CNN Forecasting Technology Lifecycle Maturity

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Companies face challenges in forecasting the maturity phase of technologies, lacking a systematic approach to ensure investments in the right technology at the right time, leading to potential misalignment or obsolescence.

Innovation Solution

A data-driven methodology using a convolutional neural network (CNN) to forecast future feature values of technologies, combined with unsupervised clustering algorithms like K-means, and the Wardley mapping technique to predict lifecycle phases, based on historical data from open-source scientific literature.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If companies invest in new technologies without systematic forecasting, then they can explore potential innovations, but they risk investing in the wrong technology at the wrong time

Engineering Contradiction:

Improvetechnology investment alignmentVSAvoidinvestment timing accuracy

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent applies preliminary action by forecasting technology maturity phases before companies make investment decisions. The system analyzes historical data and current trends to predict when technologies will reach maturity, enabling companies to plan investments in advance rather than reacting to market changes. This resolves the contradiction by providing reliable timing predictions (improving reliability) while maintaining the ability to adapt to different technology trajectories (preserving adaptability).

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the technology lifecycle into distinct maturity phases (emerging, growing, mature, declining) and applies different analysis methods to each phase. This segmentation allows the system to provide phase-specific forecasts and recommendations, improving investment alignment by matching technologies to appropriate company needs at each stage while maintaining reliable predictions through phase-specific metrics.

Inventive Principle:

Principle #1Segmentation

2Adaptability or versatility

If companies invest in technology without forecasting maturity phases, then they can pursue innovation opportunities, but they cannot ensure technology remains current and useful

Engineering Contradiction:

Improvetechnology relevanceVSAvoidtechnology obsolescence timing

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent implements feedback by continuously monitoring technology trends and comparing actual development trajectories against predicted maturity phases. The system uses historical data from scientific literature and patent databases to provide ongoing feedback on technology progression, enabling companies to adjust their technology portfolios to maintain relevance while minimizing obsolescence losses through timely identification of maturity transitions.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs preliminary analysis of technology trajectories to predict maturity phases before obsolescence occurs. By forecasting when technologies will transition from growing to mature or declining phases, companies can proactively update their technology stacks rather than reacting to obsolescence, thereby maintaining technology relevance while reducing the time loss associated with outdated systems.

Inventive Principle:

Principle #10Preliminary action

3Measurement precision

If systematic forecasting methods are implemented, then investment accuracy improves, but the complexity of the forecasting system increases

Engineering Contradiction:

Improvematurity phase prediction accuracyVSAvoidforecasting system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent uses an intermediary approach by leveraging established external data sources (scientific literature databases, patent databases, industry reports) rather than building complex proprietary data collection systems. The forecasting system acts as an intermediary layer that processes and analyzes data from these existing sources using standardized methodologies, thereby achieving high prediction accuracy without proportionally increasing system complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system achieves high measurement precision by changing parameters dynamically based on technology phase. Different forecasting models and metrics are applied depending on whether the technology is in emerging, growing, mature, or declining phase. This parameter adaptation allows the system to maintain high accuracy across diverse technologies while managing complexity through phase-specific simplifications rather than requiring a single complex universal model.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20230244927A1Using CNN in a pipeline used to forecast the future statuses of the technologies

Publication Date: 2023.08.03 DELL PROD LP

AI summary

One example method includes obtaining data from one or more databases of information about one or more technologies, selecting a set of features for extraction from the data, extracting the features from the data, and using a convolutional neural network to generate a forecast for the features, and the forecast is made with respect to a defined time period. The forecast may indicate the expected lifecycle changes of the features over the defined period of time.