Abstract: A system includes: a central controller configured to generate an energy management request; and one or more client controllers. Each of the one or more client controllers is configured to communicate with the central controller through a first communications network, each of the one or more client controllers is associated with a controllable service location, and each of the one or more client controllers is configured to communicate with a premises energy management system (PEMS) that commands one or more controllable energy resources at the controllable service location to operate according to a user specification and the energy management request.

Abstract: Determining job postings posted by one entity of an enterprise that are re-classifiable with respect to another entity of the enterprise. Computer systems and/or computer implemented methods use one or more reclassification analyses to compare an enterprise's active job postings with operations descriptions of entities of the job postings other than the entity that posted the job posting. A report and/or an alert can be generated when there is sufficient overlap between a job posting and an entity that did not post the job posting, indicating that the job posting is likely re-classifiable.

Abstract: Example systems and methods for AI capability assessment include query-based assessment of sequential decision making agents (SDMAs) in stochastic settings with minimal assumptions on SDMA internals.

Abstract: An Artificial Intelligence (AI) & Generative AI-driven cross-domain document analysis system enables accurate and consistent narratives across a longitudinal timeline for an entity regarding communications in different operational aspects. The document analysis and insight system includes an Artificial Intelligence (AI) powered Search Interface (AIPS) and an Advanced Intelligent Knowledge Engine (AIKE). The AIPS is configured to pre-process documents from structured and unstructured data sources to generate data taxonomies and custom synonym files. The AIKE generates a preliminary evaluation of the various Large Language Models (LLMs) and uses the data taxonomies and custom synonym files to generate prompts that are configured to address limitations of the various LLMs to obtain accurate replies to user requirements.

Abstract: Apparatus and methods for automatically removing customer data of a mobile device are disclosed according to various embodiments. In one example, a disclosed method comprises: detecting a mobile device connected to a slot; automatically determining whether the mobile device is valid based on data in a file system; upon determining that the mobile device is valid, automatically determining an operating system managing the mobile device; and automatically removing customer data of the mobile device based on the operating system.

Abstract: A system may include a plurality of sensors configured to sense a physical quantity and a calibration subsystem configured to perform a calibration comprising: comparing a measured characteristic from each of at least two sensors of the plurality of sensors to determine a sensitivity drift of at least one sensor of the plurality of sensors; based on the measured characteristics of the at least two sensors and stored reference characteristics for the at least two sensors, calculating a normalization factor; and applying the normalization factor to the measured characteristic of the at least one sensor to ensure sensitivity of the plurality of sensors relative to each other remains approximately constant.

Abstract: Recommendations of one or more authorizing components are provided to issuers and/or merchants for enhancing approval rates of payment processing requests. A server system receives a payment authorization request for a payment transaction between a cardholder and a merchant in real time. Payment transaction features associated with the payment transaction are identified based on the payment authorization request. A combination of one or more authorizing components to be applied to the payment transaction is predicted to obtain a product recommendation strategy for the payment transaction. The combination of one or more authorizing components is predicted based on a trained machine learning model and the payment transaction features. The payment authorization request and the product recommendation strategy are transmitted to an issuer associated with the cardholder.

Abstract: In one example aspect, a first content item is received. First metadata from the first content item; detecting a match between the first metadata and at least one predetermined metadata element in a knowledge repository pertaining to a target blockchain context; determining a first blockchain wallet address associated with the first content item; locating a second content item based on the first blockchain wallet address; extracting second metadata from the second content item; and updating the knowledge repository based on the second metadata extracted from the second content item.

Abstract: An example system includes a vehicle having a platform, a linear slide configured on the platform, a robotic retrieval system configured on the linear slide, a shelf configured adjacent to the linear slide and a control system that controls a reception of a first item and a second item by the robotic retrieval system and a placement of the first item and the second item, by the robotic retrieval system, on the shelf. A drone can be configured to retrieve the first item or the second item from the robotic retrieval system on the vehicle and deliver the first item or the second item to a destination location. The vehicle can thereby enable delivery of many items without the vehicle stopping or even driving to a specific destination location.

Abstract: A method for generating a social graph based on payment data is provided. The method includes extracting, from one or more databases, customer-based information for various entities and previous transaction information conducted by the various entities; generating data nodes for the various entities; determining a score for each of the data nodes with respect to a user based on the extracted customer-based information and the previous transaction information, in which the score indicating a proximity level with the user; generating a relationship model for the various data nodes with respect to the user based on the determined score; and identifying, based on a search performed for an input provided by the user on a user interface, one or more data nodes as potential candidates for conducting a transaction; and providing the potential candidates based on the proximity level with respect to the user.

Abstract: The disclosure describes embodiments of systems, methods, and non-transitory computer readable storage media that securely generates an unscrubbed Metro2 file. In particular, the disclosed systems can receive data from multiple data sources that relates to a user account. In some cases, the disclosed systems can merge the data related to the user account by utilizing a transformation framework to generate a data staging table. In one or more embodiments, the disclosed systems, based on the data stagging table can generate a scrubbed Metro2 file that does not include personal identification information. In some cases, the disclosed systems can validate the scrubbed Metro2 file by analyzing the data related to the user account. Subsequently, in some instances, based on validating the scrubbed Metro2 file, the disclosed systems can add the personal identification information to the scrubbed Metro2 file and generate an unscrubbed Metro2 file.

Abstract: A disclosed optical system comprises a repeater disposed between a first span and a second span of an optical cable and a node receiving an optical signal from the first span and transmitting a reflection to the first span. The node comprises a transmitter coupled to the first span to transmit the optical signal, transmit pulses having an RF modulated tone, and provide a local reflection; a receiver to receive the local reflection and the pulse reflection and passing a filtered spectrum; and a DSP to: determine a first RF phase of the local reflection and a second RF phase of the pulse reflection; determine a second RF phase; determine a first span seismic pressure based on the first RF phase and determine a second span seismic pressure based on the second RF phase.

Abstract: Disclosed are systems and methods for discovering one or more computing assets associated with primary identification data. The systems and methods comprise a series of processes and steps to discover an organization's external attack surface. The processes and steps include building a unique external attack surface management catalog to be used as a configuration value as a first step of discovering unknown internet-facing assets of an organization. Then the processes and steps include using the unique external attack surface management catalog in combination with open-source reconnaissance and proprietary scanners to determine the external attack surface of the organization. The disclosed systems and methods then uniquely present the acquired relevant data to users using a single display screen.

Abstract: A system for optimizing telemetry volume generated for an application product includes: a volume calculation service and a configuration service coupled to a client device via a network connection. The volume calculation service receiving identification of a particular event that occurs during execution of the application product by the client and calculating an optimized sampling rate for that event in telemetry produced by the client, the optimized sampling rate calculated based on reducing an overall volume of telemetry while still maintaining tracking of the particular event within the telemetry. The configuration service generating a configuration for the client device, the configuration service configuring the client to use the optimized sampling rate to produce telemetry for the event during execution of the application product by the client device.

Abstract: Disclosed is an improved approach to implement an improved approach to resolve errors with software automations. Instead of crashing when an error is encountered, the approach provides a question to the user, and uses the answer to attempt a retry for the step that was subject to the error.

Abstract: In one aspect, a capacitor or network of capacitors is/are provided for vertical power delivery in a package where the capacitor(s) is/are embedded in or forms the entirety of the package substrate core. In a second aspect, a plurality of thin-film capacitor structures are provided for implementing vertical power delivery in a package. In a third aspect a method is provided for fabricating hermetically sealed thin-film capacitors.

Abstract: Systems, apparatuses and methods provide technology for model generation with intermediate stage caching and re-use, including generating, via a model pipeline, a multi-level set of intermediate stages for a model, caching each of the set of intermediate stages, and responsive to a change in the model pipeline, regenerating an executable for the model using a first one of the cached intermediate stages to bypass regeneration of at least one of the intermediate stages. The multi-level set of intermediate stages can correspond to a hierarchy of processing stages in the model pipeline, where using the first one of the cached intermediate stages results in bypassing regeneration of a corresponding intermediate stage and of all intermediate stages preceding the corresponding intermediate stage in the hierarchy. Further, regenerating an executable for the model can include regenerating one or more intermediate stages following the corresponding intermediate stage in the hierarchy.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for initiating presentation of an interactive user interface. The techniques may include receiving one or more candidate prediction-based actions and generating a plurality of causal risk-based impact scores with respect to a candidate prediction-based action. The techniques include generating a plurality of causal quality-based impact scores and an action sequence for a plurality of evaluation entities and generating a causal net impact score based on (i) an aggregation of the plurality of causal risk-based impact scores and the plurality of causal quality-based impact scores and (ii) a sequence impact metric corresponding to the action sequence. The techniques include generating a sequence ranking for the action sequence and initiating a presentation of an interactive user interface reflective of the action sequence and the sequence ranking.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for forecasting holistic, categorical improvement predictions. The techniques may include generating a predictive quality performance measure based on (i) an evaluation entity of a plurality of evaluation entities within an entity group and (ii) a quality metric of a plurality of quality metrics corresponding to a categorical ranking scheme for the entity group. The techniques include using an action-specific causal inference model to generate a metric-specific predictive impact measure. The techniques include generating a metric-level categorical improvement prediction and a categorical improvement prediction for the entity group with respect to the categorical ranking scheme based on a weighted aggregation of the metric-level categorical improvement prediction and a plurality of metric-level categorical improvement predictions respectively corresponding the plurality of quality metrics.

Abstract: Various embodiments of the present disclosure provide computer forecasting techniques for forecasting holistic, causal risk-based scores. The techniques may include generating a predictive risk-based opportunity score for an evaluation entity based on (i) a plurality of engagement scores and (ii) a plurality of predictive risk scores respectively corresponding to a plurality of predictive entities within an entity cohort associated with the evaluation entity. Using action-specific causal inference models, a predictive impact score of a prediction-based action on the evaluation entity is generated and used to generate a causal gap closure score for the evaluation entity based on a gap closure rate associated with the evaluation entity. The techniques include generating a causal risk-based impact score for the prediction-based action and the evaluation entity based on the predictive risk-based opportunity score, the predictive impact score, and a predictive improvement measure.