Entity Issue Prediction Models for Proactive Resource Allocation

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Solution Overview

Problem

Centralized database systems lack the ability to proactively identify future issues, predict resource needs, and anticipate actions of entities, leading to inefficiencies and potential disruptions.

Innovation Solution

A central database system trains machine-learned models using historical data to predict future issues, resource requirements, and anticipated actions of entities, enabling proactive management and resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If machine-learned models are trained using historical data to predict future issues, resource needs, and entity actions, then the system can proactively identify and mitigate risks, but the device complexity and computational resources required increase

Engineering Contradiction:

Improveability to predict future issues and resource needsVSAvoidcomplexity of machine-learned model training and deployment

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by training machine-learned models on historical data to predict future issues, resource requirements, and entity actions before they occur. This enables proactive identification and mitigation of risks, allowing the system to prepare appropriate responses in advance rather than reacting to problems after they manifest.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Machine-learned models serve as intermediaries between historical data and future predictions. These models process and analyze historical entity data, issue data, and action data to generate predictions about future states, acting as a mediator that transforms past information into actionable insights without requiring direct observation of future events.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If the system proactively performs actions based on predicted future issues, then operational efficiency is enhanced, but the loss of time for data collection and model training increases

Engineering Contradiction:

Improveoperational efficiency through proactive managementVSAvoidtime required for historical data collection and model training

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system collects and processes historical data in advance to train prediction models before they are needed for operational decisions. This preliminary data collection and model training enables the system to make rapid proactive decisions when predictions are required, reducing the time loss during actual operational periods.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system maintains continuous operation by running prediction models that were trained on historical data, enabling uninterrupted proactive management. Once models are trained, they can continuously generate predictions without requiring ongoing data collection during the prediction phase, maintaining operational efficiency.

Inventive Principle:

Principle #20Continuity of useful action

3Productivity

If the system predicts resource requirements in advance for target entities, then resource allocation is optimized, but the quantity of historical data required for accurate predictions increases

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidvolume of historical data needed for training

Core Design Contradiction:

ProductivityVSQuantity of substance

Solution Approach 1:

The system extracts only the most relevant features and patterns from large volumes of historical entity data, issue data, and action data. By identifying and extracting key predictive features rather than processing all raw data, the system reduces the effective data volume required for training while maintaining prediction accuracy for resource requirement forecasting.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The prediction models focus on local quality by identifying specific critical features and patterns within the historical data that are most relevant to resource requirement predictions. Rather than treating all data uniformly, the system identifies and weights important local characteristics that drive accurate predictions with reduced data requirements.

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS12505396B2Machine learned entity issue models for centralized database predictions

Publication Date: 2025.12.23 GUSTO INC

AI summary

A central database system trains and applies machine-learned models based on characteristics of one or more entities associated with the central database system. For instance, the central database system trains a machine-learned model configured to identify issues a target entity is likely to encounter based on training data identifying characteristics of historical entities and issues faced by the historical entities. Likewise, the central database system trains machine-learned models configured to predict actions that entities are likely to take in the future, and resources required to take those actions. The central database system can then perform one or more proactive actions or make one or more recommendations based on the predicted issues, the predicted future actions, and the predicted required resources.