Abstract: A method of sparse intent clustering is provided. The method comprises identifying features in a number of electronic user reports created by a user and contained in a database, wherein the features include a title and description. The features of each user report are encoded into a binary vector. The binary vector for each user report is fed into an autoencoder neural network that creates a N-dimensional vector representing the user report. The float vectors representing the user reports are projected into a N-dimensional space. The float vectors are clustered according to cosine similarities, wherein each vector cluster represents an intent of the user in creating the reports. The intent of each vector cluster is then labeled.

Abstract: A method of sparse intent clustering is provided. The method comprises identifying features in a number of electronic user reports created by a user and contained in a database, wherein the features include a title and description. The features of each user report are encoded into a binary vector. The binary vector for each user report is fed into an autoencoder neural network that creates a N-dimensional vector representing the user report. The float vectors representing the user reports are projected into a N-dimensional space. The float vectors are clustered according to cosine similarities, wherein each vector cluster represents an intent of the user in creating the reports. The intent of each vector cluster is then labeled.

Abstract: A method of latent intent clustering is provided. The method comprises encoding identified features in a number of electronic user reports in a database. A binary matrix is created, wherein each row of the binary matric represents a different report and each column represents a different available feature. A 1 is placed in each cell of the matrix that matches a feature present in a user report. Cosine similarities are calculated for the user reports, and a similarity matrix is created, wherein each row and column of the binary matrix represents a different report, and wherein the cosine similarities of the reports are placed in corresponding cells of the matrix. The reports are clustered according to the cosine similarities. Features common to reports in each cluster are identified, and an intent of each report cluster is labeled according to the common features.

Abstract: A method, computer system, and computer program product are provided for generating reports. Existing reports are collected and modeled to determine a number of contexts. An index of the existing reports is generated according the contexts determined by the modeling. a predicted context of a new report is predicted according to the modeling. According to the index, suggested reports are identified based on the predicted context for the new report. The suggested reports are presented in a graphical user interface.

Abstract: A method, computer system, and computer program product are provided for generating reports. A subset of data fields is identified for inclusion in a new report. A context of the new report is determined based on the subset and a sequence in which the data fields of the subset were identified. Using a machine learning model, a set of suggested fields is determined based on the context of the new report. The set of the suggested fields in a graphical user interface on a display system.

Abstract: A method of latent intent clustering is provided. The method comprises encoding identified features in a number of electronic user reports in a database. A binary matrix is created, wherein each row of the binary matric represents a different report and each column represents a different available feature. A 1 is placed in each cell of the matrix that matches a feature present in a user report. Cosine similarities are calculated for the user reports, and a similarity matrix is created, wherein each row and column of the binary matrix represents a different report, and wherein the cosine similarities of the reports are placed in corresponding cells of the matrix. The reports are clustered according to the cosine similarities. Features common to reports in each cluster are identified, and an intent of each report cluster is labeled according to the common features.

Abstract: A method of sparse intent clustering is provided. The method comprises identifying features in a number of electronic user reports created by a user and contained in a database, wherein the features include a title and description. The features of each user report are encoded into a binary vector. The binary vector for each user report is fed into an autoencoder neural network that creates a N-dimensional vector representing the user report. The float vectors representing the user reports are projected into a N-dimensional space. The float vectors are clustered according to cosine similarities, wherein each vector cluster represents an intent of the user in creating the reports. The intent of each vector cluster is then labeled.

Abstract: A method, computer system, and computer program product are provided for generating reports. Existing reports are collected and modeled to determine a number of contexts. An index of the existing reports is generated according the contexts determined by the modeling. a predicted context of a new report is predicted according to the modeling. According to the index, suggested reports are identified based on the predicted context for the new report. The suggested reports are presented in a graphical user interface.