Assisted Automation Framework Synthesizing Interactive Programs
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Solution Overview
Problem
Existing program synthesis methods struggle to create interactive programs that can effectively respond to unpredictable environmental inputs, as they are typically non-interactive and require extensive data for learning, making them inefficient and limited in adaptability.
Innovation Solution
The development of an assisted automation framework that uses recordings to synthesize programs capable of interacting with environments, featuring a warping interpreter and human-as-an-interpreter, allowing for conditional guards, generalization of patterns, and iterative improvement through observations of new data and world states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional program synthesis methods are used, then programs can be generated from specifications, but the synthesized programs are non-interactive and cannot respond to unpredictable environmental inputs
Solution Approach 1:
The patent transforms static, non-interactive synthesized programs into dynamic interactive programs by introducing conditional guards that evaluate environment states at runtime. The warping interpreter continuously monitors environmental changes and dynamically adjusts program execution based on current conditions, enabling the program to adapt to unpredictable inputs while maintaining reliability through structured decision-making frameworks.
Solution Approach 2:
The patent implements feedback mechanisms where the warping interpreter evaluates environmental states and feeds this information back to the program execution engine. This feedback loop allows the synthesized program to respond to environmental changes by triggering appropriate actions based on conditional guards, thereby achieving interactivity and reliable response to unpredictable inputs through continuous environment-program interaction.
2Measurement precision
If extensive data is used for learning in program synthesis, then program accuracy can be improved, but the process becomes inefficient and requires large amounts of data
Solution Approach 1:
The patent applies preliminary action by pre-defining conditional guards and response patterns during program synthesis based on specification analysis, rather than requiring extensive training data. The warping interpreter is pre-configured with evaluation rules and action mappings that enable accurate responses to environmental inputs without needing to learn from large datasets, thereby improving data efficiency while maintaining program accuracy.
3Adaptability or versatility
If manual programming is used to create interactive programs, then adaptability to environment can be achieved, but the process is time-consuming and complex
Solution Approach 1:
The patent enables self-service by allowing the program synthesis system to automatically generate interactive programs with embedded conditional guards and warping interpreter configurations directly from specifications. The system synthesizes the complete interactive program structure, including environment monitoring logic and response actions, without requiring manual programming intervention, thereby achieving environmental adaptability while dramatically reducing program creation time.
4Adaptability or versatility
If synthesized programs are made interactive with conditional guards and interpreters, then environmental interaction is enabled, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the interactive program system into distinct modular components: the synthesized program logic, conditional guard evaluations, warping interpreter module, and environment interface. This segmentation allows each component to be independently developed, tested, and maintained, reducing overall system complexity while enabling sophisticated environmental interaction through coordinated operation of the modular elements.
Data Source
AI summary
Described herein are systems and methods for automatically building automations from desktop recordings using program synthesis. The problem of building automations for desktop applications can be lifted to a generalized concept of automations that operate on worlds whose “world state” can change asynchronously to the actions of the automation. Advantageously, in contrast with synthesis systems that take input-output demonstrations to synthesize a function that maps between them, the method presented here can synthesize from time-series traces of actions to automations that generalizes each step. The present disclosure describes ways to a) build assisted automations, b) synthesize them from recordings, c) running assisted automations using interpreters that observe the world state and adjust the actions accordingly, d) discovering them from an always-on recorder, e) suggesting them in partial progress from an always-on recorder, and lastly f) iteratively improving the assisted automation by recordings of every subsequent run.