Abstract: Disclosed herein are methods and systems for generating a merged dataset, comprising: accessing data comprising a core dataset and an additional dataset; identifying a plurality of common attributes between the core dataset and the additional dataset; determining a plurality of similarity scores between an inquiring entity in the core dataset and a plurality of candidate entities in the additional dataset, including, for each candidate entity of the plurality of candidate entities: calculating a similarity score for the candidate entity based at least in part on a distance-based score and a weight influence score; selecting one or more matches for the inquiring entity in the core dataset from the plurality of candidate entities in the additional dataset based at least in part on the plurality of similarity scores; and generating the merged dataset by adding the one or more selected matches for the inquiring entity to the core dataset.

Abstract: A first step in training a deep learning model may include generating data representing a plurality of historical episodes. Each historical episode may be divided into a sequence of time units, and historical information may be associated with each time unit. Next, for each historical episode of the plurality of episodes, a respective training action sequence may be generated using an evolutionary algorithm. A training data set comprising a plurality of training data points may then be generated. Each of the plurality of training data points may comprise an action extracted from a training action sequence generated by the evolutionary algorithm. The deep learning model may be trained using training data set to generate future actions to be executed at current or future time units.

Abstract: Provided herein are methods and systems for generating a chatbot. In response to a user selection of a content repository, the method can include generating a hierarchy map based on the selected content repository, translating the hierarchy map into a series of chatbot prompts, and generating a chatbot based on the series of chatbot prompts. The chatbot can be configured to guide the user to selected content within the content repository.

Abstract: The present disclosure relates generally to storing computer models, and more specifically to a platform for achieving replicability of a computer model (e.g., a trained machine-learning algorithm) by storing and providing access to data associated with the computer model using an immutable and decentralized ledger system (e.g., a blockchain ledger) and a distributed database. An exemplary computer-enabled method for storing a computer model, the method comprises: receiving data associated with the computer model; generating one or more asset files based on the data associated with the computer model; generating one or more hash values corresponding to the one or more asset files; generating one or more of location trackers corresponding to the one or more asset files; generating a ledger entry comprising the one or more hash values and the one or more location trackers; and adding the ledger entry to a blockchain ledger.

Abstract: A system for generating digital flowcharts is provided. The system receives sketch image data comprising a plurality of shapes and text, and processes the sketch image data to generate flowchart data by applying a first model configured to generate shape data, applying a second model configured to generate text data, and generating linking data that associates shape data and text data. The system may generate and display a visualization of the flowchart data. The system may map the flowchart data to a region of a presentation slide and display a visualization of the flowchart data on the presentation slide.

Abstract: Systems and methods for generating and utilizing an interactive causal loop diagram using a causal loop designer are provided. In one or more examples, a computer-implemented method for creating a causal loop diagram comprising visually emphasized elements can comprise displaying a first element and a second element, wherein the first element and the second element comprise visually emphasizable elements. In response to receiving a user command to connect the first element to the second element, the method can comprise displaying a connection between the first element and the second element, wherein the connection comprises a visually emphasizable element. In one or more examples, the method comprises displaying a causal loop diagram comprising the first element, the second element, and the connection. In response to a user activating a visual emphasis tool, the method can comprise visually emphasizing one or more of the visually emphasizable elements.

Abstract: Provided are asset tracking systems for determining a location of an asset, comprising a mobile device comprising one or more detection antennas and a transmitter, and a device associated with an asset and emitting Bluetooth low energy signals, wherein the system is configured to: detect a plurality of electromagnetic signals of the environment and one or more Bluetooth low energy signals from the one or more Bluetooth low energy devices; generate a signal profile based on the plurality of electromagnetic signals; determine, based on a comparison of the signal profile to data of signal profiles at a plurality of locations in the environment, a mobile-device location in the environment; and determine, based on the mobile-device location and based on the one or more Bluetooth low energy signals, one or more asset locations in the environment.

Abstract: Systems and methods for secure and accountable execution of computer scripts are disclosed. A system for validating an execution of a set of computer instructions may be configured to receive a first cryptographic hash, the first cryptographic hash corresponding to the set of computer instructions, to receive a second cryptographic hash, the second cryptographic hash corresponding to a runtime utility, wherein the runtime utility is configured to execute the set of computer instructions, to generate a ledger entry comprising the first cryptographic hash, the second cryptographic hash, and an indicator of success, and to add the ledger entry to a blockchain ledger, wherein the blockchain ledger is configured to receive the ledger entry from an authenticated node.

Abstract: Described are system, method, and computer-program product embodiments for developing an object detection model. The object detection model may detect a physical object in an image of a real world environment. A system can automatically generate a plurality of synthetic images. The synthetic images can be generated by randomly selecting parameters of the environmental features, camera intrinsics, and a target object. The system may automatically annotate the synthetic images to identify the target object. In some embodiments, the annotations can include information about the target object determined at the time the synthetic images are generated. The object detection model can be trained to detect the physical object using the annotated synthetic images. The trained object detection model can be validated and tested using at least one image of a real world environment. The image(s) of the real world environment may or may not include the physical object.

Abstract: The present disclosure relates generally to storing computer models, and more specifically to a platform for achieving replicability of a computer model (e.g., a trained machine-learning algorithm) by storing and providing access to data associated with the computer model using an immutable and decentralized ledger system (e.g., a blockchain ledger) and a distributed database. An exemplary computer-enabled method for storing a computer model, the method comprises: receiving data associated with the computer model; generating one or more asset files based on the data associated with the computer model; generating one or more hash values corresponding to the one or more asset files; generating one or more of location trackers corresponding to the one or more asset files; generating a ledger entry comprising the one or more hash values and the one or more location trackers; and adding the ledger entry to a blockchain ledger.

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: An indoor geolocation system for determining a location in three-dimensional space includes a plurality of base stations and a mobile device movable about an indoor environment in three dimensions. The mobile device detects electromagnetic signals in the indoor environment emitted by devices other than the base stations, and generates a signal profile based on the signals. The mobile device transmits the signal profile to one or more of the base stations, which forward the signal profile to a remote server. The system determines a location of the in three-dimensional space of the mobile device by comparing the signal profile to data regarding signal profiles at a plurality of locations in the indoor environment. The data regarding signal profiles in the indoor environment may have been captured by a detection device other than the mobile device at a time prior to the detection of the electromagnetic signals by the mobile device.

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: 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: Methods and systems for artificial intelligence (Al)-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: Systems and methods for processing natural language inputs to determine user intents using an insights repository are provided. An insights repository system is configured to build an insights repository as a data structure representing a plurality of entities and relationships among those various entities. The insights repository system may receive information from various sources via an event stream, and may process the information using event rules. Based on the application of the event rules, the system may configure an insights repository data structure representing various entities, relationships between various entities, and the strengths of relationships between various entities. After the insights repository is created, consumers may execute queries against the insights repository.

Abstract: Systems and methods are provided for using a structured data database and for exchanging electronic files containing unstructured or partially structured data. A system stores first structured data in a database, wherein the structured data represents a property. The system generates an electronic file, including by (a) storing unstructured data in the electronic file, wherein the unstructured data causes a representation of the property to be rendered on a face of a document represented by the file and (b) storing an identifier in the electronic file indicating a location in the database at which the first structured data is stored. The file is transmitted outside the system. Upon receipt of a copy of the file, the system reads the identifier from the copy of the received file and accesses the database location indicated by the identifier read from the received file.

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: 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: 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.