Abstract: A hierarchical document classification system is disclosed. The system includes a text-based document classifier model for classifying an input electronic document into one of a set of predefined document categories. The system further includes an image-based metadata identification model for classifying electronic documents of a particular document category into a set of metadata categories. The system further includes a fuzzy text matcher for supplementing classification accuracy of the image-based metadata identification model to obtain a metadata category for the input electronic document.

Abstract: A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.

Abstract: A method for processing digital transactions includes receiving, from one or more source systems, a plurality of uncategorized digital transactions; and prioritizing the uncategorized digital transactions according to a key performance indicator (KPI) of each uncategorized digital transaction. A subset of the prioritized uncategorized digital transactions is communicated to a labeling system configured to apply a category label to each digital transaction. A subset of categorized digital transactions corresponding to the subset of the prioritized uncategorized digital transactions is received from the labeling system. Category label and a label confidence value are applied to each transaction of a remaining set of uncategorized digital transactions based on the subset of categorized digital transactions. The label confidence value is indicative of a confidence that a given label was correctly applied to a given transaction.

Abstract: A system for self-learning archival of electronic data may be provided. A binary classifier may identify a text segment of an electronic dataset in response to text of the electronic dataset being associated with indicators of a word model. A first multiclass classifier may generate a first classification set comprising respective statistical metrics for the datafield that each predefined identifier in a group of predefined identifiers is representative of the datafield. A second multiclass classifier may receive a context of the electronic dataset and generate a second classification set. A combination classifier may apply weight values to the first classification set and the second classification set and form a weighted classification set and select a predefined identifier as being representative of the datafield based on the weighted classification set. The processor may store, in a memory, a data record comprising an association between the predefined identifier and the datafield.

Abstract: An identification and management system for log entries may filter historical data and generate closed log entries as a reference dataset. A dynamic learning engine may perform statistical modelling using the reference dataset to assign predetermined categories to each of a number of open log entries. An automation index may be generated for each of the open log entries. The automation index is indicative of accuracy of the assigned categories. Some of the open log entries may be identified as priority log entries which are representative of a group of the open log entries. The assigned category of the priority log entries may be analyzed for accuracy and the results of the analysis may be used to train the statistical model so that the open log entries may be iteratively assigned and re-assigned a category until the category of each open log entry reaches a desired accuracy.

Abstract: A method for processing digital transactions includes receiving, from one or more source systems, a plurality of uncategorized digital transactions; and prioritizing the uncategorized digital transactions according to a key performance indicator (KPI) of each uncategorized digital transaction. A subset of the prioritized uncategorized digital transactions is communicated to a labeling system configured to apply a category label to each digital transaction. A subset of categorized digital transactions corresponding to the subset of the prioritized uncategorized digital transactions is received from the labeling system. Category label and a label confidence value are applied to each transaction of a remaining set of uncategorized digital transactions based on the subset of categorized digital transactions. The label confidence value is indicative of a confidence that a given label was correctly applied to a given transaction.

Abstract: A hierarchical document classification system is disclosed. The system includes a text-based document classifier model for classifying an input electronic document into one of a set of predefined document categories. The system further includes an image-based metadata identification model for classifying electronic documents of a particular document category into a set of metadata categories. The system further includes a fuzzy text matcher for supplementing classification accuracy of the image-based metadata identification model to obtain a metadata category for the input electronic document.

Abstract: An identification and management system for log entries may filter historical data and generate closed log entries as a reference dataset. A dynamic learning engine may perform statistical modelling using the reference dataset to assign predetermined categories to each of a number of open log entries. An automation index may be generated for each of the open log entries. The automation index is indicative of accuracy of the assigned categories. Some of the open log entries may be identified as priority log entries which are representative of a group of the open log entries. The assigned category of the priority log entries may be analyzed for accuracy and the results of the analysis may be used to train the statistical model so that the open log entries may be iteratively assigned and re-assigned a category until the category of each open log entry reaches a desired accuracy.

Abstract: A system for self-learning archival of electronic data may be provided. A binary classifier may identify a text segment of an electronic dataset in response to text of the electronic dataset being associated with indicators of a word model. A first multiclass classifier may generate a first classification set comprising respective statistical metrics for the datafield that each predefined identifier in a group of predefined identifiers is representative of the datafield. A second multiclass classifier may receive a context of the electronic dataset and generate a second classification set. A combination classifier may apply weight values to the first classification set and the second classification set and form a weighted classification set and select a predefined identifier as being representative of the datafield based on the weighted classification set. The processor may store, in a memory, a data record comprising an association between the predefined identifier and the datafield.

Abstract: A self-learning system for categorizing log entries may be provided. The system may display a first log entry and receive a categorical identifier for the first log entry. The system may parse the first log entry for predetermined text information and predetermined image information. The predetermined text information may be included in a datafield classifier and the predetermined image information included in a metadata classifier. The system may identify the predetermined text information in the log entry and adjust a first prioritization of respective categorical identifiers included in the datafield classifier. The system may identify the predetermined image information in the first log entry and adjust a second prioritization of the respective categorical identifiers included in the metadata classifier. The system may map a second log entry to the categorical identifier based on adjustment of the first prioritization or adjustment of the second prioritization.

Abstract: A self-learning system for categorizing log entries may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy of the categorical identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy categorical identifiers representing the metafields. A combination classifier may form a weighted classification set and select a categorical identifier as being representative of the datafield based on the weighted classification set. A categorical controller may identify new categories based on an analysis of the context metrics of the log entry.

Abstract: A self-learning system for categorizing log entries may be provided. A text classifier may identify a log description of a log entry in response to text of the log description being associated with indicators of a word model. A datafield classifier may generate a datafield metrics including an accuracy of the categorical identifiers representing the datafield. A metafield classifier may generate a context metrics for the context of the log entry, the context metrics including an accuracy categorical identifiers representing the metafields. A combination classifier may form a weighted classification set and select a categorical identifier as being representative of the datafield based on the weighted classification set. A categorical controller may identify new categories based on an analysis of the context metrics of the log entry.