A method and an apparatus for pushing information are provided. The method may include: obtaining a prescription image presenting prescription information; recognizing the prescription image to obtain a recognition result inputting the recognition result into a pre-trained prescription image analyzing model to obtain the prescription information presented in the prescription image; and pushing information based on the prescription information.

According to an embodiment, an information processing apparatus comprises a first interface, a second interface, a third interface, and a processor. The first interface acquires a character string image that includes a character string. The second interface transmits and receives data to and from an internal device through a first network. The third interface transmits and receives data to and from an external device through a second network. The processor transmits, if the character string image includes personal information, the character string image to the internal device through the second interface and receive an input of the character string from the internal device and transmits, if the character string image does not include the personal information, the character string image to the external device through the third interface and receive an input of the character string from the external device.

The present disclosure provides a character recognition model training method and apparatus, a character recognition method and apparatus, a device and a medium, relating to the technical field of artificial intelligence, and specifically to the technical fields of deep learning, image processing and computer vision, which can be applied to scenarios such as character detection and recognition technology. The specific implementing solution is: partitioning an untagged training sample into at least two sub-sample images; dividing the at least two sub-sample images into a first training set and a second training set; where the first training set includes a first sub-sample image with a visible attribute, and the second training set includes a second sub-sample image with an invisible attribute; performing self-supervised training on a to-be-trained encoder by taking the second training set as a tag of the first training set, to obtain a target encoder.

Systems for intelligent sorting of time series data for improved contextual messaging are included herein. An intelligent sorting server may receive time series data comprising a plurality of chat messages. The intelligent sorting server may determine a first order of the plurality of chat messages based on a chronologic order. The intelligent sorting server may use one or more machine learning classifiers to identify candidates for reordering the chat messages. The intelligent sorting server may generate a second order of the chat messages based on the identified candidates for reordering. Accordingly, the intelligent sorting server may present, to a client device, a transcript of the chat messages associated with the second order and an indication that at least one chat message has been repositioned.

The present invention generally relates to system, method and graphical user interface for executing one or more tasks in dynamic data driven enterprise application. The invention includes creation of rules on a rule creation interface by one or more syntax from a rule creation syntax data library. The invention provides machine learning models driven rule engine for executing the tasks.

The present invention generally relates to system, method and graphical user interface for executing one or more tasks in dynamic data driven enterprise application. The invention includes creation of rules on a rule creation interface by one or more syntax from a rule creation syntax data library. The system of the invention is configured to identify optimum rule to process one or more tasks. The invention provides machine learning models driven rule engine for executing the tasks wherein an AI engine invokes dynamic conditions of the rules to execute the task.

A system may iteratively scan a portion of a document, extract first data from the portion of the document, and determine, using a trained model, whether the first data corresponds to one or more document types based on one or more confidence thresholds. The system may repeat this process, increasing the portion of the document scanned by a predetermined amount each iteration, until the first data corresponds to the one or more document types based on the one or more confidence thresholds. Responsive to determining the first data corresponds to the one or more document types based on the one or more confidence thresholds, the system may cause a graphical user interface (GUI) of a user device to display a notification indicating a document type match.

A system may iteratively scan a portion of a document, extract first data from the portion of the document, and determine, using a trained model, whether the first data corresponds to one or more document types based on one or more confidence thresholds. The system may repeat this process, increasing the portion of the document scanned by a predetermined amount each iteration, until the first data corresponds to the one or more document types based on the one or more confidence thresholds. Responsive to determining the first data corresponds to the one or more document types based on the one or more confidence thresholds, the system may cause a graphical user interface (GUI) of a user device to display a notification indicating a document type match.

A system for generating textual instructions for manufacturers from hybrid textual and image data includes a manufacturing instruction generator that may generate a language processing module from a first training set including at least a training annotated file describing at least a first product to manufacture, the at least an annotated file containing one or more textual data, and at least an instruction set containing one or more manufacturing instructions to manufacture the at least a first product. Manufacturing instruction generator may use the language processing to generate textual instructions for manufacturers from at least an annotated file and may initiate manufacture using the generated manufacturing instructions.

A system including a database configured to store data characterizing a plurality of emails, a plurality of projects, a plurality of vendors, and a plurality of invoices. Each invoice of the plurality of invoices includes data characterizing a project of the plurality of projects, a vendor of the plurality of vendors, and a plurality of invoice data. The system also includes a computing system communicatively coupled to the database and including at least one processor. The at least one processor is configured to receive a file in a first format, convert the file into a second format, determine a first project, a first vendor, and a first plurality of invoice data based on data in the file, prepare a new invoice based on the first project, the first vendor, and the first plurality of invoice data determined, and provide the new invoice in a third format.