Tax Return Calculation Job Scheduler
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Solution Overview
Problem
Conventional network-based return preparation programs face challenges in processing large consolidation-type tax returns and high volumes of individual tax returns, requiring significant computing resources and struggling with scalability and dependency management, leading to potential failures and prolonged processing times.
Innovation Solution
A return calculation mechanism that distributes tax calculation jobs among multiple nodes using a job scheduler, which decomposes calculations into units of work, prioritizes based on complexity and dependencies, and provides load balancing, scalability, and failover capabilities, allowing for efficient allocation and processing of tax returns across available resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional network-based return preparation programs process large consolidation-type returns or high volumes of individual returns, then the system requires large amounts of computing resources, but the system experiences processing failures and prolonged processing times
Solution Approach 1:
The patent segments the return preparation processing into discrete, independent tasks that can be distributed across multiple server nodes. Each task represents a unit of work that can be executed independently, allowing the system to process large volumes of returns without overwhelming any single server. This segmentation enables parallel processing while maintaining system reliability through distributed execution.
Solution Approach 2:
The system dynamically allocates processing tasks to available server nodes based on current system state and resource availability. The job scheduler continuously monitors server status and dynamically assigns tasks, allowing the system to adapt to changing loads and maintain both high productivity and reliability under varying conditions.
2Productivity
If conventional network-based return preparation programs use a single server for processing, then the system structure is simple, but the system lacks scalability and cannot handle large volumes of returns efficiently
Solution Approach 1:
The patent creates a universal task execution environment where server nodes can handle multiple types of return preparation tasks through a common interface and standardized task definitions. This multi-functionality allows the same infrastructure to process individual returns, consolidation returns, and various tax forms without requiring separate specialized systems, thereby increasing processing capacity while controlling complexity through standardization.
Solution Approach 2:
The job scheduler acts as an intermediary between the client interface and the distributed server nodes. It manages the complexity of task distribution, server monitoring, and result aggregation, shielding users from the underlying system complexity while enabling scalable processing across multiple servers.
3Measurement precision
If the system processes complex consolidation-type returns with dependencies between lower level returns, then the calculation accuracy is maintained, but the processing time increases significantly
Solution Approach 1:
The system performs preliminary processing of lower-level individual returns before they are needed for consolidation. The job scheduler identifies dependency relationships and executes prerequisite calculations in advance, so that when consolidation returns need to be processed, the underlying data is already available. This preliminary action maintains calculation accuracy while significantly reducing processing time for complex consolidations.
Solution Approach 2:
The system maintains continuous processing by executing independent tasks concurrently while respecting dependency relationships. Rather than waiting for complete return sets before beginning processing, the system continuously executes available tasks in parallel, maintaining useful action throughout the processing pipeline and reducing overall processing time while preserving calculation accuracy through proper dependency management.
Data Source
AI summary
Method and apparatus for return processing, including tax returns. A return calculation mechanism may partition out return preparation jobs to a plurality of return calculation nodes. A job scheduler may receive returns and distribute the returns among the calculation nodes according to a load-balancing scheme. The job scheduler may decompose a return into two or more calculation jobs. A job may be an individual return or portion of a return. In one embodiment, the calculation jobs may be prioritized according to dependency. If one job depends on results of another job, the job may be scheduled to execute after the job on which it depends. The calculation nodes may cache calculated data from jobs, and a job that depends on another job may be assigned to a node that caches data from the job on which it depends.