Unsupervised ML Content Brief Generation

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Solution Overview

Problem

Public relations departments face laborious and time-consuming processes in generating content briefs and headlines, which are further complicated by the need to keep up with rapidly changing social media and data volumes, limiting their ability to produce and update content efficiently.

Innovation Solution

A device and method using unsupervised machine learning to collect and cluster content, generate content briefs, and create headlines based on strategic guidelines, automating the process to reduce manual labor and enhance scalability and timeliness.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual content brief generation is used, then content quality can be maintained through human judgment, but productivity is severely limited to 1-2 briefs per week

Engineering Contradiction:

Improvecontent brief generation rateVSAvoidmanual research and analysis process

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent replaces the manual mechanical process of content brief generation with an automated machine learning system. The ML model automatically performs content analysis, cluster generation, and brief creation, eliminating the need for manual research and drafting while significantly increasing productivity from 1-2 briefs per week to multiple briefs generated instantaneously.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service content brief generation by allowing users to input basic parameters and receive fully automated content briefs without manual intervention. The ML model autonomously performs all analytical work including content clustering, insight generation, and brief structuring, making the process self-serve and highly scalable.

Inventive Principle:

Principle #25Self-service

2Ease of manufacture

If manual content brief generation is used, then content can be customized according to organizational strategy, but the process is laborious and time-consuming

Engineering Contradiction:

Improvecontent brief production processVSAvoidtime required for research and generation

Core Design Contradiction:

Ease of manufactureVSLoss of time

Solution Approach 1:

The system performs preliminary content analysis and clustering automatically before the user needs the content brief. By pre-processing large volumes of content and organizing it into clusters with key insights, the system eliminates the time-consuming manual research phase while maintaining customization capabilities through strategic parameter inputs.

Inventive Principle:

Principle #10Preliminary action

3Adaptability or versatility

If manually generated content briefs are used, then quality control can be maintained, but it is exceedingly difficult to keep up with rapidly changing social media and data volumes

Engineering Contradiction:

Improveability to keep up with changing media landscapesVSAvoidmanual tracking and updating process

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The ML-based system provides multi-functionality by automatically handling content collection, analysis, clustering, and brief generation in a single unified process. This universal system can adapt to various content types and sources including social media, news articles, and internal data, making it highly versatile for keeping up with changing media landscapes without increasing operational complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Productivity

If automated machine learning generation is used, then productivity and speed are dramatically improved, but the system requires complex machine learning models and data processing infrastructure

Engineering Contradiction:

Improvenumber of content briefs generatedVSAvoidmachine learning system architecture

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the content brief generation process into distinct modular components: content collection module, clustering module, insight generation module, and brief assembly module. This segmentation allows the complex ML system to be broken down into manageable functional units that can be independently optimized and maintained, reducing the perceived complexity while maintaining high productivity.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11947898B2System and method of content brief generation using machine learning

Publication Date: 2024.04.02 RAY DEBAJYOTI

AI summary

A method and apparatus of a device for generating one or more content briefs is described. In an exemplary embodiment, the device receives a strategy for generating the plurality of content briefs, wherein each of the plurality of content briefs is a structured document that is used to guide creation of content. In addition, the device may collect a collection of content and generate a plurality of content clusters using unsupervised machine learning to cluster the content collection with the received strategy. Furthermore, the device may generate the plurality of content briefs from the plurality of content clusters.