G06V30/196

Automated honeypot creation within a network

Systems and methods for managing Application Programming Interfaces (APIs) are disclosed. Systems may involve automatically generating a honeypot. For example, the system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a client device, a call to an API node and classifying the call as unauthorized. The operation may include sending the call to a node-imitating model associated with the API node and receiving, from the node-imitating model, synthetic node output data. The operations may include sending a notification based on the synthetic node output data to the client device.

Character recognizing apparatus and non-transitory computer readable medium
11568659 · 2023-01-31 · ·

A character recognizing apparatus includes an acquiring unit, an identifying unit, and a character recognizing unit. The acquiring unit acquires a string image that is an image of a string generated in accordance with one of multiple string generation schemes. The identifying unit identifies a range specified for a result of character recognition in each of the multiple string generation schemes. The character recognizing unit performs first character recognition on the string image, and if a result of the first character recognition has a feature of a particular string generation scheme of the multiple string generation schemes, the character recognizing unit performs second character recognition on the string image within the range specified for a result of character recognition in the particular string generation scheme.

SYSTEMS AND METHODS FOR REPRESENTING AND SEARCHING CHARACTERS
20230230403 · 2023-07-20 ·

Methods and supporting systems for representing and searching characters, comprising: obtaining an image of the character, labelling a structure of the character by defining a plurality of nodes and a plurality of edges on the character in the image, and generating a representation of the character by extracting a set of two-dimensional coordinates to represent the plurality of nodes and by extracting a matrix to represent the plurality of edges, and providing the representation in a searchable database.

Data model generation using generative adversarial networks

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Optical character recognition of documents having non-coplanar regions
11699294 · 2023-07-11 · ·

Systems and methods for performing OCR of an image depicting text symbols and imaging a document having a plurality of planar regions are disclosed. An example method comprises: receiving a first image of a document having a plurality of planar regions and one or more second images of the document; identifying a plurality of coordinate transformations corresponding to each of the planar regions of the first image of the document; identifying, using the plurality of coordinate transformations, a cluster of symbol sequences of the text in the first image and in the one or more second images; and producing a resulting OCR text comprising a median symbol sequence for the cluster of symbol sequences.

METHOD FOR COMPARING CONTENT OF TWO DOCUMENT FILES, AND METHOD FOR TRAINING A GRAPH NEURAL NETWORK STRUCTURE TO IMPLEMENT THE SAME
20230215205 · 2023-07-06 ·

A method for comparing content of two document files each having a plurality of content blocks is provided. The method is to be implemented by an electronic device and includes the steps of: performing, for the each of the content blocks in each of the document files, a pre-process operation so as to obtain a plurality of properties associated with the content block; comparing, for each content block from one of the document files, the properties thereof with the properties of each of the plurality of content blocks of the other one of the document files; and generating a comparison result based on the operations of the comparing.

Image processing apparatus that sets metadata of image data, method of controlling same, and storage medium
11694458 · 2023-07-04 · ·

An image processing apparatus that enables easy setting of metadata of image data. The image processing apparatus obtains image data associated with a selected work. A key candidate is identified from t image data based on one or more key types defined according to the selected work. A value candidate corresponding to the identified key candidate is identified based on a value type rule and a value search area rule which are defined for each of the one or more key types, and the identified value candidate is set as the metadata of the image data.

Systems and methods for removing identifiable information

Systems and methods for censoring text characters in text-based data are provided. In some embodiments, an artificial intelligence system may be configured to receive text-based data and store the text-based data in a database. The artificial intelligence system may be configured to receive a list of target pattern types identifying sensitive data and receive censorship rules for the target pattern types determining target pattern types requiring censorship. The artificial intelligence system may be configured to assemble a computer-based model related to a received target pattern type in the list of target pattern types. The artificial intelligence system may be configured to use a computer-based model to identify a target data pattern corresponding to the received target pattern type within the text-based data, identify target characters within the target data pattern, and to assign an identification token to the target characters.

Digital image processing

A computer-implemented method for processing a digital image. The digital image comprises one or more text cells, wherein each of the one or more text cells comprises a string and a bounding box. The method comprises receiving the digital image in a first format, the first format providing access to the strings and the bounding boxes of the one more text cells. The methods further comprises encoding the strings of the one or more text cells as visual pattern according to a predefined string encoding scheme and providing the digital image in a second format. The second format comprises the visual pattern of the strings of the one or more text cells. A corresponding system and a related computer program product is provided.

Application interface governance platform to harmonize, validate, and replicate data-driven definitions to execute application interface functionality
11609801 · 2023-03-21 · ·

Various embodiments relate generally to data science and data analysis, computer software and systems, including a subset of intermediary executable instructions constituting an communication interface between various software and/or hardware platforms, and, more specifically, to an automated application interface governance platform to automate development, maintenance, and governance functions for application interfaces, such as harmonizing, validating, and/or replicating application program interfaces (“APIs”). For example, a method may include identifying a subset of application interfaces, synthesizing a data structure for each application interface, analyzing the data structure against other data structures to identify duplicative portions among multiple data structures, substituting a reference to a location into a portion of multiple application interfaces. Optionally, the method may include evaluating interoperability of multiple application interfaces to validate collective operation of a subset of application interfaces.