Request Prioritization via Temporal Contextual Vector Analysis

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Solution Overview

Problem

Current task prioritization and digital processing systems are not configured to reliably and efficiently determine the prioritization of requests from clients based on their different aspects of priorities, leading to missed deadlines and neglected requests.

Innovation Solution

A system and method that analyze requests using temporal and contextual vector analysis, extracting features such as request age, deadline, client metadata, and sentiment to assign weight values and generate a unified numerical representation of requests, prioritizing them based on these features.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If static prioritization is used for requests in a queue, then the system is simple to operate, but deadlines are missed and requests are neglected

Engineering Contradiction:

Improvedeadline completionVSAvoidprioritization system complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent implements dynamic prioritization by continuously analyzing temporal features (request age, time until deadline) and contextual features (client importance, request urgency) to adjust priority scores in real-time. This allows the system to adapt to changing conditions and ensure deadlines are met without requiring overly complex manual intervention

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent replaces manual static prioritization with an automated machine learning-based prioritization system that processes temporal and contextual features to generate priority scores. This substitution of mechanical/manual processes with intelligent automation improves reliability while keeping the system manageable through algorithmic decision-making

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Adaptability or versatility

If all requests are processed in queue order, then the processing system is simple, but important requests are neglected and client relationships deteriorate

Engineering Contradiction:

Improverequest priority adaptationVSAvoidprocessing system complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies different weighting factors to different features based on their local importance to each specific request type and client. Temporal features like request age and deadline proximity receive different weights than contextual features like client tier and request urgency, allowing customized prioritization for different situations while maintaining a unified processing framework

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The patent changes the parameter of request prioritization from fixed queue position to dynamic priority scores based on multiple features. By transforming the processing parameter from simple FIFO (first-in-first-out) ordering to multi-dimensional feature-based scoring, the system becomes adaptable to different request priorities without requiring completely different processing mechanisms

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If detailed analysis of each request is performed, then prioritization accuracy is improved, but processing time increases

Engineering Contradiction:

Improvepriority determination accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary extraction and normalization of temporal and contextual features from incoming requests before priority calculation. By preparing feature data in advance and organizing it into standardized formats, the system reduces the computational burden during priority scoring, achieving accurate prioritization without excessive processing delays

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the prioritization process into distinct stages: feature extraction, feature normalization, priority score calculation, and request ranking. This segmentation allows each stage to be optimized independently, with feature extraction handling data preparation and the scoring stage focusing on accurate priority determination, reducing overall processing time while maintaining precision

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11570176B2System and method for prioritization of text requests in a queue based on contextual and temporal vector analysis

Publication Date: 2023.01.31 BANK OF AMERICA CORP

AI summary

A system for prioritizing a plurality of requests received from a plurality of clients is disclosed. The system receives the plurality of requests. For each request from the plurality of requests, the system extracts features of the request, where the extracted features provide information regarding a priority in performing the request. The extracted features correspond to a numerical representation of the request, such that if a priority level associated with the request is high the numerical representation comprises higher numerical values compared to another request that is associated with a low priority level. The system determines a prioritization in performing the plurality of requests by ranking a plurality of extracted features representing the plurality of requests based on ranking numerical values associated with the plurality of extracted features.