Machine Learning Intangible Asset Valuation
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Solution Overview
Problem
The music industry's 'winner-take-it-all' economy and long cash conversion cycle hinder artists from receiving fair and timely payments for their work, as conventional valuation methods are unreliable and adapted for intangible assets, making it difficult for creators to finance their projects.
Innovation Solution
A system and method using a machine learning model to estimate the current value of intangible assets by processing source file representations, obtaining evaluation parameters from social media sources, and predicting future values, enabling fair and transparent valuation of intangible assets like copyrights, allowing for trading and investment in fractions of these assets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional financial ratio models (NPV model) are used to value intangible assets, then the valuation process is simple, but the accuracy and reliability of the valuation is poor
Solution Approach 1:
The patent replaces conventional mechanical financial ratio models with a machine learning-based valuation system. The system uses neural networks and algorithms to process multiple data sources (streaming data, social media, contextual information) to predict future revenues and calculate current values, substituting the simple but inaccurate NPV model with a complex but precise computational system.
Solution Approach 2:
The valuation system combines multiple types of data (streaming data from rights management systems, social media data, contextual information) into a composite valuation model. This multi-component approach integrates diverse information sources to achieve more accurate and reliable valuation than any single data source could provide alone.
2Reliability
If rights holders wait for traditional copyright revenue collection (50-100 years after death), then the full copyright revenue can be collected, but the cash conversion cycle is extremely long and artists cannot receive timely payment
Solution Approach 1:
The system performs preliminary valuation of intangible assets before the traditional revenue collection period. By predicting future revenues and calculating current values using machine learning models, the system enables advance monetization of copyrights, allowing rights holders to receive payment now rather than waiting decades for traditional revenue collection.
Solution Approach 2:
The patent introduces an intermediary valuation system between the creation of intangible assets and traditional revenue collection. This intermediary system uses machine learning to estimate current values and enable trading, acting as a bridge that allows rights holders to access value earlier in the cash conversion cycle while maintaining the integrity of future revenue streams.
3Productivity
If the music industry operates as a 'winner-take-it-all' economy, then a minority of hits generate the lion's share of revenue, but most artists cannot achieve commercial success and struggle to finance their projects
Solution Approach 1:
The patent segments the valuation and trading process into manageable components, allowing individual intangible assets to be valued and traded separately. This segmentation enables artists to access financing for specific projects rather than requiring proven track records, breaking the winner-take-all dynamic by providing individual funding opportunities.
Solution Approach 2:
The system changes the fundamental parameter of valuation from historical performance-based (requiring proven success) to predictive future-value-based. By using machine learning models to predict potential revenues, the system enables financing decisions based on projected value rather than past performance, increasing accessibility for emerging artists.
Data Source
AI summary
There is provided a system, method and computer program product for estimating a current value of an intangible asset, by obtaining a source file representing the intangible asset; analyzing the source file to identify at least one characteristic; classifying the source file to belong to at least one class based on the identified at least one characteristic; obtaining one or more evaluation parameters associated with the creator of the intangible asset from at least one first data source; obtaining any historical values associated with the intangible asset; predicting, using a machine learning model, a future value of the intangible asset based on the selected at least one class; the one or more evaluation parameter; and any obtained historical value; and estimating, by the machine learning model, the current value of the intangible asset based on the predicted future value and a pre-set time period.