Contextualized Bot Framework Token Matching
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Solution Overview
Problem
Current automated software applications, or 'bots,' often take a long time to perform tasks and respond to requests due to the need for extensive integration and customization across various applications, and may provide inaccurate responses when requests do not exactly match stored responses, especially when dealing with misspellings or synonyms.
Innovation Solution
A contextualized bot framework that uses a machine learning implementation to generate tokens from user requests, match them against stored tokens in a database, and provide responses based on contextual information, allowing for quicker and more accurate responses by leveraging user-specific and contextual parameters, and learning from near matches to improve future interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If bots search across a library of responses to requests, then they can provide accurate responses when requests match stored responses exactly, but they take an unnecessarily long amount of time to perform tasks and respond to requests
Solution Approach 1:
The patent applies preliminary action by pre-processing and tokenizing requests before storing them in the library, and by using token-based indexing to quickly locate potential matches. This allows the bot to avoid searching the entire library of responses, significantly reducing response time while maintaining accuracy for exact matches.
Solution Approach 2:
The patent replaces the mechanical linear search through response libraries with a token-based computational system. By converting requests and responses into token sequences and using hash-based or tree-based lookup structures, the system achieves O(1) or O(log n) retrieval time instead of O(n) linear search, dramatically improving response speed.
2Adaptability or versatility
If bots require customized implementations for each application, then they can be tailored to specific application needs, but deploying bots across a wide range of applications requires an unnecessarily large amount of integration and customization
Solution Approach 1:
The patent implements universality by designing a token-based bot framework that can handle multiple applications and domains through a common interface. The tokenization system and response library structure are application-agnostic, allowing the same bot infrastructure to serve different applications without requiring separate customized implementations, thereby reducing integration complexity.
Solution Approach 2:
The patent applies segmentation by breaking down requests into discrete tokens and organizing responses in a modular library structure. This segmentation allows the bot system to be composed of independent, reusable components that can be configured for different applications through parameter settings rather than code customization, reducing integration effort.
3Reliability
If bots use a library of stored responses, then they can respond to requests that exactly match stored responses, but they may be unable to respond to a request or may provide incorrect or inaccurate responses when requests do not exactly match, such as when requests are misspelled or synonyms are used
Solution Approach 1:
The patent applies parameter changes by transforming requests into token sequences and using token frequency, position, and context as parameters for matching. Instead of requiring exact string matches, the system evaluates multiple token combinations and their statistical properties to determine the most likely intended meaning, enabling reliable responses to misspelled or synonym-based requests.
Solution Approach 2:
The patent implements feedback by analyzing the effectiveness of token-based matching and using this information to refine the response selection process. When a token sequence matches multiple potential responses, the system can use contextual feedback from the conversation history and user behavior to select the most appropriate response, improving reliability for varied requests.
Data Source
AI summary
Methods and apparatus, including computer program products, are provided for a contextualized bot framework. In some implementations, there may be provided a method which may include receiving, by a bot of a contextualized bot network and via a client interface associated with the contextualized bot network, a request, the request comprising a text string sent by the client interface; generating, by the bot, a token based on the text string of the request; determining, by the bot, whether the token generated based on the text string of the request matches a stored token that is stored in a database, based on one or more parameters, the stored token being mapped to an address stored in the database, the address comprising instructions for responding to the request; selecting, by the bot, the address in response to the match between the token generated based on the text string of the request and the stored token to obtain data at the address; and presenting, by the bot, at the client interface, data obtained at the selected address to form a response to the request. Related systems, methods, and articles of manufacture are also disclosed.