Weighted Processing Network for Audio User Profile Generation
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Solution Overview
Problem
Current methods for managing user data from audio streams are inadequate in identifying user characteristics and preferences, especially for unknown users, as cookie data is limited and does not provide a comprehensive picture of user behavior, making it difficult to build accurate user profiles for targeted advertising and content customization.
Innovation Solution
A data management apparatus and method using a weighted processing network, trained with user attribute and behavioral characteristic data, to generate personalized user profiles for both known and unknown users, allowing for targeted advertising and optimized resource allocation in media content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cookie data is collected from users to identify user characteristics, then user identification capability is improved, but the completeness of user preference data deteriorates because cookie data is limited to previously linked information
Solution Approach 1:
The patent introduces an intermediary machine learning model that mediates between limited cookie data and comprehensive user preference profiles. The model takes cookie data as input and generates inferred preference data as output, acting as a bridge that transforms incomplete information into complete user profiles without directly collecting all preference data through cookies
Solution Approach 2:
The patent creates a copied or simulated version of complete user preference data by training a machine learning model on available cookie data. The model learns to replicate the patterns and relationships that would exist in complete preference data, generating synthetic preference information that mirrors real user preferences without requiring actual collection of all preference details
2Measurement precision
If data is collected from authorized users to understand their behavior, then security system accuracy is improved, but the ability to identify and characterize unknown users deteriorates due to lack of baseline data
Solution Approach 1:
The patent performs preliminary action by collecting and analyzing data from authorized users before unknown users need to be identified. The machine learning model is trained in advance on authorized user behavior patterns, creating a knowledge base of legitimate user characteristics that can be applied when unknown users are encountered, enabling proactive preparation rather than reactive analysis
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously learns from authorized user behavior and updates its understanding of normal user patterns. This feedback loop allows the system to refine its model of authorized user characteristics, improving its ability to distinguish authorized from unauthorized users over time while maintaining adaptability to new user types
3Productivity
If comprehensive user data is collected to build detailed user profiles, then targeted advertising effectiveness is improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The patent extracts only the essential features and patterns from user data that are necessary for effective targeted advertising, rather than processing all available user information. The machine learning model identifies and extracts key predictive features from cookie data that correlate with user preferences, discarding redundant information and focusing computational resources on the most impactful data elements
Solution Approach 2:
The patent changes the parameters of data representation by transforming raw cookie data into derived features and metrics that are more suitable for advertising targeting. Instead of working with raw, unprocessed user data, the system transforms the data into meaningful parameters such as inferred preferences, behavioral patterns, and demographic characteristics that directly inform advertising decisions while reducing processing complexity
Data Source
AI summary
A data management apparatus, a method, and a computer program product for establishing personal characterisations of users. A first set of data representing, for each user of a group of users, one or more categories of user attribute data is received. The group of users includes a first and second groups of users, where the first and second groups have no users in common. A second set of data representing, for each user in the first group, one or more behavioural characteristics is received. A weighted processing network is trained to form, for each user in the first group, relationships between categories of user attribute data of the first set of data and behavioural characteristics of the second set of data. A third set of data representing, for each user in the second group, behavioural characteristic(s) present in the second set of data is generated using the formed relationships.