A method includes performing, by a processor: receiving a first document, the first document comprising a first plurality of sub-documents that are related to one another in a first time sequence; converting the first plurality of sub-documents to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words; detecting a plurality of topics within the vectorized document, the plurality of topics being related to one another in the first time sequence; applying a process discovery algorithm to the plurality of topics to generate a model that is representative of relationships between the plurality of topics; receiving a second document containing subject matter related to a course of action, the second document comprising a second plurality of sub-documents that are related to one another in a second time sequence; using the model to generate a classification for the second document; and adjusting the course of action based on the classification for the second document.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: performing by a processor: receiving a first document, the first document comprising a first plurality of sub-documents that are related to one another in a first time sequence; converting the first plurality of sub-documents to a vector format to generate a vectorized document that encodes a probability distribution of words in the document and transition probabilities between words; detecting a plurality of topics within the vectorized document, the plurality of topics being related to one another in the first time sequence; applying a process discovery algorithm to the plurality of topics to generate a model that is representative of relationships between the plurality of topics; receiving a second document containing subject matter related to a course of action, the second document comprising a second plurality of sub-documents that are related to one another in a second time sequence; using the model to generate a classification for the second document; and adjusting the course of action based on the classification for the second document. 2. The method of claim 1, wherein adjusting the course of action based on the classification for the second document comprises: determining a destination for communication of the second document based on the classification for the second document; and electronically communicating the second document to the destination. 3. The method of claim 1, wherein adjusting the course of action based on the classification for the second document comprises: allocating computing resources based on the classification for the second document. 4. The method of claim 1, wherein converting the first plurality of sub-documents to the vector format comprises: applying a Doc2Vec algorithm to the first document to generate the vectorized document. 5. The method of claim 1, wherein converting the first plurality of sub-documents to the vector format comprises: applying a Latent Dirichlet Allocation (LDA) algorithm to the first document to generate the vectorized document. 6. The method of claim 1, wherein converting the first plurality of sub-documents to the vector format comprises: applying a Term Frequency-Inverse Document Frequency (TF-IDF) algorithm to the first document to generate the vectorized document. 7. The method of claim 1, wherein detecting the plurality of topics within the vectorized document comprises: applying a K-means algorithm to the vectorized document to detect a plurality of vector point clusters. 8. The method of claim 1, wherein detecting the plurality of topics within the vectorized document comprises: applying a K-medoids variant algorithm to the vectorized document to detect a plurality of vector point clusters. 9. The method of claim 1, wherein detecting the plurality of topics within the vectorized document comprises: applying a Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to the vectorized document to detect a plurality of vector point clusters. 10. The method of claim 1, wherein applying the process discovery algorithm to the plurality of topics to generate the model comprises: applying one of a fuzzy miner algorithm, heuristic miner algorithm, inductive miner algorithm, and genetic process miner algorithm to the plurality of topics to generate the model. 11. The method of claim 1, wherein the plurality of topics is a first plurality of topics and wherein using the model to generate the classification for the second document comprises: applying a trace alignment algorithm to the model and the second document to generate a quantitative measure of a difference between the model and a second plurality of topics obtained from the second document. 12. The method of claim 11, wherein adjusting the course of action based on the classification for the second document comprises: adjusting the course of action by determining a destination for communication of the second document based on the quantitative measure; and electronically communicating the second document to the destination. 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 first document containing subject matter related to a course of action, the first document comprising a plurality of sub-documents that are related to one another in a time sequence; using a model comprising a topic sequence derived from a second document to generate a classification for the first document; and adjusting the course of action based on the classification for the first document. 14. The system of claim 13, wherein adjusting the course of action based on the classification for the first document comprises: determining a destination for communication of the first document based on the classification for the first document; and electronically communicating the first document to the destination. 15. The system of claim 13, wherein adjusting the course of action based on the classification for the first document comprises: allocating computing resources based on the classification for the first document. 16. The system of claim 13, wherein using the model comprising the topic sequence derived from the second document to generate the classification for the first document comprises: applying a trace alignment algorithm to the model and the second document to generate a quantitative measure of a difference between the model and the second plurality of topics obtained from the second 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 first document containing subject matter related to a course of action, the first document comprising a plurality of sub-documents that are related to one another in a time sequence; using a plurality of models comprising a plurality of topic sequences, respectively, derived from a plurality of second documents to generate a classification for the first document; and adjusting the course of action based on the classification for the first document. 18. The computer program product of claim 17, wherein the plurality of topics is a first plurality of topics and wherein using the model to generate the classification for the second document comprises: applying a trace alignment algorithm to the plurality of models and the second document to generate a plurality of quantitative measures of differences between the plurality of models and a second plurality of topics obtained from the second document, respectively. 19. The computer program product of claim 18, wherein adjusting the course of action based on the classification for the second document comprises: adjusting the course of action by determining a plurality of destinations for communication of the second document based on the plurality of quantitative measures; and electronically communicating the second document to the plurality of destinations. 20. The computer program product of claim 17, wherein adjusting the course of action based on the classification for the first document comprises: allocating computing resources based on the classification for the first document.