Virtual Assistant Skill Framework Cross-Platform Protocol Conversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing virtual assistant platforms are not cross-compatible, requiring developers to create separate skills for each platform, leading to time-consuming maintenance and divergent behavior, which complicates usability, documentation, and performance.
Innovation Solution
A framework that allows authors to create platform-agnostic specifications for virtual assistant services, using an execution engine and wrapper services to interpret and convert protocols, enabling a single skill to be authored and executed across multiple platforms without knowing platform-specific code.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate skills are created for each virtual assistant platform, then platform-specific functionality is achieved, but maintenance complexity and time consumption increase significantly
Solution Approach 1:
The patent implements a universal skill specification format that can be interpreted by multiple virtual assistant platforms. The skill definition uses platform-agnostic data structures and protocols, allowing a single skill to be deployed across Amazon Alexa, Google Assistant, and other platforms without creating separate versions. This universality principle directly resolves the contradiction by enabling broad platform compatibility while eliminating the need to maintain multiple separate skill implementations.
Solution Approach 2:
The patent introduces an intermediary layer consisting of protocol converters and adaptation services that translate between the universal skill specification and platform-specific implementations. This intermediary handles the conversion of commands, responses, and data formats, allowing the core skill logic to remain platform-independent while adapting to specific platform requirements. This mediator approach enables platform versatility without increasing maintenance complexity.
2Productivity
If separate skills are created for each platform, then platform-specific optimization is possible, but behavioral consistency across platforms deteriorates
Solution Approach 1:
By using a universal skill specification format with standardized data structures, the patent ensures that the same skill logic is executed across all platforms. The consistent interpretation of skill definitions through a common framework guarantees behavioral uniformity, while the platform-agnostic nature allows efficient deployment to multiple platforms simultaneously without compromising consistency.
3Adaptability or versatility
If platform-specific code is required for each virtual assistant platform, then platform-specific features can be optimized, but the skill authoring process becomes more complex
Solution Approach 1:
The patent employs a universal skill authoring framework where developers create skills using platform-independent specifications. The system automatically handles platform-specific adaptations through protocol converters, eliminating the need for developers to write platform-specific code. This approach maintains broad platform coverage while significantly simplifying the skill authoring process.
Solution Approach 2:
The intermediary adaptation layer automatically translates universal skill specifications into platform-specific implementations, shielding developers from platform complexity. This mediator handles all platform-specific code generation and adaptation, allowing authors to focus solely on creating the core skill logic without worrying about platform differences.
4Reliability
If multiple separate skills are maintained for different platforms, then each platform can have optimized performance, but update and expansion efforts must be replicated across all platforms
Solution Approach 1:
The universal skill specification approach allows a single source of truth for skill logic that is automatically deployed to multiple platforms. When updates or expansions are needed, the changes are made once in the universal specification and automatically propagated to all supported platforms, eliminating the need to replicate update efforts across multiple separate skill implementations while maintaining platform-specific reliability through consistent interpretation.
Data Source
AI summary
Techniques for exposing virtual assistant skills as authorable content items interpretable across multiple platforms are disclosed. In an embodiment, a query system comprises an execution engine and a plurality of wrapper services including (a) a first wrapper service for converting messages between a first protocol of a first virtual assistant platform and a target protocol and (b) a second wrapper service for converting messages between a second protocol of a second virtual assistant platform and the target protocol. The query system may receive a specification for a skill and requests from the plurality of virtual assistant platforms that invoke the skill. The wrapper services convert the requests to a target protocol consumable by the execution engine and convert responses from the execution engine a protocol consumable by the virtual assistant platform through which the request originated.