Automated Service Recommendation System Using Multi-Source Data Segmentation
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Solution Overview
Problem
Existing systems fail to effectively capture and utilize user data, such as location, social network information, and device usage patterns, leading to missed opportunities for personalized service recommendations and commercial interactions.
Innovation Solution
A network-based system that collects and analyzes spatial, temporal, social, and topical data to identify relevant services for users, using agents to automatically recommend and enroll users in suitable services by matching their interests and behaviors with available services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user data is collected and analyzed to provide personalized service recommendations, then service recommendation accuracy and user experience are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex data collection and analysis process into distinct functional modules: data collection module, data processing module, service matching module, and recommendation module. Each module handles specific aspects of the data flow, making the overall system more manageable and maintainable while achieving high recommendation accuracy through coordinated operation of these specialized components
Solution Approach 2:
The patent introduces intermediary components such as data processing intermediaries that bridge raw data collection and service recommendation generation. These intermediaries pre-process and structure data before it reaches the recommendation engine, reducing the computational burden on the core recommendation system while maintaining high accuracy through systematic data preparation
2Productivity
If automated service recommendation systems are implemented, then commercial opportunities and user engagement increase, but information privacy and security risks increase
Solution Approach 1:
The system extracts and separates personally identifiable information from behavioral data, processing only the necessary behavioral patterns and preferences while excluding sensitive personal identifiers. This extraction approach allows the system to maintain high user engagement through personalized recommendations while minimizing privacy risks by removing unnecessary sensitive data from the processing pipeline
Solution Approach 2:
The patent implements feedback mechanisms that allow users to control their data sharing preferences and receive notifications about data usage. This feedback loop enables users to opt-in or opt-out of specific data collection activities, maintaining system productivity through user-approved personalization while reducing privacy risks through user-controlled data exposure
3Measurement precision
If comprehensive user data is captured including idle time and social network information, then service recommendation relevance is improved, but data collection complexity and storage requirements increase
Solution Approach 1:
The system performs preliminary data filtering and aggregation during the collection phase, pre-processing raw data into structured formats that capture essential user behaviors and preferences. This preliminary action reduces the volume of data that needs to be stored and processed later while maintaining the relevance of service recommendations through pre-computed user profiles and behavioral patterns
4Reliability
If manual service enrollment processes are used, then user control and accuracy are maintained, but time consumption and operational complexity increase
Solution Approach 1:
The patent implements self-service enrollment mechanisms where users can automatically enroll in recommended services through pre-configured preferences and authorization settings. The system automatically matches users with relevant services based on their profiles and enrolls them with minimal user intervention, maintaining reliability through user-approved preferences while dramatically reducing enrollment time through automation
Data Source
AI summary
A system and method for automated service recommendations. A request is received over a network, from a user for service recommendations, wherein the request comprises an identification of the user and at least one service selection criteria. A query is formulated so as to search, via the network, for user profile data, spatial data, temporal data, social data and topical data that is available via the network and relates to the requesting user, the service selection criteria and to a plurality of services available via the network so as to a identify a subset of the plurality of services available via the network that relate to the request. A list of the identified subset of services is transmitted, via the network, to the requesting user. A selection of at least one of the selected subset of services available received from the user and the user is enrolled in the selected service.