Hierarchical Topic Clustering for Digital Assistant Command Interpretation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional digital assistants face challenges in accurately understanding the context of received commands, leading to misinterpretations and limitations in executing user-intended actions, especially with vague or undefined commands, and struggle to adapt to diverse user dialects and natural language variations.
Innovation Solution
The development of improved command interpretation techniques using principles of discourse communities, semiotics, and intertextuality to generate sophisticated language models that enable digital assistants to accurately decipher user inputs and select appropriate actions, even with undefined or unconventional commands, by creating a command-specific language model index and employing synthetic documents to enhance context understanding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language processing algorithms are used to interpret commands, then the digital assistant can process basic commands, but it frequently misinterprets spoken commands and cannot handle vague or undefined commands accurately
Solution Approach 1:
The patent segments the interpretation process into multiple stages: first identifying potential topic clusters from the command, then determining which cluster best matches the user's intent, and finally selecting the appropriate action. This multi-stage segmentation allows the system to handle ambiguity by evaluating multiple possible interpretations before committing to a specific action.
Solution Approach 2:
The patent creates a universal topic cluster structure that can accommodate multiple languages, dialects, and domains. By organizing commands into hierarchical topic clusters with general and specific sub-clusters, the system achieves multi-functionality that handles diverse user inputs while maintaining accurate interpretation through the structured matching process.
2Adaptability or versatility
If the digital assistant uses a comprehensive language model to understand all possible commands, then it can handle diverse dialects and languages, but the system complexity and processing time increase
Solution Approach 1:
The language model is segmented into hierarchical topic clusters with general clusters at higher levels and specific sub-clusters at lower levels. This segmentation reduces complexity by organizing the vast language space into manageable hierarchical groups, allowing the system to handle diverse languages and dialects without requiring a single monolithic complex model.
Solution Approach 2:
The system performs preliminary action by pre-organizing commands into topic clusters before actual command interpretation. This preliminary structuring of language data into hierarchical clusters enables faster processing during actual use, as the system can quickly navigate the pre-organized structure rather than analyzing all possible interpretations from scratch.
3Measurement precision
If the digital assistant requires extensive training data to improve accuracy, then it can better understand context, but the training time and data requirements increase significantly
Solution Approach 1:
The topic cluster structures are preliminarily organized and stored before actual command interpretation occurs. This preliminary organization of language data into hierarchical clusters eliminates the need for extensive real-time training, as the system can directly match commands against the pre-structured clusters, achieving accurate context understanding without prolonged training periods.
Data Source
AI summary
Disclosed are techniques for automatically extracting discovered topics and/or from determined discourse clusters for the generation of a language model that is applicable to interpreting commands received from a digital assistant device. An electronic document corpus can be generated having a plurality of documents that are clustered based on entropy, among other things. The clusters can be associated with a corresponding plurality of cluster attractors that are generally representative of a context of the documents included therein. The documents within the cluster for each of the document clusters can be analyzed, so that clusters determined representative of a hierarchical discourse community can be determined and logically merged. The merged clusters can be analyzed, such that topics and/or sub-topics can be determined and extracted therefrom, for indexing and storage, among other things. In this way, a more efficient searching of the electronic document corpus to interpret received inputs, such as commands received via a digital assistant device, can be facilitated.