Template-Based Knowledge Data Generation for Conversational AI Systems
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Solution Overview
Problem
The existing information providing systems face significant complexity and maintenance burdens when dealing with a large number of input character strings and topics, making it difficult to generate and manage knowledge data effectively.
Innovation Solution
The system employs template data to generate knowledge data, including assumed input sentences and response sentences, which reduces labor by allowing companies to create knowledge data from existing data and manage it more efficiently, enabling flexible responses based on dynamically acquired data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If knowledge data is manually created for each input character string and response, then response accuracy is improved, but the complexity and maintenance burden increase significantly
Solution Approach 1:
The patent segments knowledge data into template portions (reusable patterns) and specific portions (variable data). This segmentation allows the system to store only the structural framework once, then generate multiple specific knowledge entries by filling in different variables, thereby reducing overall complexity while maintaining response accuracy.
Solution Approach 2:
The patent performs preliminary action by pre-defining template structures for common question-response patterns. These templates are created in advance with placeholders for specific variables, allowing the system to quickly generate specific knowledge data without manual creation of each individual entry, thus reducing maintenance burden while preserving accuracy.
2Adaptability or versatility
If the number of input character strings and topics is increased to cover more user queries, then system versatility is improved, but the maintenance burden and complexity increase heavily
Solution Approach 1:
The patent implements universality by creating template structures that can serve multiple topics and query types. A single template can generate knowledge data for numerous different topics by substituting different variable values, allowing the system to achieve high versatility without proportionally increasing maintenance burden.
Solution Approach 2:
The patent uses copying by generating specific knowledge data entries from template patterns. Instead of manually creating each knowledge entry, the system copies the template structure and fills in specific variables, enabling rapid expansion of topic coverage while keeping maintenance work focused on updating templates rather than individual entries.
3Productivity
If template data development is used to generate knowledge data, then productivity in knowledge data generation is improved, but the initial setup complexity increases
Solution Approach 1:
The patent applies preliminary action by establishing template structures in advance that encode common question-response patterns. Although this requires initial setup effort, it enables high-speed generation of specific knowledge data thereafter, significantly improving productivity for large-scale knowledge data creation while the template complexity remains manageable through reuse.
Data Source
AI summary
To provide a scheme capable of easily generating knowledge data containing an enormous number of assumed input sentences (input sentences assumed to be inputted by a user) in an information providing system realizing a conversation with the user by outputting responses corresponding to inputs by the user to a terminal of the user. An information providing system develops sets of assumed input sentences and response sentences with respect to respective data stored in intermediate data based on a set of assumed input sentences and response sentences defined in template data in accordance with an instruction of a client or the like to generate knowledge data.