A method includes performing, by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence, converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words, applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document, associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document, and adjusting the course of action responsive to predicting the future subject matter.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: performing by a processor: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence; converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words; applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document; associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document; and adjusting the course of action responsive to predicting the future subject matter. 2. The method of claim 1, wherein converting the document to the vector format comprises: applying a Doc2Vec algorithm to the document to generate the vectorized document. 3. The method of claim 1, wherein converting the document to the vector format comprises: applying a Latent Dirichlet Allocation (LDA) algorithm to the document to generate the vectorized document. 4. The method of claim 1, wherein converting the document to the vector format comprises: applying a Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to the document to generate the vectorized document. 5. The method of claim 1, wherein the machine learning algorithm is a neural network machine learning model. 6. The method of claim 1, wherein the machine learning algorithm is a linear regression machine learning model. 7. The method of claim 1, wherein the machine learning algorithm comprises an ensemble machine learning model. 8. The method of claim 7, wherein the ensemble machine learning model comprises a plurality of Bayes optimal classifier, bagging, boosting, Bayesian parameter averaging, Bayesian model combination, bucket of models, and stacking. 9. The method of claim 1, wherein associating the estimated vectorized document with a reference document comprises: performing a Euclidean Distance analysis between the estimated vectorized document and the reference document. 10. The method of claim 1, wherein associating the estimated vectorized document with a reference document comprises: performing a Cosine Similarity analysis between the estimated vectorized document and the reference document. 11. The method of claim 1, wherein adjusting the course of action responsive to predicting the future subject matter comprises: determining a destination for communication of the document based on the future subject matter that was predicted; and electronically communicating the document to the destination. 12. The method of claim 1, wherein adjusting the course of action responsive to predicting the future subject matter comprises: allocating computing resources based on the future subject matter that was predicted. 13. A system comprising: a processor; and a memory coupled to the processor and comprising computer readable program code embodied in the memory that is executable by the processor to perform: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence; converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words; applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document; associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document; adjusting the course of action by determining a destination for communication of the document based on the future subject matter that was predicted; and electronically communicating the document to the destination. 14. The system of claim 13, wherein converting the document to the vector format comprises: applying one of a Doc2Vec algorithm, a Latent Dirichlet Allocation (LDA) algorithm, and a Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to the document to generate the vectorized document. 15. The system of claim 13, wherein the machine learning algorithm is one of a neural network machine learning model, a linear regression machine learning model, and an ensemble machine learning model. 16. The system of claim 13, wherein associating the estimated vectorized document with a reference document comprises: performing one of a Euclidean Distance analysis and a Cosine Similarity analysis between the estimated vectorized document and the reference document. 17. A computer program product comprising: a tangible computer readable storage medium comprising computer readable program code embodied in the medium that is executable by a processor to perform: receiving a document containing subject matter related to a course of action, the document comprising a plurality of sub-documents that are related to one another in a time sequence; converting the document to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words; applying a machine learning algorithm to the vectorized document to generate an estimated vectorized document; associating the estimated vectorized document with a reference document; predicting future subject matter contained in a future sub-document of the document based on the reference document; and adjusting the course of action by allocating computing resources based on the future subject matter that was predicted. 18. The computer program product of claim 13, wherein converting the document to the vector format comprises: applying one of a Doc2Vec algorithm, a Latent Dirichlet Allocation (LDA) algorithm, and a Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to the document to generate the vectorized document. 19. The computer program product of claim 13, wherein the machine learning algorithm is one of a neural network machine learning model, a linear regression machine learning model, and an ensemble machine learning model. 20. The computer program product of claim 13, wherein associating the estimated vectorized document with a reference document comprises: performing one of a Euclidean Distance analysis and a Cosine Similarity analysis between the estimated vectorized document and the reference document.