Machine Learning Module for Personalized Realtor Script Generation
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Solution Overview
Problem
Existing systems for generating scripts for realtors do not effectively predict and weight parameters based on user preferences, failing to provide an efficient and economical solution for real estate marketing.
Innovation Solution
A system and method utilizing a head-mounted display (VR, MR, XR) connected to a server via a network, which includes a machine learning module to predict user parameters and assign weights based on entered preferences, incorporating databases and modules for data processing and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing systems for generating scripts for realtors are used, then basic script generation is possible, but the systems fail to predict and weight parameters based on user preferences, reducing marketing effectiveness
Solution Approach 1:
The machine learning module pre-processes and analyzes user preferences before script generation, predicting relevant parameters and assigning weights in advance. This preliminary analysis enables the system to generate more accurate and personalized realtor scripts by having parameter predictions ready before the actual script creation process begins.
Solution Approach 2:
The system dynamically changes and adjusts parameters based on predicted user preferences. The machine learning module modifies script parameters such as property features, pricing strategies, and marketing approaches according to the weighted predictions, allowing the script to adapt to specific user needs and preferences rather than using fixed templates.
2Measurement precision
If complex features are added to existing realtor script systems, then parameter prediction capability improves, but implementation and maintenance costs increase
Solution Approach 1:
The patent introduces a server as an intermediary component that hosts the machine learning module and databases separately from the client devices. This architecture allows complex AI processing to occur on the server while clients receive simplified script generation outputs, reducing the complexity burden on individual devices and making the system easier to implement and maintain across multiple platforms.
Solution Approach 2:
The machine learning module serves multiple functions: it predicts user preferences, analyzes property data, determines parameter weights, and generates script content. This multi-functional approach consolidates what could be separate complex systems into a single versatile module, reducing overall system complexity while maintaining high prediction accuracy.
Data Source
AI summary
A system and method for generating a master realtor script to aid marketing and sales of new development properties with manually entered parameters or parameters gathered through machine learning. The system includes a VR headset that is in communication with a server via a communication network. The server includes a database containing preferred parameters of a given user for a real estate option that are either manually entered or generated by a machine learning module. This may include, number of rooms, color, housing type, preferred furniture, family members, house layout, and other parameters. The system will then predict user preferences based on the predetermined parameters and assign a weight to each different type of parameters. Afterwards, a master realtor script is averaged and provided in real time via a virtual reality headset based on the weighted parameters.