Conversational Interface for Hierarchical Data Access
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Solution Overview
Problem
Users in the financial industry face inefficiencies and errors due to the need to access and manage data from multiple disparate systems with different terminologies, protocols, and access restrictions, especially when using audio input and speech recognition systems prone to transcription errors.
Innovation Solution
A system and method that processes user queries through a conversational interface, using hierarchical question sets and APIs to identify and retrieve data from authorized sources, with string and API matching to ensure accurate and efficient data access while accounting for user permissions and data formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users access multiple disparate data sources manually, then data retrieval is possible, but efficiency decreases and workload increases
Solution Approach 1:
The patent introduces a conversational interface as an intermediary layer between users and multiple data sources. This interface automatically routes queries to appropriate sources, eliminating the need for users to manually access each system. The intermediary handles protocol translation, authentication, and data aggregation, thereby improving productivity while reducing time loss.
Solution Approach 2:
The conversational interface serves as a universal access point that can query multiple different data sources through a single interface. It supports various query types and adapts to different data source protocols, allowing users to access diverse information without learning multiple system-specific interfaces, thus enhancing efficiency and reducing time investment.
2Measurement precision
If users learn multiple platform-specific terminologies and codes, then accurate data retrieval is possible, but ease of operation decreases
Solution Approach 1:
The conversational interface acts as a mediator that translates user-friendly natural language queries into platform-specific terminologies and codes. It automatically maps generic terms like 'commodity price' to specific platform codes such as 'WTI' or 'CL' without requiring users to learn these codes, thereby maintaining retrieval accuracy while simplifying operation.
Solution Approach 2:
The system dynamically changes the parameter representation based on the target data source. It automatically adapts terminology, date formats, and query structures to match each platform's requirements while presenting a consistent interface to users. This parameter transformation occurs transparently, ensuring accurate data retrieval without increasing operational complexity.
3Productivity
If speech recognition systems are used for rapid input, then input speed increases, but transcription accuracy decreases
Solution Approach 1:
The system implements feedback mechanisms where transcription results are automatically verified and corrected. It uses contextual analysis, query validation, and confidence scoring to detect and correct transcription errors. When accuracy thresholds are not met, the system prompts users for clarification, thereby maintaining high input speed while improving transcription precision through iterative feedback.
Solution Approach 2:
The patent combines multiple input modalities and processing methods to create a composite input system. It integrates speech recognition with text-based correction, contextual analysis, and query validation. This composite approach leverages the speed of speech input while compensating for its accuracy limitations through additional verification layers, achieving both rapid input and high precision.
4Reliability
If hierarchical question sets with multiple authorization levels are implemented, then security is improved, but device complexity increases
Solution Approach 1:
The patent segments the question set into hierarchical levels with distinct authorization requirements. Each level contains specific queries that require particular permission levels, allowing fine-grained security control. This segmentation enables the system to enforce security policies without requiring complex custom authentication logic for each query, as the hierarchical structure inherently organizes access control rules.
Solution Approach 2:
The hierarchical authorization system serves multiple functions: it controls access to different data sources, manages user permissions, and organizes query categories. This multi-functional design reduces overall system complexity by consolidating security management into a unified hierarchical framework rather than requiring separate authentication mechanisms for each data source or user role.
Data Source
AI summary
An ecosystem can be used for processing user questions and commands received via a conversational interface in a hierarchical permissioning manner. The questions and commands which a user is permissioned to ask/issue are determined at user logon. The ecosystem may be configured to access and return appropriate answers from a variety of sources, but only for questions which the user is permissioned to ask. In addition, the ecosystem can be configured to execute commands which may be received by the user via the conversational interface, but only for the commands which the user is permissioned to issue.The ecosystem may include a computer system configured to receive a text query representing a question asked from the user, score the questions which the user is permissioned to ask, select a high-scoring question, use an API to receive information responsive to the high-scoring question, and output the answer to the user.