Application Resource Usage Information System
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Solution Overview
Problem
Existing systems fail to effectively convey application-level usage information to users, particularly in data plans with usage constraints, making it difficult for users to manage data consumption efficiently.
Innovation Solution
A processing system that provides personal and expected usage information to users by collecting actual usage data from user devices and processing it to generate detailed reports, including data consumption patterns, allowing users to make informed decisions about application usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a processing system collects and processes actual usage data from user devices to provide application-level usage information, then measurement precision and information accuracy are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a processing system as an intermediary between user devices and users. This intermediary collects usage data from devices, processes it to generate application-level usage information, and presents it to users. The processing system acts as a mediator that centralizes data collection and processing functions, reducing the complexity burden on individual devices while maintaining high measurement precision through aggregated data analysis.
Solution Approach 2:
The system creates copies of usage data by collecting actual usage information from multiple user devices and generating virtual representations of usage patterns. These copies are processed to create expected usage information and personalized reports without requiring direct modification of the original device data, thereby maintaining accuracy while managing processing complexity through data replication and virtual analysis.
2Ease of operation
If the system provides detailed application-level usage information to help users manage data consumption, then ease of operation and user control are improved, but loss of information and data processing requirements increase
Solution Approach 1:
The patent extracts key usage information from detailed device data by collecting actual usage data and processing it to generate summarized application-level reports. The system takes out essential metrics such as data consumption by application, expected usage predictions, and personalized insights, separating these high-value summaries from the raw data stream. This extraction enables ease of operation for users through digestible information while managing processing requirements by focusing computation on generating actionable summaries rather than processing every raw data point.
Solution Approach 2:
The system performs preliminary processing of usage data by collecting and analyzing actual usage patterns before presenting information to users. It pre-processes the data to generate expected usage information and personalized reports in advance, allowing users to access ready-to-use insights without experiencing the full complexity of raw data processing. This preliminary action reduces the information loss burden by pre-computing meaningful summaries that users can easily interpret and act upon.
3Productivity
If the system collects actual usage data from multiple user devices to generate expected usage information, then productivity and data accuracy are improved, but use of energy and processing resources increase
Solution Approach 1:
The processing system performs multiple functions using the same collected usage data: it generates personal usage information for individual users, creates expected usage information for applications, produces aggregated statistics, and delivers personalized recommendations. By making the data collection and processing infrastructure universal and multi-functional, the system improves productivity through data reuse and reduces energy consumption by avoiding redundant processing operations for different purposes.
Solution Approach 2:
The system optimizes processing energy consumption by dynamically adjusting data collection and processing parameters based on usage patterns and device capabilities. It changes processing intensity, data sampling rates, and analysis depth according to current conditions, allowing high productivity during efficient processing windows while reducing energy consumption during peak demands. This parameter adaptation enables the system to maintain high data accuracy and productivity while managing energy resources effectively.
Data Source
AI summary
An environment is described in which a processing system provides application-level usage information to users. In one scenario, for example, the processing system may provide personal usage information to a user who is operating a user device. The personal usage information itemizes the amount of data (and/or other resources) that has been consumed by each application run by the user device. In another scenario, the processing system may provide expected usage information associated with at least one candidate application provided by a marketplace system. The expected usage information describes an expected consumption of data (and/or other resources) by the candidate application upon running the candidate application by the user device. The processing system can tailor the expected usage information that it sends to a particular user based on user profile data. The user profile data describes a manner in which users operate applications.