Language Model Information Miner for Document Classification and Q&A

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Solution Overview

Problem

Conventional information processing technologies are error-prone, costly, and lack flexibility and nuance, leading to inefficiencies and inconsistencies in handling large volumes of structured and unstructured data, particularly in enterprise environments.

Innovation Solution

Utilizing a combination of extractive, abstractive, and structured miners facilitated by language models like ROBERTa and llama2-7B to process and classify documents, generate summaries, and answer queries, with feedback loops for model improvement.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional information processing technologies are used, then processing can be performed, but the processing is error-prone and lacks accuracy

Engineering Contradiction:

Improveprocessing accuracyVSAvoiderror rate

Core Design Contradiction:

Measurement precisionVSReliability

Solution Approach 1:

The patent implements feedback loops where processing results are validated against ground truth data, and model parameters are continuously refined based on performance metrics. This feedback mechanism reduces errors and improves accuracy by iteratively optimizing the processing system based on actual performance data.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces conventional mechanical information processing systems with AI-based neural network models that can learn and adapt patterns from data. This substitution enables more accurate and reliable processing by leveraging machine learning capabilities rather than rigid rule-based systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of manufacture

If conventional information processing technologies are used, then processing can be performed, but costly licensing fees and subscriptions are required

Engineering Contradiction:

Improvecost-effectivenessVSAvoidlicensing cost

Core Design Contradiction:

Ease of manufactureVSLoss of energy

Solution Approach 1:

The patent employs open-source language models that can be freely deployed without expensive licensing fees. These models provide sufficient processing capability at minimal cost, replacing proprietary solutions that require ongoing subscription payments, thereby eliminating recurring licensing expenses.

Inventive Principle:

Principle #27Cheap short-living objects (Disposable)

Solution Approach 2:

The system uses open-source models that can be self-deployed and maintained without requiring expensive commercial licenses. The organization can independently host, update, and manage the processing models, eliminating dependency on vendor licensing arrangements and reducing ongoing costs.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If conventional information processing technologies are used, then processing can be performed, but flexibility and nuance are insufficient

Engineering Contradiction:

ImproveflexibilityVSAvoidprocessing sophistication

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic processing where the system can adapt its approach based on input characteristics. Different processing strategies are applied depending on the document type, complexity, and required output format, enabling flexible handling of diverse information processing tasks rather than relying on fixed rigid processes.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent employs multi-functional AI models that can perform various processing tasks including classification, summarization, extraction, and analysis within a single unified system. This universal approach provides greater flexibility compared to specialized conventional tools, allowing the same system to handle diverse processing requirements.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If manual information processing is performed, then detailed processing can be achieved, but significant effort and time are required

Engineering Contradiction:

Improveprocessing speedVSAvoidprocessing time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces manual mechanical processing with automated AI-based systems that can analyze and process information at speeds impossible for human operators. Neural network models rapidly process large volumes of data through parallel computation, dramatically increasing productivity while reducing the time required for processing tasks.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces AI models as intermediary systems between raw data and final outputs. These intermediaries automatically perform complex processing tasks including understanding, analysis, and synthesis of information, eliminating the need for manual intervention and significantly accelerating the processing workflow.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20260056965A1Apparatuses and methods for facilitating an information miner using language models

Publication Date: 2026.02.26 JPMORGAN CHASE BANK NA

AI summary

Aspects of the subject disclosure may include, for example, obtaining a model, obtaining a document, processing the document based on the model, the processing of the document resulting in a classification of a type of the document, generating, based on the classification of the type of the document, a first output, the first output including a first summary of content of the document along with a capability to answer any cadence queries based on document type and any free text query from a user, and transmitting the first output to a first communication device. Other aspects are disclosed.