AI Auto-Naming for Customer Behavior Tree Nodes
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Solution Overview
Problem
Existing systems lack an efficient method to automatically name customer behavior tree nodes, which hinders optimal categorization and display of products, affecting customer satisfaction and cross-selling of similar items in retail settings.
Innovation Solution
A computer-implemented method using a processor to generate a hierarchy of nodes, create term-document matrices, identify high-frequency words, apply models like n-gram frequency, common themes, and word vector representation, and incorporate user feedback to predict and select node names, ensuring optimal naming and categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual naming of customer behavior tree nodes is performed, then naming accuracy can be ensured, but time consumption and operational complexity increase significantly
Solution Approach 1:
The system enables automatic self-naming of customer behavior tree nodes by analyzing product descriptions, attributes, and purchase patterns. The AI model autonomously generates node names without requiring manual human input, thus eliminating time consumption while maintaining naming accuracy through sophisticated text analysis and machine learning algorithms.
Solution Approach 2:
The patent replaces the manual mechanical process of naming nodes with an automated AI-based system. The system uses natural language processing, term-document matrices, and machine learning models to automatically generate node names, substituting human manual operations with computational processes that are both faster and equally accurate.
2Productivity
If automatic naming methods are implemented, then time consumption is reduced, but naming precision and accuracy deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms where the AI model generates multiple candidate node names, evaluates them based on predefined criteria such as relevance to product attributes and purchase patterns, and selects the most accurate name. This feedback loop ensures that automatic naming maintains high precision while achieving rapid processing.
Solution Approach 2:
The patent employs multiple adjustable parameters in the AI model including term-document matrix thresholds, n-gram frequency cutoffs, and similarity scoring parameters. By optimizing these parameters, the system achieves both high naming efficiency and accurate results that align with business requirements and customer behavior patterns.
3Measurement precision
If complex AI models are used for node naming, then naming accuracy improves, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the complex AI naming system into distinct modular components: data preprocessing module, term-document matrix generation module, candidate name generation module, evaluation and selection module, and feedback integration module. Each module performs a specific function, making the overall complex system manageable, maintainable, and scalable while achieving accurate node naming.
Solution Approach 2:
The AI system is designed with universal components that can handle multiple naming scenarios across different product categories and retail contexts. The same core architecture and algorithms are reused across various applications, reducing overall system complexity while maintaining high naming accuracy through proven multi-functional modules.
4Measurement precision
If multiple preprocessing steps are applied to remove high-frequency words, then naming precision improves, but processing time and computational complexity increase
Solution Approach 1:
The system applies selective preprocessing by removing only the most critical high-frequency words that do not contribute to meaningful node names (such as common stop words and generic terms). This partial action approach removes sufficient noise to improve naming precision without applying excessive preprocessing steps that would unnecessarily increase computational complexity and processing time.
Data Source
AI summary
Systems and methods for auto-naming nodes in a behavior tree are provided. An example method can include: providing a hierarchy of tree nodes by a computing device; generating a first corpus for each node at a final level; creating a first term-document matrix associated with the first corpus; identifying a first group of high-frequency words in the first term-document matrix; removing the first group of the high-frequency words obtain a second corpus; creating a second term-document matrix based on each of a set of predefined rules; identifying a second group of high-frequency words to represent node names; selecting a best set of the predefined rules based on an automatic evaluation model; generating a node name by removing a duplicate word in each node; incorporating feedback to generate a predicted name for each node; and selecting a final name for each node from the predicted name and the generated node name.