Contextual Voice Assistant for Dynamic Application Selection
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Solution Overview
Problem
Existing productivity applications often overwhelm users with complex features, leading to underutilization due to the lack of dynamic assistance in identifying contextually relevant applications for specific tasks, resulting in suboptimal user experiences.
Innovation Solution
A system that interprets user input signals, such as voice or text, to identify user intent and recommend or automate tasks, dynamically selecting the most suitable application based on contextual data and historical activity, using machine learning to rank recommendations and populate application fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If productivity applications add more specialized features to enable users to perform various tasks, then the functionality and versatility of the application improve, but the complexity of the application increases making it harder for users to know how to perform particular tasks
Solution Approach 1:
The patent introduces an intermediary system (voice-activated service) that mediates between the user and the complex application features. This intermediary interprets natural language commands and translates them into appropriate application actions, shielding users from the underlying complexity while maintaining access to diverse functionalities across multiple applications.
Solution Approach 2:
The voice-activated service acts as a universal interface that can handle various tasks across different applications (messaging, calls, emails, documents) through a single unified command structure. This allows users to access multiple application functionalities through one consistent interaction mode rather than learning each application's specific interfaces.
2Ease of operation
If existing voice-activated services are implemented to assist users with tasks, then ease of operation improves for specific tasks, but adaptability deteriorates because the service is static and only allows use of a single application for a particular task
Solution Approach 1:
The patent transforms the static voice-activated service into a dynamic system that can adapt its behavior based on context. The service now analyzes user input signals, identifies intent, and dynamically selects the most appropriate application from multiple options based on the specific task requirements, rather than being fixed to a single application.
Solution Approach 2:
The system changes the parameter of application selection from a fixed state to a variable state that responds to contextual factors. By processing contextual data and historical activity patterns, the service dynamically adjusts which application is selected for a given task, allowing the same voice command to potentially trigger different applications depending on the situation.
3Device complexity
If a static voice service is used that only allows a single application for a task, then device complexity is reduced, but productivity deteriorates due to underutilization of available applications and features
Solution Approach 1:
The system implements self-service by automatically analyzing user needs and selecting the most appropriate application without requiring user intervention. The voice-activated service processes contextual data and historical patterns to autonomously determine which application will best serve the user's intent, eliminating the need for users to manually navigate through multiple applications.
Solution Approach 2:
The system incorporates feedback mechanisms by processing historical activity data and contextual information to continuously improve its application selection accuracy. By analyzing past user behavior patterns and current contextual factors, the service learns and adapts to provide increasingly accurate recommendations, thereby improving productivity while maintaining system simplicity.
Data Source
AI summary
A system for assisting users in making progress towards the completion of a task by recommending actions is provided. The techniques disclosed herein enable a system to receive user input signals, such as a voice input, a text input or any other type of input comprising phrases indicating a task. The system can then automatically generate recommendations for directing the user to complete the task. The techniques disclosed herein can also identify a contextually-relevant application that is most suitable for completing the identified task. For instance, a user may have several messaging applications. Based on a context of the user's input, and other historical data utilized by one or more machine learning engines, a system can recommend an appropriate application, or provide a ranking of the applications, suitable for completing an identified task. The techniques disclosed herein also employ machine learning engines to improve accuracy of the recommendations over time.