Home Energy Assessment Platform for ML-Based Lead Qualification
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Solution Overview
Problem
The process of conducting comprehensive home energy assessments and identifying high-potential leads for energy efficiency upgrades and solar installations is complex and challenging, relying on manual data collection and analysis that is time-consuming and prone to inaccuracies.
Innovation Solution
An integrated home energy assessment platform utilizing generative artificial intelligence to query multiple third-party databases, analyze data via machine learning, and generate scores for energy-related features, categorizing homes based on scoring categories, and determining overall scores for lead prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and analysis methods are used for home energy assessments, then the process can be performed with simple tools, but the assessment accuracy and lead identification precision deteriorate due to time-consuming operations and human errors
Solution Approach 1:
The patent replaces manual data collection and analysis methods with an automated machine learning system. The system queries multiple third-party databases programmatically, uses ML models to analyze energy-related features, and automatically generates lead scores, eliminating human manual operations while improving both accuracy and speed of assessment
Solution Approach 2:
The system performs self-service by automatically querying databases, processing data, and generating assessments without requiring manual intervention. The machine learning model autonomously identifies energy features, calculates scores, and prioritizes leads, making the assessment process self-executing and highly efficient
2Reliability
If comprehensive data from multiple third-party databases is collected and analyzed via machine learning, then the lead scoring accuracy and energy feature identification improve, but the system complexity and computational resources required increase
Solution Approach 1:
The patent segments the complex assessment process into distinct functional modules: data collection module that queries multiple third-party databases, machine learning analysis module that identifies energy features, and scoring module that generates lead scores. This modular segmentation manages system complexity while maintaining high accuracy through specialized processing in each module
Solution Approach 2:
The machine learning model acts as an intermediary between raw data from multiple databases and the final lead scoring output. It processes and integrates data from diverse sources, transforming complex multi-source information into reliable standardized assessments, thereby managing system complexity while improving reliability
3Productivity
If automated machine learning analysis is implemented to identify energy-related features and generate lead scores, then the productivity and efficiency of the assessment process increase, but the initial development cost and technical complexity increase
Solution Approach 1:
The patent implements a universal machine learning platform that can assess multiple types of energy features (solar potential, energy efficiency, renewable energy suitability) across different property types and regions. This multi-functional system increases productivity by handling diverse assessments with a single platform, though it requires sophisticated development to achieve such versatility
Data Source
AI summary
An integrated home energy assessment apparatus, method, and computer program product are disclosed. The apparatus includes one or more processors and non-transitory computer readable storage media storing code. The code is executable by the processors to perform operations that include querying third-party databases for information pertaining to a home and receiving the information from the third-party databases. The operations include analyzing, via machine learning, the information to identify energy-related features of the home. Each of the features corresponds to a scoring category. The operations include determining a lead score corresponding to each feature and aggregating the lead scores within the scoring category to generate a category score. The operations include determining an overall score for the home based on the category score for each of the scoring categories.