Virtual AI Consultant for Strategic Topic Analysis
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Solution Overview
Problem
Current search engines and human consultants face challenges in efficiently and accurately providing strategic information and relevant documents to users, especially in analyzing large volumes of data to determine topics and likelihood of success for entities, which can be time-consuming and costly.
Innovation Solution
A virtual consultant system utilizing artificial intelligence, natural language processing, and machine learning to adaptively generate content by crawling digital information, identifying raw topics, determining their frequencies, and assigning topic scores to provide strategic insights and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human consultants analyze large volumes of data to determine topics and likelihood of success, then accuracy of strategic information can be maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the mechanical system of human consultants with an automated AI-based system that uses natural language processing, machine learning models, and topic modeling algorithms to analyze documents, extract topics, and determine entity success likelihood, thereby eliminating time consumption and cost while maintaining or improving accuracy
Solution Approach 2:
The system enables self-service by automatically crawling documents, training models on extracted topics, generating topic scores, and providing strategic insights without requiring human intervention at each step, allowing the system to serve itself in processing and analyzing large volumes of data
2Reliability
If human consultants provide strategic information and analyze documents, then quality of insights can be maintained, but cost increases significantly
Solution Approach 1:
The patent replaces expensive human consultant services with an automated AI system that uses trained machine learning models to provide strategic insights, thereby dramatically reducing cost while maintaining or improving the quality and reliability of insights through consistent application of analytical algorithms
Solution Approach 2:
The system changes the parameters of the consulting service by transitioning from human-based analysis to algorithm-based analysis, using topic scores, frequency weights, and model confidence scores as quantitative parameters to measure and deliver insight quality at low cost
3Productivity
If search engines crawl and index webpages to provide search results, then information retrieval capability is improved, but ability to provide strategic insights and determine topic relevance deteriorates
Solution Approach 1:
The patent segments the information retrieval process into distinct stages: document crawling, topic extraction using NLP, model training on extracted topics, topic scoring based on frequency and relevance, and strategic insight generation. This segmentation allows each stage to specialize and improve upon the limitations of simple search engine indexing
Solution Approach 2:
The patent introduces an intermediary layer of topic modeling and machine learning models between the raw document corpus and the final insights. This intermediary processes crawled documents through trained models that extract and score topics, thereby transforming basic information retrieval into precise strategic analysis with measured topic relevance
4Quantity of substance
If a system processes large volumes of documents to identify topics and trends, then comprehensiveness of analysis is improved, but computational complexity and resources increase
Solution Approach 1:
The patent applies preliminary action by training machine learning models on extracted topics before deploying them for large-scale document analysis. This pre-training phase creates reusable models that can efficiently process large volumes of documents without requiring complex real-time computation, thereby managing computational complexity while maintaining comprehensiveness
Solution Approach 2:
The system uses copying by creating trained machine learning models that replicate the topic extraction and scoring process. Once a model is trained on a subset of data, it can be copied and applied to large volumes of documents, reducing computational complexity compared to processing each document individually with full analytical complexity
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a virtual consultant. The methods, systems, and apparatus include actions of obtaining a set of training data that indicates raw topics that are indicative of the set of topics, training a model to identify raw topics relevant to a set of topics, the set of topics associated with one or more digital documents, identifying, within at least one of the digital documents and with the model, raw topics that correspond to the set of topics, determining, for each identified raw topic, a frequency that the raw topic appears in the digital document, determining, for each of the identified raw topics, a topic that corresponds to the raw topic, determining, for each of the topics of the set of topics, a topic score, and providing an indication of the topic scores for the digital document.