A device includes a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations of loading a user profile for a user consuming undigitized, static media content; identifying an area of interest in the undigitized, static media content; analyzing the area of interest; responsive to the user profile, creating immersive content to enhance the area of interest; and providing the immersive content for presentation to the user.

Systems, methods, and products described herein include a server, which receives a request from a user's electronic client device. The server may query web pages and determine if the web pages comply with the compliance rules. When the web pages do not comply with the compliance rules, the server may generate an analysis report on the web pages and send the analysis report to the user. After the user reviews the report and chooses the trouble web pages, the server sends review alerts to the points of contact, such as the product owners and other stakeholders. The product owner may more easily be able to take proper actions and better manage the websites by refreshing noncompliant or incorrect content. In this way, the systems and methods allow product owners and users to manage and/or curate the content of web pages more systematically and automatically.

Systems, methods, and products described herein include a server, which receives a request from a user's electronic client device. The server may query web pages and determine if the web pages comply with the compliance rules. When the web pages do not comply with the compliance rules, the server may generate an analysis report on the web pages and send the analysis report to the user. After the user reviews the report and chooses the trouble web pages, the server sends review alerts to the points of contact, such as the product owners and other stakeholders. The product owner may more easily be able to take proper actions and better manage the websites by refreshing noncompliant or incorrect content. In this way, the systems and methods allow product owners and users to manage and/or curate the content of web pages more systematically and automatically.

A system for verifying data integrity in a database may include accessing a first file with a management object. The management object corresponds to a set of database tables located in a database. The system loads a data extract file including the management object, the data extract file containing fields related to content associated with the management object. The system accesses a second file with a condition, the condition being indicative of the content associated with the management object. The system generates code to evaluate the management object in the data extract file. The system evaluates the content associated with the management object in the data extract file using the generated code. The system identifies an entry in the data extract file that does not meet the condition, the entry representative of content associated with the management object, and then presents the entry to a user.

In one example, a system can receive a set of text samples and generate a set of summaries based on the set of text samples. The system can then generate a training dataset by iteratively executing a training-sample generation process. Each iteration can involve selecting multiple text samples from the set of text samples, combining the multiple text samples together into a training sample, determining a text category and a summary corresponding to a selected one of the multiple text samples, and including the text category and the summary in the training sample. After generating the training dataset, the system can use it to train a model. The trained model can then receive a target textual dataset and a target category as input, identify a portion of the target textual dataset corresponding to the target category, and generate a summarization of the portion of that target textual dataset.

In one example, a system can receive a set of text samples and generate a set of summaries based on the set of text samples. The system can then generate a training dataset by iteratively executing a training-sample generation process. Each iteration can involve selecting multiple text samples from the set of text samples, combining the multiple text samples together into a training sample, determining a text category and a summary corresponding to a selected one of the multiple text samples, and including the text category and the summary in the training sample. After generating the training dataset, the system can use it to train a model. The trained model can then receive a target textual dataset and a target category as input, identify a portion of the target textual dataset corresponding to the target category, and generate a summarization of the portion of that target textual dataset.

The field of the invention relates to systems and methods for data mining and processing, and more particularly to systems and methods for automating content from performance assessment data. In one embodiment, an automated notes and categorization system may include a primary database comprising performance assessment data. The primary database is operatively coupled to a computer program product having a computer-usable medium having a sequence of instructions which, when executed by a processor, causes said processor to execute a process that analyzes and converts raw performance data into automated content that presents data in readable user-friendly form to facilitate human understanding.

The present invention provides a method for updating and displaying information, which comprises a matrix program that operates as a program to analyze a data list and to form a table separately by setting a plurality of keywords, then produce an alive matrix map after the analysis result is matched and the table is created; therefore, the alive matrix map can add new data lists without repeating the tedious setting steps.

A text summarization system auto-generates text summarization models using a combination of neural architecture search and knowledge distillation. Given an input dataset for generating/training a text summarization model, neural architecture search is used to sample a search space to select a network architecture for the text summarization model. Knowledge distillation includes fine-tuning a language model for a given text summarization task using the input dataset, and using the fine-tuned language model as a teacher model to inform the selection of the network architecture and the training of the text summarization model. Once a text summarization model has been generated, the text summarization model can be used to generate summaries for given text.

Embodiments are directed towards real time display of event records and extracted values based on at least one extraction rule, such as a regular expression. A user interface may be employed to enable a user to have an extraction rule automatically generate and/or to manually enter an extraction rule. The user may be enabled to manually edit a previously provided extraction rule, which may result in real time display of updated extracted values. The extraction rule may be utilized to extract values from each of a plurality of records, including event records of unstructured machine data. Statistics may be determined for each unique extracted value, and may be displayed to the user in real time. The user interface may also enable the user to select at least one unique extracted value to display those event records that include an extracted value that matches the selected value.