Universal API Data Structure for Low-Latency Mass Pricing

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Solution Overview

Problem

Existing data structure designs fail to efficiently handle the wide variety of data types and formats required for complex commercial consumer offerings, leading to increased latency and inefficiencies in distributed processing architectures, particularly in microservices environments, which hinder the timely presentation of vehicle pricing alternatives within a one-second timeframe.

Innovation Solution

A flexible data structure that accommodates various data types and formats, allowing for parallel execution of computations and minimizes data duplication, while maintaining a fixed format during information exchange, and utilizes a microservices approach with four separate threads for mass pricing calculations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a unique data structure design is employed for each different type of commercial consumer data, then the data structure can support the wide range of data needs, but the ability to readily support and scale requests simultaneously is precluded

Engineering Contradiction:

Improvedata structure adaptabilityVSAvoidrequest processing throughput

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent implements a universal data structure design that can accommodate multiple types of commercial consumer data through a single standardized format. This universal structure enables the system to handle diverse data requirements (vehicle information, pricing scenarios, consumer profiles) without requiring separate data structures for each data type, thereby maintaining high adaptability while enabling simultaneous processing of multiple requests through standardized serialization and deserialization mechanisms.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If data structures are designed to support elaborate data for complex commercial consumer offering requests, then the data can capture rich financial scenarios, but the processing time increases beyond the one-second timeframe

Engineering Contradiction:

Improvedata structure flexibilityVSAvoidprocessing latency

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent segments the data structure into distinct fields and types, organizing complex financial scenario data into modular components. This segmentation allows the system to efficiently serialize and deserialize only the necessary data portions, reducing processing time while maintaining the ability to capture elaborate financial scenarios. The segmented approach enables parallel processing and reduces the overhead of handling complex data structures.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent employs parameter changes through standardized data type definitions and serialization formats. By establishing fixed parameter mappings and type conversions, the system can rapidly transform and process complex data without repeated parsing operations. This parameter standardization maintains data flexibility for complex scenarios while significantly reducing processing latency within the one-second timeframe.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If distributed processing architecture is used to meet technical performance demands, then parallel computation can be enabled, but data transfer overhead increases

Engineering Contradiction:

Improvecomputation parallelizationVSAvoiddata transfer overhead

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The patent merges multiple data structures into a single unified format that can be efficiently transmitted and processed across distributed systems. By combining diverse data types into a standardized serialized structure, the system reduces the number of separate data transfer operations and minimizes overhead. This merged approach enables parallel computation across distributed nodes while reducing the total data transfer volume and associated overhead.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS12541775B2Unique method of processing API data supporting wide variety of data types and multiple/singular formats without data duplication

Publication Date: 2026.02.03 CAPITAL ONE SERVICES LLC

AI summary

A computer-implemented approach is described that includes receiving a mass price computation request for a plurality of deals involving a plurality of vehicles. The request includes a data structure configured to store input values, and a tri-value field configured to store a tri-value, the tri-value being one of a first value, a second value, or a third value, the first value indicating a single computation is requested using the input values, the second value indicating a limited set of combinatorial calculations to be calculated using the input values, and the third value indicating a full set of combinatorial calculations using the input values. The approach further causes computing of each of the plurality of deals based on the data structure, and outputting results to a user device based on the mass price computation request.