations. Specifically, it uses quantum algorithms (such as Grover's algorithm) to search through historical support request data, replacing traditional linear search mechanisms with quantum parallel search that provides superior speed and accuracy for large datasets.
2Speed
If quantum computing is used to search historical support requests, then the speed of finding relevant responses improves, but the device complexity increases
Solution Approach 1:
The patent segments the computing system into distinct classical and quantum components. The classical system handles data preparation, storage, and interface management, while the quantum system dedicatedly handles the search and similarity computation. This segmentation allows each component to be optimized independently and simplifies the overall system architecture despite the inherent complexity of quantum computing.
Solution Approach 2:
The patent introduces an intermediary layer that bridges classical and quantum computing systems. This intermediary handles the translation between classical data formats and quantum states, manages the communication protocols, and coordinates the workflow between the two systems. This intermediary simplifies the integration complexity and allows the system to leverage quantum capabilities without requiring complete redesign of existing infrastructure.
3Reliability
If the system processes larger volumes of historical support requests, then the accuracy of response generation improves, but the time required for processing increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and indexing historical support request data before it is needed for actual response generation. The system pre-organizes the data in quantum states ready for rapid retrieval, and pre-computes certain similarity metrics. This preliminary preparation allows the system to quickly access and process large volumes of historical data without incurring proportional increases in processing time during actual use.
Data Source
AI summary
A system includes a memory configured to store a plurality of instances of a software application executable on a computing device, a set of historical generated responses, and a set of confidence scores. The system includes processors coupled to the memory and configured to receive a support request and execute generative machine-learning models. The generative machine-learning models are trained to identify an intent and named entities included within the support request, determine, based on the identified intent and named entities, whether the support request is associated with a historical generated response. In response, the generative machine-learning models are further trained to identify, based on the historical generated response and the confidence score, a support service interaction to be executed for satisfying the support request and to generate a response comprising a recommendation to initiate an execution of the identified support service interaction to satisfy the support request.