Method and apparatus for the semi-autonomous management, analysis and distribution of intellectual property assets between various entities

A machine learning engine to manage digital assets in a multi-phase management comprising:

• a database to store digital assets, wherein the digital assets can be associated with one or more phases of an asset lifecycle, wherein the asset lifecycle comprises at least an asset generation phase, an asset examination phase, an asset diligence phase, and an asset transfer phase;
• a processing device operatively coupled to memory comprising instructions, wherein the processing device is configured to:
• generate a binary file for each digital asset based on a machine learning model comprising a vector representation based on paragraph vector classification using an unsupervised learning model and an asset similarity metric based on comparison of the vector representation with the other digital assets;
• and
• provide a multi-phase management interface to enable different analytical analysis for each phase of the asset lifecycle, wherein the analytical analysis for each phase comprises at least a ranked set of results.

A method to manage digital assets comprising:

• maintaining a repository of a plurality of digital assets wherein the repository comprise digital assets in different formats at least comprising textual content, wherein the plurality of digital assets can be associated with one or more phases of an asset lifecycle, wherein the asset lifecycle comprises at least an asset generation phase, an asset examination phase, an asset diligence phase, and an asset transfer phase;
• selecting a corpus of digital assets form the repository;
• generating a multi-phase management model using machine learning on at least the textual content of the corpus of digital assets from the repository, wherein the multi-phase management model is usable with at least the asset lifecycle, wherein the machine learning comprises a deep-learning neural network based on:
• training an unsupervised learning model based at least on constituent text for a plurality of linguistic and descriptive scopes segments from the corpus of digital assets, wherein the unsupervised learning model comprises training a set of paragraph vector classifiers;
• for each digital asset in the corpus, using the unsupervised learning model to determine a paragraph vectors for the textual content using the paragraph vector classifiers, and map each asset to a fixed length asset vector based at least on the paragraph vectors;
• determining a mean vector for the corpus based on the asset vectors to denoise the vector representations produced by the unsupervised learning model;
• adjusting the asset vector for each digital asset in the corpus based on the mean vector for the corpus;
• and
• determining a similarity metric between each digital asset in the corpus based on the asset vector for each digital asset using vector similarity and contextual similarity analysis.