Empathy-Based Machine Learning for Personalized IoT Insights
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Solution Overview
Problem
Machine learning approaches often fail to consider empathetic situational aspects of individuals, leading to insights that may have limited uptake or acceptance, particularly in IoT ecosystems where accuracy and convenience are crucial.
Innovation Solution
A computational approach that integrates empathy-based machine learning by using neural networks to incorporate empathy-driven factors into decision-making processes, such as notification timing and graphical user interface presentation, to provide more relevant and personalized insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning approaches are used for generating insights, then productivity and speed of decision-making are improved, but the relevance and acceptance by individuals deteriorates due to lack of empathetic considerations
Solution Approach 1:
The patent merges traditional machine learning models with empathy-based computational models into a unified hybrid system. The machine learning component processes data for speed and pattern recognition, while the empathy component adds emotional and contextual understanding. Both components work together to generate insights that are both efficient and personally relevant, resolving the contradiction between productivity and reliability.
Solution Approach 2:
The empathy-based computational model acts as an intermediary layer between raw data processing and final insight generation. It translates emotional and contextual factors into computational signals that modulate the output of machine learning models, ensuring that insights are not only fast to generate but also aligned with individual emotional states and preferences.
2Reliability
If empathy-based machine learning models are implemented, then relevance and affinity for users are improved, but device complexity and infrastructure requirements worsen
Solution Approach 1:
The system is segmented into distinct functional modules: data collection modules, machine learning processing modules, empathy computation modules, and output generation modules. Each module has a specific function and can be independently optimized or scaled. This segmentation reduces overall system complexity by making each component manageable and allowing parallel development and deployment.
Solution Approach 2:
The empathy-based computational framework is designed as a universal layer that can be applied across multiple domains and applications (e.g., customer service, healthcare, education). By creating a multi-functional empathy engine that handles various emotional contexts and user types, the system reduces the need for separate complex infrastructure for each specific application.
3Reliability
If continuous empathy-based modeling computation is performed, then real-time personalized insights are improved, but computational burden and processing time worsen
Solution Approach 1:
Instead of continuous computation, the system employs periodic updates of empathy models at strategically determined intervals. The computation is triggered by significant events or changes in user state rather than running continuously, reducing computational burden while maintaining real-time responsiveness when needed. This periodic action allows the system to balance personalization quality with energy efficiency.
Solution Approach 2:
The system performs preliminary computation of empathy model components in advance, pre-processing emotional and contextual data when computational resources are available. These pre-computed results are stored and quickly retrieved during real-time operations, reducing the computational burden during critical decision-making moments while maintaining personalized insight quality.
Data Source
AI summary
A computing system configured to generate empathy-based machine-learning outputs, which, for example, can include notifications, automatic service delivery, payments, among others. The system receives as inputs a first set of data sets representative of historical behaviour through tracked interactions, a second set of data sets representative of circumstantial knowledge (e.g., environmental factors, such as weather), and a set of empathy model weights from one or more machine learning models that are configured to model one or more empathy consideration components (e.g., curiosity, preconceptions, inspirations, direct experiences, listened experiences, imagination, among others). Corresponding methods and non-transitory computer readable media are contemplated.