Abstract: Systems and methods for health and body simulations in order to predict numerous physiological parameters in a subject or a population of subjects based on the input of limited physiological data.

Abstract: A system for generating risk assessments based on a data representing a plurality of statements and data representing corroborating evidence is provided. The system receives data representing a plurality of statements and data representing corroborating evidence. The system applies one or more integrity analysis models to the first data and the second data in order to generate an assessment of a risk that one or more of the plurality of statements represents a material misstatement. A system for generating an assessment of faithfulness of data is provided. The system compared data representing a statement to data representing corroborating evidence, and generates a similarity metric representing their similarity. Based on the similarity metric, the system generates an output representing an assessment of faithfulness of the first data set.

Abstract: Systems and methods for adjudicating AI-augmented automated analysis of documents in order to quickly and efficiently make various adjudications based on the documents are provided, including adjudications as to whether the documents represent underlying data that meets one or more predefined or dynamically-determined criteria. Criteria for adjudication may include commercial-substance criteria, related-party-transaction criteria, and/or collectability criteria. A system may receive a plurality of documents and generate a plurality of feature vectors by applying natural language processing techniques. The system may apply one or more classification models to the plurality of feature vectors to generate output data classifying each of the feature vectors. The system may identify, for each feature vector, a subset of closest matching prior feature vectors.

Abstract: Systems and methods for determining whether an electronic document constitutes vouching evidence is provided. The system may receive ERP item data and generate hypothesis data based thereon, and may receive electronic document data and extract ERP information therefrom. The system may then apply one or more models to compare the hypothesis data to the extracted ERP information to determine whether the electronic document constitutes vouching evidence for the ERP item. Systems and methods for verifying an assertion against a source document are provided. The system may receive first data indicating an unverified assertion and second data comprising a plurality of source documents. The system may apply one or more extraction models to extract a set of key data from the plurality of source documents and may apply one or more matching models to compare the first data to the set of key data to determine whether vouching criteria are met.

Abstract: Systems and methods for automated document processing for use in AI-augmented auditing platforms are provided. A system for determining the composition of document bundles extracts substantive content information and metadata information from a document bundle and generates, based on the extracted information regarding a composition of the document bundle. A system for validating signatures in documents extracts data representing a spatial location for respective signatures and generates a confidence level for respective signatures, and determines, based on location and confidence level, whether signature criteria are met. A system for extracting information from documents applies a set of data conversion processing steps to a plurality received documents to generate structured data, and then applies a set of knowledge-based modeling processing steps to the structured data to generating output data extracted from the plurality of electronic documents.

Abstract: Systems and methods for providing explainability for processing data through multiple layers are provided. An input layer is configured to receive an evidence data set comprising a plurality of evidence items, apply evidence processing models to the evidence data set to generate evidence understanding data, and generate input-layer explainability data, wherein the input-layer explainability data represents information about the processing of the evidence data set by the input layer. A presentation layer is configured to receive data (the evidence understanding data and/or data generated based on the evidence understanding data), apply one or more presentation generation models to the received data to generate presentation data, and generate presentation-layer explainability data for presentation to the user, wherein the presentation-layer explainability data represents information about the processing of the received data set by the presentation layer.

Abstract: Techniques for determining levels of interactions amongst entities in a group are provided. A plurality of mobile electronic devices detect signal data indicating when pairs of devices are proximate to one another. A receiver receives the detected signal data, and a data structure representing a contact network is generated based on the received signal data. Based on the contact network, a first metric is generated comprising a quantification of an average number of other entities in the contact network with which an entity in the contact network will have interactions in a predetermined amount of time. Based on the contact network, a second metric is generated comprising a quotient comprising of a size of a component of the contact network divided by an overall network size of the contact network. Based on the first and second metric, a level of interaction amongst entities in the group is determined.

Abstract: Described are methods and systems for calibrating simulation models to generate digital twins for physical entities. In some embodiments, a method includes receiving a plurality of datasets for a plurality of corresponding physical entities. A calibration request is enqueued to a calibration requests queue for each received dataset and includes information indicating a dataset and a corresponding physical entity. A plurality of calibration engines and a plurality of corresponding simulation clusters for generating a plurality of calibration results for a plurality of calibration requests dequeued from the calibration requests queue can be deployed.

Abstract: Described herein is a regulatory parser that downloads and efficiently processes regulatory documents. The regulatory documents may be from different sources and may have different formats. The regulatory parser parses all of the text in the regulatory documents and converts into a predetermined, single format for downstream applications. The text is organized and stored in a structured tree, organized into one or more hierarchies with nodes storing segments of text from a regulatory document. In some embodiments, each node in the regulatory tree may represent a segment of text. Partitioning the text of a regulatory document into segments of text may make the storage and querying of the regulatory documents more manageable. The organization and structure of the structured tree may reduce the times and resources needed for accessing and searching for a regulatory citation. The structured tree may allow a user to manipulate a regulatory document or text.

Abstract: Described herein is a machine-learning model that categorizes and classifies regulatory text and methods for operation thereof. The machine-learning model may receive raw data. The raw data may be data in a file that includes a list of text examples (e.g., leaf node citation texts). One or more datasets may be annotated. A training, validation, and test dataset may be generated. The machine-learning model is used to determine one or more predictions regarding the category and classification of input data. The training dataset is used to train the machine-learning model, the validation dataset is used to tune the hyper parameters of the model, and the test dataset is used to evaluate its performance. The prediction(s) are stored or sent to one or more downstream applications.

Abstract: Embodiments described herein generate proximal groupings of wireless signals based upon the temporal persistence and spatial proximity of the wireless signals as observed by a plurality of observer devices. For example, a first observer device may observe a first set of wireless signals at a first timepoint and a second observer device may observe a second set of wireless signals at a second timepoint. The first observer device may again observe a third set of wireless signals at a third timepoint. Based upon these observations, a server may generate a proximal grouping a wireless signals containing a subset of the first, second, third of wireless signals based upon temporal persistence and spatial proximity. Temporal persistence may be based upon the repeated observations of the subset of wireless signals across different timepoints and the spatial proximity may be based upon the proximity of locations of the observer devices.

Abstract: A method for data ingestion for a data visualization platform comprises receiving a plurality of data sets, generating and storing a merged data set based on the plurality of received data sets, and, receiving, via a graphical user interface, a first input comprising an instruction to perform a data standardization operation. The method comprises, in response to receiving the first input, applying a data standardization operation to the merged data set to process the merged data set to generate a standardized data set. The method comprises receiving, via the interface, a second input comprising an instruction to perform a data analytics operation, and responsively applying the data analytics operation to the standardized data set to generate insights data. The method includes receiving, via the interface, a third input comprising an instruction to perform a data visualization operation, and responsively generating one or more data visualizations based on the insights data.

Abstract: Systems, methods, and graphical user interfaces (GUIs) for ingesting and enriching data regarding a plurality of entities are provided. A first data set comprising company data and a second data set comprising customer data are ingested. The first data set is processed to generate a processed data set. The first data set may be processed by applying an entity matching technique, wherein one or more data elements are generated based on whether an entity of the first data set and an entity of the second data set are commonly associated. The first data set may additionally or alternatively be processed by applying a statistical matching technique, wherein one or more predicted data elements are generated based on similarity between an entity of the first data set and one or more entities of the second data set.

Abstract: Systems, methods, and graphical user interfaces (GUIs) for visualizing price-book data are provided. Product identifiers and associated price data points are ingested from a plurality of sources and stored in accordance with a product hierarchy. The product identifiers are furthermore organized into a plurality of price books that have a nested relationship with one another. A GUI is provided by which a user may execute an instruction to visualize a portion of the stored data, wherein the input comprises an indication of a subset of the plurality of product identifiers comprising an indication of one or more of the price books. In response to detecting the input, the system generates and displays a visualization based at least in part on the input. Users may further use the GUI to modify one or more price data points in a price book, including simultaneously modifying price data points across multiple price books.

Abstract: A language understanding system is configured to process utterances to predict intents of users, including by suggesting utterances and intents based on searches performed by one or more microservices. A central service is configured to receive inputs/queries from a user, to communicate with a plurality of language processing microservices, and to return a response to the user. The microservices may be configured to apply respective search algorithms comparing the input to respective data sources such as databases, indexes, or knowledge graphs. The microservices may rate utterances and/or entities in the respective data sources with respect to the input. The one or more microservices may generate a ranked list and return the ranked list to the central service. The central service may then apply a secondary rating/ranking algorithm in order to select one or more predicted utterances and/or entities to return to the user based on the initial user input.

Abstract: Systems and methods for securely storing data for efficient access by cloud-based computing instances is provided. In one or more examples, a computing hub can receive one or more access requests to data stored within a persistent data storage computing resources that in connected to the computing hub. The computing hub can be configured to determine if the access request is from an authorized computing resource, and can then generate one or more tokens that provide access to the computing resource. The one or more tokens can include information regarding the IP address of the requesting cloud-based computing resource, and each time that the cloud-based computing resource uses the token to request access to the stored data, the computing hub can check the IP address of the computing resource against the IP address indicated on the token to decide whether or not to grant access to the data.

Abstract: Methods and systems for artificial intelligence (AI)-assisted document annotation and training of machine learning-based models for document data extraction are described. The methods and systems described herein take advantage of a continuous machine learning approach to create document processing pipelines that provide accurate and efficient data extraction from documents that include structured text, semi-structured text, unstructured text, or any combination thereof.

Abstract: The present disclosure relates generally to natural language understanding.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprising an analytic server, which builds a pipeline of five classifiers to validate whether an electronic device is operated by a human user. The server monitors wireless signals detected by different electronic devices by triggering a signal scanning function on the electronic devices. Based on the wireless signals reported by different electronic devices, the server generates a set of signal clusters. The server applies the pipeline of five classifiers on the wireless signals detected by an electronic device by exploiting the signal clusters included in the wireless signals. If any of the classifiers determines the electronic device is a legitimate human device, the server validates the electronic device; otherwise, the server applies the next classifier. If after a number of classifiers, none of the classifiers validates the device usage, the server determines that the device is not operated by a human.

Abstract: Systems, methods, and graphical user interfaces for managing upskilling content are provided. An upskilling system ingests information regarding learners and regarding roles, including information regarding current skills (and skill levels) for individual learners and target skills (and skill levels) for roles. Skill gaps for learners are then identified based on the difference between current skills and target skills for a learner's role. The system selects upskilling content based on skill information associated with various pieces of upskilling content, wherein the selected content is selected such that the learner may reduce or eliminate a skill gap by consuming the selected content. The system associates upskilling content with role profiles for roles and/or with user profiles for learners. The system provides access to upskilling content, via a graphical user interface, for learners, and monitors consumption of upskilling content by learners.