ML Entity Classification for Sales Scenario Forecasting

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Solution Overview

Problem

Existing sales planning systems lack the ability to dynamically simulate the impact of strategic changes and predict outcomes based on hypothetical scenarios, hindering the capacity to tailor sales approaches, prioritize efforts, and forecast revenue potential accurately.

Innovation Solution

A computerized method and system utilizing machine learning models for analyzing and presenting entity class and performance data, enabling dynamic simulation and prediction of outcomes through a graphical user interface, allowing users to query and visualize proposed changes in entity classes and performance data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing computing technology is used for sales planning, then the planning process can be completed with current resources, but the system lacks the ability to dynamically simulate strategic changes and predict outcomes accurately

Engineering Contradiction:

Improveability to dynamically simulate strategic changesVSAvoidaccuracy in forecasting revenue potential

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent replaces traditional mechanical sales planning systems with machine learning models that can dynamically simulate strategic changes. The ML models process historical sales data, entity attributes, and proposed changes to predict outcomes, substituting static spreadsheets and manual analysis with adaptive computational intelligence that continuously learns from data patterns.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system changes parameters by incorporating multiple entity attributes (industry, size, location, performance metrics) and proposed strategic changes as inputs to the ML models. This allows dynamic adjustment and simulation of different scenarios by modifying input parameters, enabling accurate prediction of revenue potential under various strategic conditions.

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If traditional sales planning methods are used, then resource consumption is lower, but time and resource consumption increases when attempting to improve accuracy through manual analysis

Engineering Contradiction:

Improveaccuracy in sales planningVSAvoidtime consumption in planning process

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The ML models perform self-service by automatically processing sales data, entity attributes, and proposed changes to generate predictions without requiring extensive manual analysis. The system autonomously executes simulations and generates forecasted outcomes, reducing both time consumption and the need for human resource-intensive planning activities while maintaining high accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-processing and analyzing historical sales data and entity attributes before actual planning occurs. The ML models are trained in advance on comprehensive datasets, enabling rapid prediction and simulation during the planning process, thus reducing real-time computational requirements and accelerating decision-making.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If existing planning systems are used, then implementation is simpler, but the capacity to tailor sales approaches and prioritize efforts is limited

Engineering Contradiction:

Improvecapacity to tailor sales approachesVSAvoidcomplexity of planning system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent applies segmentation by dividing entities into different classes based on attributes such as industry, size, and performance metrics. The ML models process segmented entity data to generate tailored predictions and recommendations for each class, enabling customized sales approaches while managing system complexity through modular processing of segmented information.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The ML-based planning system achieves universality by handling multiple functions within a single platform: data processing, entity classification, strategic change simulation, revenue forecasting, and recommendation generation. This multi-functional approach increases adaptability to tailor sales approaches across different entities and scenarios while consolidating complexity into an integrated system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250371475A1Advanced ML models for entity performance prediction and entity class analysis

Publication Date: 2025.12.04 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

A computerized method analyzes and presents entity class and performance data. An entity identifier of an entity is received via an entity identifier prompt on a user interface (UI). An icon representing the entity identifier is presented along with current performance data of the associated entity in a portion of the UI associated with a current entity class of the entity. A proposed performance data value for the entity is received and the proposed performance data value is provided to an entity classifier model as input. A proposed entity class is generated using the entity classifier model and based on the proposed performance data value. The icon representing the entity identifier is then automatically moved to a portion of the UI associated with the proposed entity class, whereby the entity can be compared to other entities in the proposed entity class.