Location-Aware Caller Identification with Privacy-Controlled Granularity
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Solution Overview
Problem
Current technologies do not provide user-configurable or contextually determined geographical location information for caller IDs, lacking privacy controls and dynamic display capabilities.
Innovation Solution
A computer-implemented method using a trained call annotation machine learning model to associate user locations with phone numbers based on annotating conditions and granularities, extracting location information from transaction data, and dynamically displaying user-specific location information in caller IDs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If location information is added to caller ID, then caller identification relevance is improved, but privacy control is worsened
Solution Approach 1:
The system applies different location granularities to different phone number records based on user preferences and context. Some records receive detailed location information while others receive only coarse-grained or no location data, allowing privacy protection for sensitive contacts while providing relevant location info for others.
Solution Approach 2:
The system dynamically determines whether to annotate location information for each phone number record based on real-time evaluation of annotating conditions. These conditions include user preferences, relationship context, and situational factors, allowing the privacy protection level to adapt dynamically rather than being static.
2Productivity
If location information is displayed for all callers, then caller identification efficiency is improved, but user configurability is worsened
Solution Approach 1:
The system segments phone number records into different categories based on user-defined preferences and contextual factors. Each segment can have different location annotation settings, allowing users to configure privacy levels for different contact groups (e.g., family, work, strangers) while maintaining efficient identification for all.
Solution Approach 2:
The system changes the parameter of location granularity for different phone number records based on user preferences and context. Users can configure different granularity levels (e.g., city, neighborhood, street) for different contacts, and the system automatically adjusts the displayed location information accordingly.
3Quantity of substance
If machine learning model annotates all phone numbers with location data, then data completeness is improved, but processing complexity is worsened
Solution Approach 1:
The system applies partial annotation by evaluating annotating conditions for each phone number record and only annotating those that meet the criteria. This avoids the excessive complexity of processing and annotating all phone numbers uniformly, while still achieving sufficient location data completeness for relevant contacts.
Data Source
AI summary
Systems and methods of location-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: utilizing a trained call annotation machine learning model to determine one or both of an annotating condition and annotating location granularity, and associate one location of a user with one phone number of the user based at least on one or both of the annotating condition and the annotating location granularity; receiving second transactional information of one transaction associated with a first user; extracting second location information from the second transactional information of the one transaction; utilizing the trained call annotation machine learning model to automatically annotate one phone number record of one phone number of the first user, with the second location information at the annotating location granularity to form at least one user-specific location-specific annotated phone number record.