Sentiment-Based Interface Zoom Control for Rapid Text Retrieval
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Solution Overview
Problem
Storing and processing large text-based datasets, such as call transcripts, poses challenges due to computational overhead, inefficient file management, and the need for real-time data processing, especially in time-sensitive contexts like earnings calls, which can lead to delays and inaccurate decision-making.
Innovation Solution
Implementing a computer-implemented method involving segmentation of text data, using machine learning systems to identify sentiment and topics, and providing a user interface for rapid retrieval and display of relevant data segments, utilizing lightweight machine learning systems that require less computing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional file systems and processing algorithms are used on large text datasets, then data storage and basic processing can be performed, but processing speed and computational efficiency deteriorate significantly
Solution Approach 1:
The patent segments large text datasets into smaller, manageable chunks and processes them in batches. This allows the system to handle large volumes of data without overwhelming computational resources, improving processing speed while maintaining the ability to analyze extensive datasets.
Solution Approach 2:
The system performs preliminary processing steps such as tokenization, embedding generation, and indexing before actual analysis. This pre-processing prepares the data in advance, enabling faster retrieval and analysis operations when needed, thus improving productivity without requiring proportional increases in computational power.
2Measurement precision
If comprehensive NLP processing is applied to all text data, then analysis accuracy improves, but computational resources and processing time increase substantially
Solution Approach 1:
The patent applies different levels of processing quality to different parts of the data based on their importance and characteristics. Critical segments receive comprehensive NLP analysis while less important segments receive simplified processing, maintaining analysis accuracy for key insights while reducing overall computational resource consumption.
Solution Approach 2:
The system performs partial processing on the majority of data and comprehensive processing only when necessary. By using embeddings and indexing, the system can quickly identify which segments require deep analysis, applying comprehensive NLP processing selectively rather than uniformly, thus reducing total computational resources while maintaining accuracy where needed.
3Loss of time
If real-time processing is implemented for time-sensitive data like earnings calls, then decision-making speed improves, but system complexity and computational load increase
Solution Approach 1:
The system pre-processes and indexes text data in advance, generating embeddings and organizing data structures before actual analysis is needed. This preliminary preparation enables rapid retrieval and processing of time-sensitive data like earnings calls, reducing decision-making time without requiring complex real-time processing systems during actual analysis events.
Solution Approach 2:
The patent creates simplified representations (embeddings) of text data that capture essential meanings in a compressed format. These copying representations can be quickly processed and compared without requiring complex analysis of the original text, enabling real-time decision-making while reducing system complexity by working with simpler data structures.
Data Source
AI summary
In accordance with one aspect of the present disclosure, there is provided a computer-implemented method. The method may include: performing segmentation on text data to generate a number of data segments; providing each of the data segments to a machine learning system; obtaining, as output of the machine learning system, an indication of a sentiment associated with each of the data segments; obtaining an indication of overall sentiment for a plurality of data segments represented by the text data; and providing the indication of the overall sentiment to a device, together with a selectable option to retrieve one or more of the data segments having an indication of sentiment corresponding to the indication of the overall sentiment.