Architectural Space Evaluation with Human-Centric AI Scoring
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Solution Overview
Problem
Existing machine learning techniques for human-centric evaluation of architectural spaces lack sufficient large training data sets that include consistent human evaluations, leading to inconsistent or conflicting assessments due to subjective human evaluator expertise and preferences.
Innovation Solution
A computer-implemented method using a machine learning model generates alignment scores based on textual prompts and 2D input images of architectural spaces, refining the model through iterative training on smaller quantities of image-text pairs to provide consistent, quantifiable evaluations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human evaluation is used to collect training data for machine learning models, then the evaluation incorporates human expertise and subjective judgment, but the subjectivity leads to inconsistent and conflicting evaluations across multiple evaluators and over time
Solution Approach 1:
The patent introduces an intermediary consistency check mechanism that mediates between multiple human evaluators. The system automatically detects conflicting evaluations and triggers a resolution process, ensuring that only consistent evaluations are incorporated into training data. This intermediary layer filters out subjective inconsistencies while preserving genuine expert judgment.
Solution Approach 2:
The system implements feedback loops where evaluation results are continuously monitored for consistency. When inconsistencies are detected, the system provides feedback to evaluators to reconcile their judgments. This feedback mechanism ensures that training data maintains high consistency across different evaluators and time periods.
2Measurement precision
If large quantities of training data are collected from multiple evaluators to improve model accuracy, then more diverse perspectives are captured, but the internal inconsistency of the data complicates model training
Solution Approach 1:
An intermediary consistency verification system is introduced between data collection and model training. This system automatically analyzes collected evaluations for internal consistency and filters out conflicting data points before they reach the training pipeline, ensuring that only reliable, consistent data is used for training.
Solution Approach 2:
The patent replaces manual consistency checking with automated computational methods. Machine learning algorithms and statistical analysis tools automatically detect and resolve inconsistencies in training data, substituting human judgment with systematic computational processes that can handle large datasets efficiently while maintaining consistency.
3Productivity
If existing machine learning techniques are used with available training data, then model development can proceed with current resources, but the lack of sufficiently large and consistent training data limits model performance
Solution Approach 1:
The system performs preliminary consistency verification and data quality assessment before model training begins. By pre-processing and validating training data for internal consistency upfront, the system ensures that models are trained on high-quality data, improving evaluation accuracy without delaying the development timeline.
Solution Approach 2:
The patent creates standardized templates and protocols for data collection that can be replicated across multiple evaluators and projects. These standardized copying mechanisms ensure consistent data quality and format, enabling efficient model development with reliable training data from diverse sources.
Data Source
AI summary
One embodiment of the present invention sets forth a technique for evaluating architectural spaces. This technique includes receiving a 2D input image of an architectural space and generating one or more prompts. Each prompt includes one of a plurality of human-centric criteria. The plurality of human-centric criteria may include terms such as “social,”“isolating,”“tranquil,”“distracting,”“inspirational,” or “boring.” The technique also includes generating, via a trained machine learning model, an alignment score associated with each of the prompts and the 2D input image, wherein the alignment score indicates a degree of alignment between the prompt and the 2D input image. The technique further includes storing the 2D input image and generated alignment scores for later retrieval and/or presentation to a user.