Natural Language UX Orchestration via Token Sequences
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Solution Overview
Problem
Achieving a desired user experience in complex ecosystems is often repetitive, time-consuming, and prone to human error, requiring extensive knowledge and numerous interactions, especially in large ecosystems where the number of possible operations is astronomical, making automated user experience orchestration challenging.
Innovation Solution
A computer-implemented method using a machine learning model trained on natural language instructions to determine sequences of operations in a domain-specific language, allowing users to input natural language requests to generate desired results without manual navigation through multiple features, by tokenizing ecosystem operations and generating sequences of tokens that produce the desired outcome.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual navigation through multiple features is used to achieve desired results in complex ecosystems, then users can access and interact with various features, but the process becomes repetitive, time-consuming, and prone to human error
Solution Approach 1:
The patent introduces an intermediary system comprising a language model and token operator that translates natural language instructions into sequences of ecosystem operations. This intermediary automatically navigates the complex ecosystem on behalf of the user, eliminating manual navigation through multiple features and significantly reducing both time required and potential for human error while maintaining ease of operation through simple natural language input
Solution Approach 2:
The system enables self-service by allowing users to input natural language descriptions of desired results without needing to manually navigate through the ecosystem's features. The automated orchestration system independently determines and executes the necessary sequence of operations, making the user experience simple and intuitive while the system handles the complex navigation automatically
2Reliability
If extensive knowledge and skill with the ecosystem is required to perform tasks, then users can achieve desired results, but the process becomes difficult and requires significant experience
Solution Approach 1:
The intermediary translation system converts natural language instructions into precise sequences of ecosystem operations, ensuring accurate execution of desired results without requiring users to have extensive knowledge of the ecosystem. The language model and token operator handle the complexity of mapping user intent to specific operations, maintaining reliability while dramatically reducing the difficulty of performing tasks
Solution Approach 2:
The patent replaces the mechanical system of manual navigation and interaction with an automated intelligent system that uses natural language processing and machine learning. This substitution eliminates the need for users to acquire extensive ecosystem knowledge and skill, as the automated system interprets natural language and executes the appropriate operation sequences with high accuracy
3Adaptability or versatility
If the number of possible operations in large ecosystems is astronomical, then the ecosystem provides comprehensive functionality, but determining the correct sequence of operations without user input becomes practically impossible
Solution Approach 1:
The intermediary system comprising the language model and token operator serves as a intelligent mediator that navigates the astronomical number of possible operations by interpreting natural language instructions and determining the correct sequence of operations. This intermediary reduces the effective complexity from the user's perspective while preserving the complete functionality of the ecosystem, as the system independently resolves the combinatorial explosion of possible operation sequences
Solution Approach 2:
The system changes the parameter of operation specification from requiring detailed step-by-step navigation instructions to accepting high-level natural language descriptions of desired results. This parameter change allows the ecosystem to maintain its comprehensive functionality and astronomical number of possible operations while making operation sequencing feasible through intelligent interpretation and automated determination of correct operation sequences
Data Source
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AI summary
Certain aspects of the present disclosure provide techniques for orchestrating a user experience using natural language input. A user experience is orchestrated within an ecosystem of features for which a plurality of corresponding tokens is defined. Natural language describing a desired user experience result is received by a user experience orchestrator. A sequence of tokens corresponding to operations belonging to an ecosystem of features which produce a correct result for the natural language input can be identified by a trained large language model and executed by the user experience orchestrator using a token operator. The output operations determined by the model to produce or be likely to produce the correct result based on the natural language input can be disambiguated, confirmed, and/or executed.