Cognitive Real Estate Advisor Engine for Buyer-Seller Matching
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Solution Overview
Problem
Current real estate transaction models are inefficient, giving buyers and sellers limited information and uncertainty about matches, with brokers holding most leverage, leading to subjective analysis due to time constraints and lack of direct access to relevant data.
Innovation Solution
A cognitive system with a real estate advisor engine that processes buyer and seller immutable records, using machine learning to identify matches by generating buyer and real estate profiles, providing ranked lists of properties or buyer candidates with confidence scores and supporting evidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If current real estate searching systems are used, then information flow is managed through brokers, but buyers and sellers have limited information and uncertainty about matches
Solution Approach 1:
The patent introduces a cognitive system as an intermediary between buyers and sellers, using natural language processing and machine learning to analyze immutable records and facilitate direct matching. This mediator processes information objectively without broker influence, providing both comprehensive information access and reliable match certification simultaneously.
Solution Approach 2:
The system enables buyers and sellers to directly access and analyze each other's immutable records through the cognitive system's natural language interface. Parties can independently verify information and assess compatibility without broker mediation, achieving both full information availability and self-determined match reliability.
2Ease of operation
If brokers manage the transaction model, then leverage resides in brokers, but the system becomes inefficient and subjective due to time constraints
Solution Approach 1:
The patent replaces the mechanical broker-mediated transaction system with a cognitive system that uses natural language processing, machine learning, and blockchain technology. This substitution eliminates human time constraints and subjectivity, enabling automated analysis of immutable records and objective match recommendations without broker involvement.
Solution Approach 2:
The system changes the fundamental parameters of the transaction model by using immutable blockchain records instead of broker-held information, and natural language processing instead of traditional search criteria. These parameter changes enable comprehensive information analysis without time constraints and eliminate human subjectivity from the matching process.
3Quantity of substance
If traditional searching systems provide limited real estate information, then analysis is required, but results become subjective due to time constraints
Solution Approach 1:
The cognitive system replaces human analysis with machine learning algorithms that process immutable records containing comprehensive information about buyers, sellers, and properties. This substitution enables analysis of vast information volumes without time constraints and eliminates human subjectivity, providing objectively accurate match recommendations.
Solution Approach 2:
The cognitive system acts as an intermediary that objectively processes and analyzes comprehensive information from immutable records using natural language processing and machine learning. This mediator provides unbiased, accurate match recommendations based on complete information analysis without human time constraints or subjectivity.
Data Source
AI summary
Embodiments can provide a computer implemented method for identifying a match between a commercial buyer and a seller for a real estate transaction. The method includes receiving, from the buyer, a service request and receiving, from the buyer, historical information stored in a buyer immutable record. The method also includes receiving one or more real estate requirements and one or more commercial external factors and determining a buyer need profile based on the historical information, the real estate requirements, and the commercial external factors. The method also includes receiving one or more answers in response to one or more first questions raised by the processor, refining the buyer need profile based on the one or more answers, identifying a match between the buyer need profile and a real estate profile from the seller, and providing a ranked list of real estate properties and supporting evidence for each real estate property to the buyer.