Automated Resource Allocation Inference System
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Solution Overview
Problem
Conventional software engineering approaches lack a holistic view of productivity measurement and resource allocation, relying on incomplete data sets and requiring explicit user participation, which leads to inaccurate and incomplete data collection and reporting.
Innovation Solution
A system utilizing intelligent algorithms to map existing information from various data sources into container data objects, generating inference information for resource allocation, providing an aggregate view of how developers spend their time, and dynamically adjusting allocations based on additional data, eliminating the need for explicit user participation and manual tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit user participation and manual tracking are required for data collection, then data accuracy can be improved, but user burden and system complexity increase significantly
Solution Approach 1:
The system automatically captures operational information from existing software tools and data sources without requiring user intervention. The intelligent algorithms self-service by autonomously collecting, processing, and analyzing data from version control systems, ticketing systems, and communication platforms, eliminating manual tracking while maintaining data accuracy through automated inference modeling.
Solution Approach 2:
The patent replaces manual mechanical tracking processes with automated intelligent algorithms. Instead of users manually recording time and tasks, the system uses machine learning models to infer work allocations from digital footprints left in existing tools, substituting human effort with automated computational analysis.
2Ease of operation
If incomplete data sets are used for productivity measurement, then data collection burden is reduced, but measurement accuracy deteriorates
Solution Approach 1:
The system implements feedback loops where initial incomplete data collections are continuously refined. The intelligent algorithms analyze captured operational information, identify gaps, and automatically supplement incomplete data by cross-referencing multiple data sources and applying inference models, progressively improving measurement accuracy without increasing user burden.
Solution Approach 2:
The patent combines multiple disparate data sources (version control logs, ticketing system data, communication platform records) into a composite data structure. By integrating information from these diverse sources and applying intelligent algorithms, the system creates a complete productivity measurement picture from inherently incomplete individual sources.
3Device complexity
If conventional tracking tools are used, then implementation simplicity is maintained, but holistic productivity insight is lost
Solution Approach 1:
The system merges data from multiple existing conventional tools (version control systems, ticketing systems, communication platforms) into a unified analysis framework. By combining these separate information sources and applying intelligent algorithms, the system recovers holistic productivity insights that would be lost if any single tool were used in isolation, while maintaining compatibility with existing simple toolchains.
4Quantity of substance
If manual time allocation tracking is implemented, then detailed resource allocation data is obtained, but user resistance and data quality issues increase
Solution Approach 1:
The system performs self-service data collection by automatically capturing operational information from existing software tools without requiring user participation. This eliminates user resistance and the associated data quality problems (underreporting, inaccurate reporting) while still obtaining detailed resource allocation data through automated inference from digital footprints.
Data Source
AI summary
Systems, methods, and storage media for optimizing automated modelling of resource allocation are disclosed. Exemplary implementations include operations for: receive or retrieve by a computer system, operational information associated with a plurality of users; allocate, by the computer system, at least a first time portion to at least a first task associated with a first user of the plurality of users based on analysis of the operational information; and dynamically modify, by the computer system, the first time portion responsive to receiving or retrieving additional operational information over time.