A deep learning approach for comparative incident reference identification. An incident comparator receives descriptive texts including at least one past incidents, associated past features, selected incident and associated selected incident features. The descriptive texts are sent toward a text to vector engine. The incident comparator receives neural vectors associated to the descriptive texts and sends the neural vectors toward a learning engine. The incident comparator receives common features based on deep learning of the neural vectors. The neural vectors are plotted in N-D space based on the common features to create incident reference models. The incident comparator compares the selected incident and associated features with the incident reference models to identify and output comparative reference incidents.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for a deep learning approach for comparative incident reference identification, the method comprising: receiving, by an incident comparator, one or more descriptive texts comprising at least one of a one or more past incidents, one or more past features associated with the one or more past incidents, selected incident and one or more selected incident features associated with the selected incident; sending, by the incident comparator, the one or more descriptive texts toward a text to vector engine; receiving, by the incident comparator, one or more neural vectors associated with the one or more descriptive texts; sending, by the incident comparator, the one or more neural vectors toward a learning engine; receiving, by the incident comparator, one or more common features based on deep learning of the one or more neural vectors; plotting, by the incident comparator, the one or more neural vectors in N-D space for at least one of the one or more common features, creating one or more incident reference models; comparing, by the incident comparator, the selected incident and the one or more selected incident features associated with the one or more incident reference models to identify one or more comparative reference incidents; and outputting, by the incident comparator, the one or more comparative reference incidents. 2. The method of claim 1, wherein the one or more descriptive texts are based on receiving input from at least one of one or more mobile devices, one or more connected sensors and one or more social network sources. 3. The method of claim 2, wherein the one or more social network sources comprises feedback of at least one of a user, one or more social network friends and one or more social network friends of a friend. 4. The method of claim 1, wherein outputting the one or more comparative reference incidents comprises data in a format of at least one of one or more text, one or more numeric value, one or more positional references and one or more referential pointers. 5. The method of claim 1, wherein the one or more comparative reference incidents are filtered based on at least one of a strongest relationship threshold, the one or more selected incident features and a proximity to incident feature. 6. The method of claim 1, wherein outputting the one or more comparative reference incidents comprises at least one of an identifier of one or more incident sources distinguishing at least one of a user, a one or more social network friends and a one or more social network friends of a friend. 7. The method of claim 1, wherein outputting the one or more comparative reference incidents are filtered based on at least one of a predetermined quantity, a number of features to display, the one or more selected incident features and a proximity to an incident feature. 8. A computer program product for a deep learning approach for comparative incident reference identification, the computer program product comprising: one or more computer readable storage media and program instructions stored on the one or more computer readable storage media, the program instructions comprising: program instructions to, receive, by an incident comparator, one or more descriptive texts comprising at least one of a one or more past incidents, one or more past features associated with the one or more past incidents, selected incident and one or more selected incident features associated with the selected incident; program instructions to, send, by the incident comparator, the one or more descriptive texts toward a text to vector engine; program instructions to, receive, by the incident comparator, one or more neural vectors associated with the one or more descriptive texts; program instructions to, send, by the incident comparator, the one or more neural vectors toward a learning engine; program instructions to, receive, by the incident comparator, one or more common features based on deep learning of the one or more neural vectors; program instructions to, plot, by the incident comparator, the one or more neural vectors in N-D space for at least one of the one or more common features, creating one or more incident reference models; program instructions to, compare, by the incident comparator, the selected incident and the one or more selected incident features associated with the one or more incident reference models to identify one or more comparative reference incidents; and program instructions to, output, by the incident comparator, the one or more comparative reference incidents. 9. The computer program product of claim 8, wherein the one or more descriptive texts are based on receiving input from at least one of one or more mobile devices, one or more connected sensors and one or more social network sources. 10. The computer program product of claim 9, wherein the one or more social network sources comprises feedback of at least one of a user, one or more social network friends and one or more social network friends of a friend. 11. The computer program product of claim 8, wherein outputting the one or more comparative reference incidents comprises data in a format of at least one of one or more text, one or more numeric value, one or more positional references and one or more referential pointers. 12. The computer program product of claim 8, wherein the one or more comparative reference incidents are filtered based on at least one of a strongest relationship threshold, the one or more selected incident features and a proximity to incident feature. 13. The computer program product of claim 8, wherein outputting the one or more comparative reference incidents comprises at least one of an identifier of one or more incident sources distinguishing at least one of a user, a one or more social network friends and a one or more social network friends of a friend. 14. The computer program product of claim 8, wherein outputting the one or more comparative reference incidents are filtered based on at least one of a predetermined quantity, a number of features to display, the one or more selected incident features and a proximity to an incident feature. 15. A computer system for a deep learning approach for comparative incident reference identification, the computer system comprising: one or more computer processors; one or more computer readable storage media; program instructions stored on the one or more computer readable storage media for execution by at least one of the one or more computer processors, the program instructions comprising: program instructions to, receive, by an incident comparator, one or more descriptive texts comprising at least one of a one or more past incidents, one or more past features associated with the one or more past incidents, selected incident and one or more selected incident features associated with the selected incident; program instructions to, send, by the incident comparator, the one or more descriptive texts toward a text to vector engine; program instructions to, receive, by the incident comparator, one or more neural vectors associated with the one or more descriptive texts; program instructions to, send, by the incident comparator, the one or more neural vectors toward a learning engine; program instructions to, receive, by the incident comparator, one or more common features based on deep learning of the one or more neural vectors; program instructions to, plot, by the incident comparator, the one or more neural vectors in N-D space for at least one of the one or more common features, creating one or more incident reference models; program instructions to, compare, by the incident comparator, the selected incident and the one or more selected incident features associated with the one or more incident reference models to identify one or more comparative reference incidents; and program instructions to, output, by the incident comparator, the one or more comparative reference incidents. 16. The computer system of claim 15, wherein the one or more descriptive texts are based on receiving input from at least one of one or more mobile devices, one or more connected sensors and one or more social network sources. 17. The computer system of claim 16, wherein the one or more social network sources comprises feedback of at least one of a user, one or more social network friends and one or more social network friends of a friend. 18. The computer system of claim 15, wherein outputting the one or more comparative reference incidents comprises data in a format of at least one of one or more text, one or more numeric value, one or more positional references and one or more referential pointers. 19. The computer system of claim 15, wherein the one or more comparative reference incidents are filtered based on at least one of a strongest relationship threshold, the one or more selected incident features and a proximity to incident feature. 20. The computer system of claim 15, wherein outputting the one or more comparative reference incidents comprises at least one of an identifier of one or more incident sources distinguishing at least one of a user, a one or more social network friends and a one or more social network friends of a friend.