Automated Code Generation for Natural Language Interaction Applications
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Solution Overview
Problem
Current natural language interaction applications, such as virtual assistants, are limited by their rigid scripting and high development costs, making it difficult for organizations of all sizes to create and maintain systems that can interpret and respond to a wide range of natural language inputs, including casual conversational elements.
Innovation Solution
A system and method for supervised automatic code generation and tuning of natural language interaction applications, utilizing a build environment with automated coding, testing, and optimization tools, which automatically generates and refines language recognition rules based on semantic content analysis and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If natural language interaction applications use rigid scripting to carry out limited activities, then development cost and complexity are reduced, but the communication language becomes unnatural and user acceptance is limited
Solution Approach 1:
The system automatically generates language recognition rules, language objects, and flow elements from text samples without requiring manual programming by linguistic experts. The automated coding tools analyze semantic content and produce working code, allowing the system to serve itself in the development process.
Solution Approach 2:
The system transforms unstructured text samples into structured language recognition rules by changing the parameter representation from raw text to coded linguistic structures. This automatic transformation enables the system to handle diverse natural language inputs without manual scripting for each case.
2Reliability
If natural language interaction applications are developed by teams of linguistic experts with technical skills, then natural language interpretation capability is improved, but development time and cost increase significantly
Solution Approach 1:
The automated coding tools perform the work that would traditionally require linguistic experts with technical skills. The system automatically analyzes text samples, generates language recognition rules, and produces working code, eliminating the need for specialized human expertise and dramatically reducing development time.
Solution Approach 2:
The system replaces the manual mechanical process of expert developers writing code with an automated computational process. The automated coding tools use algorithms to analyze semantic content and generate code automatically, substituting human intellectual labor with machine-based processing.
3Measurement precision
If manual testing is performed to ensure proper behavior of natural language interaction applications, then testing accuracy is improved, but testing becomes extremely time-consuming and difficult to oversee
Solution Approach 1:
The system replaces manual testing with automated testing tools that automatically execute test cases and verify application behavior. The automated tools perform the mechanical repetitive work of testing while maintaining accuracy, freeing developers from time-consuming manual testing oversight.
4Ease of operation
If virtual assistants are equipped with rudimentary natural language interpretation, then they can understand basic inputs, but they only know a small number of facts and talk about a limited range of subjects making them unrealistic
Solution Approach 1:
The system creates language recognition rules and language objects that can handle multiple subjects and topics through automated generation from diverse text samples. The generated rules are not limited to a single domain but can recognize and process various types of natural language inputs across different subjects, making the virtual assistant more versatile and realistic.
Data Source
AI summary
A system for supervised automatic code generation and tuning for natural language interaction applications, comprising a build environment comprising a developer user interface, automated coding tools, automated testing tools, and automated optimization tools, and an analytics framework software module. Text samples are imported into the build environment and automated clustering is performed to assign them to a plurality of input groups, each input group comprising a plurality of semantically related inputs. Language recognition rules are generated by automated coding tools. Automated testing tools carry out automated testing of language recognition rules and generate recommendations for tuning language recognition rules. The analytics framework performs analysis of interaction log files to identify problems in a candidate natural language interaction application. Optimizations to the candidate natural language interaction application are carried out and an optimized natural language interaction application is deployed into production and stored in the solution data repository.