Abstract: The invention concerns a method comprising: detecting strokes of digital ink input on a computing device in a free handwriting format; detecting a text block from said strokes; performing text recognition on each text line of said text block, including extracting text lines from the text block and generating model data that associate each stroke of the text block with a character, a word and a text line of the text block; normalizing each text line from the free handwriting format into a structured format to comply with a document pattern. The normalization may comprise for each text line: computing a transform function to transform said text line into the structured format; applying the transform function to the text line; and updating the model data of said text line based on the transform function.

Abstract: Methods, systems, and computer readable medium for personalizing an analytics user interface. The method includes generating a set of training data from received user interaction data, inputting the set of training data to a machine learning model to train the model, generating a set of user interest scores for the particular user that each indicate a user's interest in accessing information corresponding to a UI element of the application, determining, from the user interest scores, that the user is interested in a particular UI element that was not included in the initial UI and has at least a threshold score, dynamically modifying the initial UI to include the particular UI element, presenting the updated UI, monitoring further user interactions, updating the model based on the further user interactions, and modifying the updated UI based on the updated model.

Abstract: A method predicts custom fields from text. Transaction text is normalized from transaction data to generate normalized text. A field prediction and a type prediction are selected using prediction data and the normalized text. The prediction data is generated using a machine learning model trained to identify field predictions from free form text. The field prediction and the type prediction are presented to a client device. In response to user input from the client device, the transaction data is updated with the field prediction.

Abstract: The embodiments herein provide a framework for and specific implementations of machine learning (ML) analysis of incident, online chat, knowledgebase, skills, and perhaps other types of databases. The ML techniques described herein may include various forms of semantic analysis of textual information in these databases, such as clustering, term frequency, word embedding, paragraph embedding, and potentially other techniques. Advantageously, use of ML in the specific ways described herein can provide insights into this textual information that otherwise would be impossible to determine in an accurate or concise fashion.

Abstract: 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.

Abstract: A method may include extracting training tokens from a training corpus, generating training token features and training contextual features for each of the training tokens, training a probabilistic graphical model using classes, the training token features, and the training contextual features, extracting, from actual data, actual tokens including an actual token, generating actual token features and actual contextual features for each of the actual tokens, and classifying the actual token as a class using the probabilistic graphical model, the actual token features, and the actual contextual features.

Abstract: A computer-implemented method includes obtaining a plurality of textual records divided into clusters and a residual set of the textual records, where a machine learning (ML) clustering model has divided the plurality of textual records into the clusters based on a similarity metric. The method also includes receiving, from a client device, a particular textual record representing a query and determining, by way of the ML clustering model and based on the similarity metric, that the particular textual record does not fit into any of the clusters. The method additionally includes, in response to determining that the particular textual record does not fit into any of the clusters, adding the particular textual record to the residual set of the textual records. The method can additionally include identifying, by way of the ML clustering model, that the residual set of the textual records contains a further cluster.

Abstract: Classification of erroneous cell data includes performing unsupervised pre-training of a machine learning model to learn a bidirectional encoder representation of data cells, obtaining an initial training set, with labeled training examples that correlate observed cell data to correct cell data, for training the machine learning model to classify cell data, automatically augmenting the initial training set to produce an augmented training set, where the augmenting includes identifying patterns in the labeled training examples, generating transformation functions, and using the transformation functions, learning an augmentation strategy and automatically generating additional training examples correlating erroneous data values to correct data values, and training the machine learning model using the augmented training set.

Abstract: A method, apparatus and product for identifying data drifts.

Abstract: A method and apparatus for building and delivering a HTML (Hypertext Markup Language) construction representing digital content layout are disclosed herein. In one embodiment, the method includes constructing the digital content layout by selecting a delivery method, selecting subwidgets, and using the selected subwidgets to build and represent the digital content layout, wherein the delivery method is an inline, and wherein the subwidgets include images, texts, or videos. The method also includes integrating the digital content layout with a designated website. The method further includes delivering third-party content to the designated website. In addition, the method includes tracking interactive content on the designated website.

Abstract: Certain embodiments involve a template-based redesign of documents based on the contents of documents. For instance, a computing system selects a template for modifying an input document. To do so, the computing system uses a generative adversarial network to generate an interpolated layout image from an input layout image, which represents the input document, and a template layout image, which represents the selected template. The computing system matches the input element to an interpolated element from the interpolated layout image. The computing system generates an output document by, for example, modifying a layout of the input document to match the interpolated layout image, such as by fitting the input element into a shape of the interpolated element.

Abstract: Systems and methods for translation of a digital document to an equivalent interactive User Interface (UI), receive the digital document containing content; detect a selection and placement of a plurality of form fields, based at least in part on the content; construct a UI template and corresponding object graph, based at least in part on the selection and placement of each of the plurality of form fields; wherein the UI template represents the content of the digital document according to a predefined document schema; and wherein the object graph is defined by one or more dependencies and one or more validations associated with the selection and placement of each of the plurality of form fields; parse the UI template; and render the UI template as an interactive UI for distribution to an end user based at least in part on a predefined rendering library.

Abstract: Disclosed herein are systems and methods for interactive graphical user interfaces (GUIs) that users (e.g., medical professionals) can use to interact with modelled versions of brains and easily and intuitively analyze deep and/or lateral structures in the brain. A user can, for example, selectively view structures and their connectivity data (e.g., nodes and edges) relative to other structures and connectivity data over a representation of a particular patient's brain. Emphasis can be minimized for certain foreground nodes and edges (e.g., lateral structures) to make it easier for the user to focus on and analyze deeper structures that otherwise can be challenging to visualize and understand. A method can include overlaying deep and non-deep nodes on a representation of a brain, displaying the representation of the brain in a GUI, receiving user input indicating interest in focusing on one or more deep nodes, and taking an action based on the input.

Abstract: A machine learning process for use with a pharmacy benefits management system. The machine learning process identifies a first predicted set of drug benefit claims impacted by a pricing error, reprices a sample of the first predicted set of drug benefit claims to adjust for the error, and trains a predictive model as a function of the repriced sample. Based on the trained model, the machine learning process predicts a second predicted set of drug benefit claims impacted by the error and initiates automatic repricing.

Abstract: The present teaching relates to method, system, medium, and implementations for text processing. Upon receiving input data including a plurality of text strings, a plurality of manipulated text strings are generated for each of the plurality of training text strings by first applying a manipulation to each of at least one original token in the text string to generate a manipulated token, where the original token has a ground truth token label and then determining, with respect to each manipulated token, a ground truth action which, when applied to the manipulated token, yields the original token with the ground truth token label.

Abstract: An embodiment includes identifying, from among the plurality of digital content datasets, a set of candidate textual items based on relevance to a specified subtopic using one or more natural language processing techniques. The embodiment groups candidate textual items into a predetermined number of groups using relevance scores and feature vectors. The embodiment trains a pre-trained encoder-decoder model using a designated group of selected textual items, where the pre-trained encoder-decoder model is pretrained to generate textual content according to a particular style of writing. The embodiment generates, using the pre-trained encoder-decoder model, machine-authored textual content in the particular style of writing resulting in an article about the specified subtopic based on the designated group of selected textual items. The embodiment also includes transmitting the article to a remote web server as an update for a website.

Abstract: A method performed by an electronic device includes retrieving, from a first computing environment, a first set of structured documents that contains metadata for a first version of a workflow to be performed by a workflow engine, the first version of the workflow including a first set of workflow elements. The method further including retrieving, from a second computing environment, a second set of structured documents. The method determines whether a rule is to be applied, based on one but not both of the first and second sets of structured documents including at least one section for a workflow element having a type to which the rule applies, and generating a description of a change in the second version of the workflow relative to the first version of the workflow.

Abstract: This disclosure includes technologies for image processing, specifically for generating layout variations that are different but visually consistent with the input layout. The disclosed system determines a visual flow of the design blocks in the input layout, and then generates layout variations based on the visual flow. Advantageously, the disclosed technologies enable both novices and seasoned designers to efficiently create alternative layout variations, even in real-time with intricate designs.

Abstract: Mechanisms are provided for optimizing an automated machine learning (AutoML) operation to configure parameters of a machine learning model. AutoML logic is configured based on an initial default value and initial range for sampling of a parameter of the machine learning (ML) model and an initial AutoML process is executed on the ML model based on a plurality of datasets comprising a plurality of domains of data elements, utilizing the initially configured AutoML logic. For each domain, a cross-dataset default value and cross-dataset value range are derived from results of the execution of the initial AutoML process. For each domain, an entry is stored in a data structure, the entry storing the derived cross-dataset default value and cross-dataset value range for the domain. The AutoML logic performs a subsequent AutoML process on a new dataset based on one or more entries of the data structure.

Abstract: Methods, apparatuses, and computer program products are disclosed for dynamic user interfaces. An example computer-implemented method includes receiving a communication channel request from a first user and establishing a communication channel between a dynamic interface system and a device associated with the communication channel request. The computer-implemented method also includes determining that the first user and a second user are accessing the communication channel via the device and rendering a hybrid interface. The hybrid interface includes blended interface data based upon first user parameter data associated with the first user and second user parameter data associated with the second user, first personalized interface data based upon the first user parameter data, and second personalized interface data based upon the second user parameter data. The computer-implemented method may include obscuring the first personalized interface data and/or the second personalized interface data.