Real-Time Recommendation Engine Using NLP Tagging

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Solution Overview

Problem

The abundance of data from various sources poses a challenge in determining the optimal values and sources for use, requiring extensive domain expertise and time-consuming evaluation processes that are not compatible with modern applications' demands.

Innovation Solution

A system for generating real-time recommendations that includes processors executing instructions to receive requests, generate tags, extract observations and actions, predict recommendations, and send them for display to a user device, utilizing natural language processing and machine learning to prioritize data sources and provide accurate predictions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional exhaustive analysis methods are used to evaluate data sources, then accuracy and domain expertise requirements are met, but time consumption and resource requirements increase significantly

Engineering Contradiction:

Improveaccuracy of data evaluationVSAvoidtime consumption for evaluation

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and tagging data sources in advance, creating structured representations that can be quickly queried and evaluated. This allows the system to have evaluation results ready before they are actually needed, reducing the time required for on-demand analysis while maintaining accuracy through pre-computed expertise-based evaluations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces manual exhaustive analysis (mechanical human effort) with automated natural language processing and machine learning systems. The NLP engine and recommendation engine automatically evaluate data sources, extract observations, and generate recommendations without requiring human domain experts to manually review each data source, thus maintaining accuracy while dramatically reducing time consumption

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If detailed and individualized data analysis is performed for each personalized scenario, then recommendation accuracy improves, but processing time and computational resources increase

Engineering Contradiction:

Improverecommendation accuracyVSAvoidprocessing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The system segments the recommendation process into distinct modular components: data source evaluation, tag generation, observation extraction, and recommendation generation. Each component processes specific aspects of the data independently, allowing for efficient parallel processing and reducing the overall time required to provide accurate personalized recommendations

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system changes parameters by using configurable thresholds and weights that can be adjusted based on specific scenario requirements. The recommendation engine can modify evaluation criteria and processing depth according to the particular needs of each personalized scenario, maintaining high accuracy while adapting processing resources to match the required level of detail

Inventive Principle:

Principle #35Parameter changes

3Reliability

If comprehensive data source evaluation is conducted, then reliability of recommendations improves, but system complexity and resource requirements increase

Engineering Contradiction:

Improvereliability of recommendationsVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system introduces intermediary components including the NLP engine that translates unstructured data into structured observations, and the recommendation engine that acts as a mediator between raw data and final recommendations. These intermediaries simplify the overall system architecture by breaking down complex evaluation tasks into manageable stages, maintaining reliability through systematic processing while reducing apparent system complexity

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent implements universal components that can handle multiple types of data sources and evaluation criteria through a single integrated system. The recommendation engine and NLP engine are designed to work with various data formats and domains, reducing system complexity by avoiding the need for separate specialized systems for each data type while maintaining comprehensive and reliable evaluation across diverse sources

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250103830A1Systems and methods for providing real-time recommendations using natural language processing

Publication Date: 2025.03.27 HEALTH INC

AI summary

Methods, systems, and computer-readable media for the generation of real-time recommendations using natural language processing. The method receives a request for a benefit recommendation; generates at least one tag based on input data; extracts, based on the at least one tag, at least one observation and at least one action from the input data; predicts at least one recommendation based on the extracted at least one observation and the extracted at least one action in real time; and sends the at least one predicted recommendation for display to a user device.