Conversational Data-to-Text System for Multi-Turn Query Processing
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Solution Overview
Problem
Conventional query systems require specific syntax and are limited to single-turn interactions, failing to understand natural language queries and infer context from user history, leading to information loss and miscommunication.
Innovation Solution
A conversational data-to-text system that processes natural language queries, generates analytic operation instructions, and updates contextual data to provide multi-turn responses by retrieving and analyzing multi-dimensional data objects, allowing for flexible and generic querying across various datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional query systems require specific syntax rules for user interaction, then the system can process queries systematically, but users cannot communicate using natural language and may make mistakes leading to information loss
Solution Approach 1:
The patent introduces a discourse model as an intermediary component that sits between the user's natural language input and the query processing system. This discourse model maintains contextual information from previous interactions and translates natural language queries into structured query parameters, thereby mediating between the user's convenient natural language expression and the system's need for systematic processing.
Solution Approach 2:
The patent replaces the traditional mechanical syntax-rule-based query processing system with a natural language understanding system that uses discourse models and contextual inference. Instead of requiring users to follow rigid syntactic structures, the system uses natural language processing techniques to interpret user intent based on conversational context.
2Device complexity
If query systems only accept one-shot input and generate one-shot output, then the system structure remains simple, but the system cannot infer information from communication history leading to performance deterioration
Solution Approach 1:
The patent transforms the static one-shot query processing system into a dynamic multi-turn conversation system. The discourse model continuously updates contextual information as the conversation progresses, allowing the system to adapt its understanding based on the evolving conversation history. This dynamic approach enables the system to maintain state information across multiple interactions.
Solution Approach 2:
The patent implements preliminary action by pre-processing and storing contextual information from previous turns in the discourse model before generating responses. This allows the system to have contextual information ready and available when processing subsequent queries, enabling faster and more accurate understanding without adding significant complexity to the core processing pipeline.
3Measurement precision
If query systems cannot understand contextual references like 'How about in Germany?', then the system can process explicit queries accurately, but the system fails to infer implicit information from user intent
Solution Approach 1:
The patent implements feedback mechanisms where the discourse model continuously monitors the conversation flow and uses the context from previous exchanges to inform the interpretation of current queries. When a user asks 'How about in Germany?', the discourse model feedbacks the previously mentioned product and query type back into the current query interpretation, enabling the system to understand the implicit reference.
Solution Approach 2:
The patent adds a contextual dimension to the query processing by incorporating discourse history as an additional layer of information. Instead of processing only the explicit query text, the system processes the query in the context of the conversation history, effectively adding a temporal and contextual dimension to the query interpretation process.
Data Source
AI summary
Methods, apparatuses and computer program products for providing a conversational data-to-text system are described herein. An example method may include receiving a first natural language query from a client device; generating a first analytic operation instruction associated with a multi-dimensional dataset based at least in part on the first natural language query; determining a first multi-dimensional data object based at least in part on the first analytic operation instruction and the multi-dimensional dataset; generating a first natural language response to the first natural language query based at least in part on the first multi-dimensional data object; and transmitting the first natural language response to the client device.