In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Systems and techniques are disclosed for improvement of machine learning systems based on enhanced training data. An example method includes providing a visual concurrent display of a set of images of features, the features requiring classification by a reviewing user. The user interface is provided to enable the reviewing user to assign classifications to the images, the user interface being configured to create, read, update, and/or delete classifications. The user interface is responsive to the user, with the user response indicating at least two images with a single classification. The user interface is updated to represent the single classification.

An embodiment of the present invention is directed to automated trust discretionary distribution decisions. The innovative system comprises a computer server configured to perform the steps of: receiving, via an electronic input, a trust beneficiary cash distribution request relating to a trust instrument; responsive to the trust beneficiary request, obtaining trust details relating to the trust instrument; applying, via a computer server, a trust decision predictor to the distribution request to generate a trust decision wherein the trust decision predictor considers a set of decision factors comprising the trust beneficiary cash distribution request, beneficiary details, trust details and applicability of governing restrictions; presenting, via an electronic interface, the trust decision; automatically executing the trust decision; and applying feedback data to refine and standardize the trust decision predictor.

A method including isolating a protocol language of a data set comprising a text structure representing data regarding a network communication procedure between a plurality of user devices, wherein the protocol language comprises a pattern for implementing the network communication procedure; generating a document from the data set, wherein the document includes a text structure, organizing, in light of the protocol language, the text structure into a natural language scheme; and detecting, using the natural language scheme, insights in the document.

A method including isolating a protocol language of a data set comprising a text structure representing data regarding a network communication procedure between a plurality of user devices, wherein the protocol language comprises a pattern for implementing the network communication procedure; generating a document from the data set, wherein the document includes a text structure, organizing, in light of the protocol language, the text structure into a natural language scheme; and detecting, using the natural language scheme, insights in the document.

A system for format-agnostic document ingestion including a document ingestion server and a database is disclosed. The server is configured to receive an image of a document comprising text in an unknown format, convert the image, using OCR, into a plurality of text elements a content, a size, and an absolute position. The server is also configured to retrieve data detectors from the database, each associated with a data type anticipated to be in the document, and comprising at least one identifier and direction, and at least one validation criteria. The server is also configured to identify a potential descriptor by comparing the content of each text element with the at least one identifier, and then determine if the text element pointed to by the data detector meets the validation criteria. Finally, the server is configured to associate the validated text element with the data detector, and store the content.

Systems and methods are described for processing ingested data in an asynchronous manner as the data is being ingested to detect potential anomalies. For example, one or more streaming data processors can convert data as the data is ingested into a comparable data structure, determine whether the comparable data structure should be assigned to an existing data pattern or a new data pattern, and optionally update a characteristic of the data pattern to which the comparable data structure is assigned. The streaming data processor(s) can perform these operations automatically in real-time or in periodic batches. Once one or more comparable data structures have been assigned to one or more data patterns, the streaming data processor(s) can analyze the comparable data structures assigned to a particular data pattern to determine whether any of the comparable data structures appear to be anomalous.

Disclosed herein are system, method, and computer program product embodiments for scrubbing anomalies from an expanding dataset. In an embodiment, a data sanitization system may determine whether data is anomalous to a set of data stored on a first blockchain. The data sanitization system may perform this determination using a first machine learning algorithm trained using the set of data. Upon determining that data is anomalous, the data sanitization system may publish the data in a second blockchain different from the first blockchain. The data sanitization system may monitor data of the second blockchain and apply a second machine learning algorithm to this data to identify a pattern of anomalous data. In response to identifying the pattern, the data sanitization system may publish the anomalous data of the second blockchain to the first blockchain.

An automotive sensor integration module including a plurality of sensors which differ in at least one of a sensing period or an output data format, and a signal processing unit, which simultaneously outputs, as sensing data, pieces of detection data respectively output from the plurality of sensors on the basis of the sensing period of any one of the plurality of sensors, determines whether each region of an outer cover corresponding to a location of each of the plurality of sensors is contaminated on the basis of the pieces of detection data, and outputs a determination result as contamination data.