Relationship-Based Application Content for AI-Personalized Interactions
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Solution Overview
Problem
Existing personal device technologies lack a means to efficiently manage and integrate resources and interactions, leading to overwhelming information and inefficient relationship management.
Innovation Solution
A method utilizing an AI engine to generate customized application data based on user relationships, including communication, location, and chronotype data, to enhance relationship reinforcement between users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personal device technology advances to enable more functionality, then user capability is improved, but information overload increases
Solution Approach 1:
The patent extracts and prioritizes relationship data from the overwhelming amount of available information. By identifying and focusing on relationship-specific data (communication patterns, location history, interaction frequency), the system filters out irrelevant information and presents only what is necessary for relationship management, thus reducing information overload while maintaining user capability.
Solution Approach 2:
The patent applies local quality by customizing the information presentation to match the specific relationship context. Different relationships (friends, family, colleagues) receive different types of information and recommendations based on their unique characteristics. This localized approach ensures that each user receives personalized information relevant to their specific relationship needs, reducing general information overload.
2Adaptability or versatility
If relationship management features are added to personal devices, then relationship management capability is improved, but device complexity increases
Solution Approach 1:
The patent implements universality by designing a relationship management system that can handle multiple relationship types and scenarios through a single integrated framework. The same core system processes different relationship data (communication, location, interaction) and generates appropriate recommendations for various contexts, eliminating the need for separate complex systems for each relationship type.
Solution Approach 2:
The patent applies self-service by enabling the system to automatically analyze relationship data and generate management recommendations without requiring manual user input or complex configuration. The AI engine autonomously processes communication patterns, location history, and interaction data to produce personalized relationship management advice, reducing the operational complexity for users.
3Ease of operation
If AI-based personalized content is generated, then user experience is improved, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing relationship data in an optimized format before it is needed for generating recommendations. The system continuously updates relationship profiles and pre-calculates potential recommendations, so when a user interacts with the system, the AI engine can quickly retrieve and process pre-organized data rather than analyzing raw data in real-time, thus reducing processing time while maintaining personalized user experience.
Data Source
AI summary
A method of determining application content based on user relationships may include receiving first application data that indicates how a first user has used an application installed on the first user's device. The method may include receiving relationship data that indicates a relationship between the first user and a second user and providing the first application data and the relationship data to an AI engine. The AI engine may be trained to generate customized application data based on an activity of the first user and appropriate for the first user's relationship with the second user. The customized application data may enable the second user to improve or reinforce the relationship. The method may include generating the customized application data by the AI engine and transmitting it to the second user's device.