Flow Engine for Dynamic AI Health Support
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Solution Overview
Problem
Current artificial intelligence health support systems lack flexibility and efficiency in providing specific, targeted treatments and tracking progress, often requiring significant effort to configure and maintain, which can lead to perceived incompetence and incoherent conversations.
Innovation Solution
The system employs a flow engine to call conversational services, allowing for different instructions to be executed based on configured programs and goals, and includes features that activate specific instructions or repeat tasks based on priority thresholds, thereby enhancing flexibility and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI health support systems use fixed, pre-programmed responses, then implementation is simple, but flexibility and adaptability to individual user needs deteriorate
Solution Approach 1:
The system employs dynamic instruction execution where the flow engine selectively executes instructions based on real-time conditions, user progress, and priority thresholds. This allows the AI health support system to adapt its behavior dynamically rather than following fixed pre-programmed responses, resolving the contradiction between flexibility and complexity by making the system's operational logic adaptable rather than rigid.
Solution Approach 2:
The system changes operational parameters such as instruction execution, priority thresholds, and feature activation based on user progress and conditions. By adjusting these parameters dynamically, the system achieves versatility in providing targeted treatments without requiring complete reconfiguration, thus improving adaptability while managing complexity through parameter-based control.
2Reliability
If the system executes all instructions repetitively, then thoroughness is improved, but efficiency and time consumption worsen
Solution Approach 1:
The system incorporates feedback mechanisms where the flow engine monitors user progress, priority thresholds, and instruction completion status. Based on this feedback, the system determines whether to execute instructions repetitively or move to the next instruction, balancing thoroughness with efficiency by avoiding redundant executions when priority thresholds are met or user progress indicates completion.
Solution Approach 2:
The system implements periodic evaluation of priority thresholds and instruction priorities rather than continuous repetitive execution. This allows the system to maintain thoroughness by periodically checking conditions while improving efficiency by avoiding unnecessary repetitive actions, transitioning from continuous to intermittent evaluation based on actual system state.
3Measurement precision
If the system tracks detailed user progress, then measurement precision is improved, but data management complexity worsens
Solution Approach 1:
The system segments user progress tracking into distinct data structures and tracking mechanisms organized by program, goal, and instruction type. This segmentation allows detailed progress measurement for each component while managing overall data complexity through structured organization, making the tracking system scalable and maintainable despite the precision required.
Data Source
AI summary
A system provides artificial intelligence health support for people. The system renders specific, targeted treatments for people by using a flow engine and a conversational service to call one or more conversational modules. The treatments for the people may be tracked. The flow engine and/or one or more of the modules may include different instructions to perform for different programs and/or goals that have been configured. The flow engine and/or one or more of the conversational modules may also include instructions to perform when certain features are active (which may be activated when certain programs and/or goals are configured), when data regarding activity for people are received, and so on. Other modules may be dedicated to particular programs and/or goals. Some modules may determine whether or not to perform various instructions repetitiously, and/or may determine to do so when a priority of a previous instruction is below a threshold.