Contextual Skills Discovery for Audio Devices
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Solution Overview
Problem
Users of digital personal assistants on audio devices without screens face challenges in understanding available features and functionalities, often resorting to a 'guess and try' interaction style due to lack of intuitive awareness.
Innovation Solution
An automated system using a machine learning algorithm to create a contextual skills model that aggregates user and context data to recommend relevant skills based on current user contexts, eliminating the need for manual searching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If digital personal assistant capabilities are expanded to provide more functionalities, then the versatility and usefulness of the device improves, but users become less aware of available features and must resort to guess-and-try interaction
Solution Approach 1:
The system performs preliminary action by proactively analyzing user context (location, time, activity, device state) and predicting which skills the user will need before the user even asks. The contextual skills system generates skill recommendations in advance based on aggregated data from multiple users and machine learning patterns, presenting relevant capabilities before they are requested, thus eliminating the need for users to guess or try random commands.
2Ease of operation
If skill recommendations are provided to users, then user experience and skill discovery improve, but device processing power and bandwidth consumption increase
Solution Approach 1:
The system applies partial action by providing only the most relevant skill recommendations rather than presenting all available skills. The contextual skills model ranks skills based on predicted relevance to the user's current context and only presents the top recommendations, avoiding the processing overhead of analyzing and presenting every possible skill. This selective approach maintains user experience while conserving device resources.
Solution Approach 2:
The system uses copying by leveraging patterns learned from aggregated data across multiple users. Instead of requiring each individual device to learn and process all skill-context relationships independently, the contextual skills system creates a shared model from collective user behavior data, then applies this copied knowledge to generate recommendations for individual users, significantly reducing per-device processing requirements.
3Adaptability or versatility
If contextual data aggregation and machine learning analysis are performed locally, then personalized skill recommendations improve, but device memory and computational resources are consumed
Solution Approach 1:
The system introduces an intermediary layer (the contextual skills system and model) that handles the complex data aggregation, processing, and pattern recognition tasks. Rather than requiring each device to maintain and process large datasets locally, the intermediary contextual skills system performs these functions centrally or in the cloud, then provides simplified recommendations to individual devices. This mediator approach enables personalized recommendations without burdening individual device memory and computational resources.
Data Source
AI summary
Recommending one or more relevant skills to a user that are appropriate for the user's current context is provided. A machine learning contextual skills model is trained on data associated with skills that are invoked by the particular user and by various users and context data that defines characteristics of the context of the users, the users' computing devices, and the users' environments in association with the invoked skills. Responsive to a trigger event associated with the user and a computing device, current context data and user data is collected and passed to the contextual skills system. The contextual skills model is applied to the collected data and generates a ranked list of skills. One or more top-ranking skills are selected and provided to the user as one or more skill recommendations that are determined to be relevant to the user and relevant to the user's current context.