Abstract: In some implementations, a device may receive data associated with a set of taxonomies, wherein a taxonomy, of the set of taxonomies, represents a classification of a set of relationships of data entries of the data. The device may integrate the set of taxonomies to generate a meta-taxonomy of the data. The device may generate a data graph of the data based on integrating the set of taxonomies to generate the meta-taxonomy, wherein the data graph is based on a graph-based search model that is associated with at least one of: taxonomy-based filtering, metadata attribute prioritization, or semantic matching. The device may store the data graph.

Abstract: A device may process, using a first model and based on an entity-specific task description that is included in entity role data and that is associated with a role, the entity role data to identify a task associated with the role. The device may determine, using a second model and based on the entity-specific task description and standardized descriptions of automation-capable tasks, a task automation score associated with the task. The device may determine, using a third model and based on a characteristic of the task and mappings of standardized characteristics to a plurality of automation categories, a set of automation category scores for the task. The device may classify, based on the set of automation category scores, the task as being associated with a particular automation category, and may perform an action associated with the task automation score and the particular automation category.

Abstract: Customer relationship management (“CRM”) implemented in a computer system, including parsing, by a parsing engine of the computer system into parsed triples of a description logic, words of a CRM event from an incoming stream of CRM events, the CRM event characterized by an event type, the stream implemented in a CRM application of the computer system; and inferring, by an inference engine from the parsed triples according to inference rules specific to the event type, inferred triples.

Abstract: A method and system for emotion recognition and forecasting are disclosed. The method may include obtaining an audio data of a conversation involving a plurality of speakers and identifying a plurality of turns of the conversation from the plurality of utterances. The method may further include extracting audio embedding features from the plurality of turns, obtaining a plurality of text segments associated with the audio data, extracting text embedding features from the plurality of text segments, obtaining and concatenating speaker embedding features associated with the audio data, obtaining and concatenating a plurality of emotion features corresponding to the plurality of turns. The method further comprises executing a tree-based prediction model to predict emotion features of the plurality of speakers for a subsequent turn of the ongoing conversation based on the audio embedding features, text embedding features, the concatenated speaker embedding features, and the concatenated emotion features.

Abstract: A device may identify unique segments within data objects, of an object corpus stored in a data structure, as elements, and may generate an embedding space based on unique elements and mappings of the data objects to embeddings. The device may estimate semantic proximities among the data objects based on the mappings, and may build a semantic cohesion network among the data objects based on the semantic proximities. The device may identify semantically cohesive data clusters in the semantic cohesion network, and may sort the data objects in the semantically cohesive data clusters. The device may determine, from the semantically cohesive and sorted data clusters, a home data cluster for a new data object, and may store bookkeeping details of the new data object in the data structure based on the new data object being semantically similar to the data object in the home data cluster.

Abstract: Methods, systems, and apparatuses, including computer programs encoded on computer-storage media, for prioritizing test cases. Processes may include obtaining test artifacts that were generated based on testing one or more legacy versions of a software application using multiple test cases, generating a risk index based at least on the test artifacts that were generated based on testing the one or more legacy versions of the software application using the multiple test cases, and training an ensemble model that is configured to identify likely unnecessary or redundant test cases in connection with in testing an updated version of the software application, based at least on the risk index.

Abstract: In some implementations, a system may receive a submission associated with an innovation associated with an entity. The system may analyze, using a first machine learning model, the submission to identify a classification associated with the innovation. The system may analyze, using a second machine learning model in association with the classification, content of the submission to identify a characteristic of the innovation. The system may determine, using a third machine learning model and based on the characteristic, a ranking of the innovation relative to individual innovations in the subset of innovations. The system may determine, using a fourth machine learning model, an impact score associated with the innovation. The system may perform, based on the impact score satisfying a threshold, an action associated with a project involving the innovation.

Abstract: A model retraining tool is provided for utilizing a knowledge graph to retrain analytical models used in production. The model retraining tool retrains the analytical models to improve performance of the analytical models in an efficient and resource conserving manner.

Abstract: The present disclosure describes a computer-implemented method for detecting anomalies during lot production, wherein the products within a production lot are processed according to a sequence of steps that include manufacturing steps and one or more quality control steps interspersed among the manufacturing steps, the method comprising: obtaining process quality inspection data from each of the one or more quality control steps for a first production lot; obtaining product characteristics data for the products in the first production lot after the final step in the sequence; training a Gaussian process regression model using the process quality inspection data and the product characteristics data from the first production lot; generating a predictive distribution of the product characteristics data using the Gaussian process regression model that uses a bathtub kernel function; obtaining process quality inspection data from each of the quality control steps for a second production lot; identifying anomalies

Abstract: Methods, systems and apparatus for implementing a secure quantum swap operation on a first and second qubit. In one aspect a method includes establishing, by a first party and with a second party, an agreement to use a secure swap protocol; performing the quantum swap operation, comprising, for each two-qubit gate included in the quantum swap operation: performing, by the first party and according to the secure swap protocol, a respective preceding quantum gate cipher on the first qubit; performing, by the first party and the second party, the two-qubit gate on the first qubit and the second qubit; and performing, by the first party and according to the secure swap protocol, a respective succeeding quantum gate cipher on the first qubit. The preceding and succeeding quantum gate ciphers comprise computational bases that anti-commute with a computational basis of the two-qubit gate across a second axis of the Bloch sphere.

Abstract: Systems, methods, devices, and other techniques for placing and rendering virtual objects in three-dimensional environments. The techniques include providing, by a device, a view of an environment of a first user. A first computing system associated with the first user receives an instruction to display, within the view of the environment of the first user, a virtual marker at a specified position of the environment of the first user, the specified position derived from a second user's interaction with a three-dimensional (3D) model of at least a portion of the environment of the first user. The device displays, within the view of the environment of the first user, the virtual marker at the specified position of the environment of the first user.

Abstract: Electrical input devices can be produced using a multi-material 3D-printing process. The electrical input devices can include a non-conductive material portion and a conductive material portion. The non-conductive and conductive material portions are integrally formed during a single 3D-printing process. Deformation of the electrical input devices cause an electrical variance of the conductive material portion that is responsive to the deformation. Some electrical input devices described provide digital responses, and some electrical input devices described provide analog responses. The described techniques can be used to manufacture complex finished devices in a single 3D-print run, and, in some examples, without the need for post-processing or assembly.

Abstract: A device may receive an application for transforming legacy applications into low-code/no-code applications to be managed by a low-code/no-code platform, and may execute the application for a legacy application of the legacy applications. The device may process the legacy application, with a machine learning model, to identify one or more components of the legacy application to be managed by the low-code/no-code platform, and may transform the one or more components into one or more transformed components to be managed by the low-code/no-code platform. The device may implement the one or more transformed components in the legacy application to generate a transformed legacy application, and may perform one or more actions based on the transformed legacy application.

Abstract: A device may receive input data associated with a legal regulation, and may process the input data to generate a record that includes: the input data in a knowledge representation format and a semantic representation format, data identifying a feature, data identifying an industry classification, or data identifying an entity of interest. The device may process the record, with machine learning models, to determine output data that includes: data indicating that the legal regulation is inconsistent, data indicating that the legal regulation is outdated, data indicating a sentiment for the legal regulation, data indicating a prescriptive nature of the legal regulation, data indicating a complexity of the legal regulation, data indicating a misrepresentation in the legal regulation, data indicating a compliance burden associated with the legal regulation, or data indicating an industry performance impact of the legal regulation. The device may perform actions based on the output data.

Abstract: A system may receive enterprise information associated with a client enterprise. The system may select, using an industry analysis model, a set of queries associated with obtaining status information that is associated with a technology profile of the client enterprise. The system may generate client data that is associated with the enterprise information and the status information. The system may convert, using a matrix factorization technique, the client data associated with the client enterprise to a client matrix. The system may convert, using the matrix factorization technique, reference data associated with reference enterprises to a reference matrix. The system may determine, based on a comparison of the client matrix and the reference matrix, a set of scores associated with technology metrics of the technology profile. The system may perform an action associated with the client enterprise based on the set of scores.

Abstract: An upper-extremity prosthetic is adapted to engage with an athletic ball. The prosthetic includes one or more springs that provide energy return as a user is throwing the ball using the prosthetic. The springs can have a conductivity that changes in relation to an amount of strain or deformation of the spring. The change in conductivity can be used to provide haptic feedback to the user so the user can sense the amount of force being applied to throw the ball. In some embodiments, the springs are made by a multi-material 3D printing (additive manufacturing) process and include a first material that is electrically non-conductive and a second material that electrically conductive. In some embodiments, the prosthetic also includes one or more cantilevered springs that are also adapted to engage with the ball and to provide energy return while throwing the ball.

Abstract: Implementations include determining a set of components within the connected vehicle ecosystem, components within the set of components representing at least one layer within the connected vehicle ecosystem, for each component in the set of components: providing a set of facts representative of the respective component, and providing a component digital twin using the set of facts, defining a set of digital twins including digital twins of components in the set of components, generating, using the set of digital twins, at least one AAG representative of potential lateral movement between components of the at least one layer within the connected vehicle ecosystem, the at least one AAG representing a contextual digital twin of components operating within the connected vehicle ecosystem, and evaluating the connected vehicle ecosystem using the at least one AAG.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: In some implementations, a device may receive prescription information associated with a medication in a container. The device may cause a camera to capture first image data associated with the medication while the medication is in the container and the container is positioned on a receptacle. The device may cause an adjusting device to reposition the container on the receptacle. The device may cause the camera to capture second image data associated with the medication while the medication is in the container. The device may process, via a neural network, the first image data and the second image data to identify the medication based on depictions of individual units of the medication included in the first image data and the second image data. The device may verify the medication based on the prescription information and an identifier of the medication provided by the neural network.

Abstract: A device may receive log data from application logs associated with applications, service logs associated with services, and server logs associated with server devices. The device may store the log data. The device may perform natural language processing on the log data to convert the log data into event data identifying events associated with categories. The device may process the event data, with a first machine learning model, to identify patterns in the event data and to generate an alert based on the patterns. The device may process the event data, with a second machine learning model, to generate a correlation matrix for the event data and to predict an event based on the correlation matrix. The device may process the event data, with a third machine learning model, to classify the event data based on the categories and to generate a recommendation based on classifying the event data.