Abstract: Improved artificial intelligence computer systems actively and/or passively provide end-users with information from firm data systems to help the end-user perform the end-user's job functions. In a passive implementation, the system can prioritize emails for a user, craft consistent responses to multiple email inquiries on the same topic, and/or craft responses whose content is drawn from a library of electronic documents. In an active implementation, the system comprises AI agents associated with individual databases of the system, where the AI agents are tuned to retrieve data from their associated database in response to a query from the end-user.

Abstract: Examples are described herein for monitoring power source usage of a computing device, including a battery and another power source, over time. In various examples, time periods of this monitored time may be added to a time count based on their immediately previous or immediately subsequent time periods satisfying a condition. The time count may be used to track periods of time during which certain power source usage factors are present that may lead to or may indicate the presence of battery swelling. After a time period is added to the time count, the updated time count can be compared to a time count threshold, and a user of the computing device may be warned based on the comparison. The threshold and warning may be selected to warn a user of potential present or future swelling of the battery.

Abstract: An example of an apparatus including a network interface to receive enrollment data from a device, wherein the device is identified by a device identifier. The apparatus further includes a memory storage unit connected to the network interface. The memory storage unit receives device history data associated with the device identifier while the device is enrolled. The memory storage unit is also to maintain a database to store the device history data associated with the device identifier. The apparatus also includes a processor to receive a de-enrollment command associated with the device via the network interface. The de-enrollment command causes the processor to modify the device identifier stored in the database.

Abstract: Examples described herein include systems and methods for contextually providing automated device enrollment into a management system. A management application on a user device can receive network settings for connecting to a local server. The network settings can be preconfigured by an administrator. The management application can cause the user device to send an enrollment request and a device identifier to the local server. The device identifier can be used to validate the device and provide a security token to the management application. The management application can use the security token to complete enrollment of the user device.

Abstract: One embodiment of a wellbore to fracture connectivity apparatus comprises an opening portion representing an opening in a wellbore for receiving fluid, proppant, or any combination thereof to generate at least one fracture in a formation; a fracture portion representing the at least one fracture in the formation; and a coupling portion representing wellbore to fracture connectivity between the opening portion and the fracture portion. The coupling portion couples the opening portion and the fracture portion.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine a video embedding for a video content item based at least in part on a first machine learning model. A set of music embeddings can be determined for a set of music content items based at least in part on a second machine learning model. The set of music content items can be ranked based at least in part on the video embedding and the set of music embeddings.

Abstract: Examples described herein include systems and methods for contextually providing automated device enrollment into a management system. A management application on a user device can receive network settings for connecting to a local server. The network settings can be preconfigured by an administrator. The management application can cause the user device to send an enrollment request and a device identifier to the local server. The device identifier can be used to validate the device and provide a security token to the management application. The management application can use the security token to complete enrollment of the user device.

Abstract: Improved artificial intelligence computer systems actively and/or passively provide end-users with information from firm data systems to help the end-user perform the end-user's job functions. In a passive implementation, the system can prioritize emails for a user, craft consistent responses to multiple email inquiries on the same topic, and/or craft responses whose content is drawn from a library of electronic documents. In an active implementation, the system comprises AI agents associated with individual databases of the system, where the AI agents are tuned to retrieve data from their associated database in response to a query from the end-user.

Abstract: In some examples, a non-transitory computer-readable medium stores machine-readable instructions which, when executed by a processor, cause the processor to: collect operational data of an enterprise; identify a domain of the operational data; determine a performance metric of the domain; and generate a report based on the performance metric.

Abstract: The present subject matter relates to techniques of assigning a mobility factor to the user based on the spatial movement data. In one example, the technique may include four phases for assigning the mobility factors to the users. In a first phase, spatial movement data from a plurality of user devices assigned to a plurality of users may be received. In a second phase, spatial movement behavior for a set of user devices may be assessed using the received spatial movement data of the members of the set using machine learning techniques. In a third phase, user devices may be selected based on the assessed spatial movement behavior using machine learning techniques for reconfiguration. Finally, at a four phase, the selected user devices may be reconfigured.

Abstract: An example of an apparatus including a network interface to receive first metadata and second metadata. The first metadata is associated with a first data source and the second metadata is associated with a second data source. The apparatus includes a processor to determine a first series of variance values associated with first metadata and second metadata, and to determine a second series of variance values associated with first metadata and second metadata. Furthermore, the apparatus includes a memory storage unit to store the first series of variance values and the second series of variance values. The apparatus includes an analysis engine to analyze the first series of variance values and the second series of variance values to confirm compatibility between the first data source and the second data source. The second series of variance values is to be analyzed after the first series of variance values is passed.

Abstract: An example of an apparatus including a memory storage unit to store telemetry data collected from a plurality of sources, wherein each source of the plurality of sources maintains confidentiality. The apparatus further includes an anonymizing engine to remove identifying information from the telemetry data to generate anonymized data. The apparatus also includes a communication interface to receive request from a client device for an upgrade. The request includes a requesting device configuration of the client device. The apparatus also includes a selection engine to select a subset of the anonymized data based on the requesting device configuration. Furthermore, the apparatus includes a comparison engine to analyze the subset of the anonymized data to determine a probability of an upgrade failure at the client device. Additionally, the apparatus includes an upgrade engine to implement the upgrade on the client device based on the probability.

Abstract: Embodiments of determining proppant distribution for a plurality of clusters within a fracture stage of a wellbore are provided herein. The embodiments include performing computational fluid dynamics modeling on at least a portion of a wellbore without any openings and a portion of the wellbore comprising at least one opening along a single azimuth to determine proppant efficiency for the at least one opening along the single azimuth while simulating flow of fluid, proppant, or any combination thereof through the wellbore, an equilibrium proppant concentration profile for the portion of the wellbore without any openings, and an equilibrium velocity profile for the portion of the wellbore without any openings. The embodiment further comprises determining proppant efficiency for at least one other opening of the wellbore at a different azimuth, generating a model that correlates the single azimuth among other items, and using the model to determine the proppant distribution.

Abstract: The invention discloses a first unit (1) for treatment of a bioprocess liquid comprising a first lateral face (2), a second lateral face (3) and a front face (4) which meets the two said lateral faces. The front face comprises: a plurality of valves (7) adapted to receive and act upon one or more legs (8) of a disposable flow path (6); optionally one or more pumps (10) adapted to receive and act upon one or more legs of the disposable flow path; optionally one or more sensors (11) adapted to receive and to measure one or more parameters in one or more legs of the disposable flow path; wherein the plurality of valves and optional pumps and sensors are vertically offset from each other to give one or more legs of a disposable flow path received by said valves and optional pumps and sensors a slope of at least 3.0 degrees from the horizontal plane (h).

Abstract: A system for providing responses to natural language queries or commands is disclosed. The system comprises a server comprising a processor and non-transitory memory storing instructions that cause the processor to: retrieve a stored data structure comprising a plurality of named entities; receive a natural language query or command from a human user; extract one or more named entities from the query or command, wherein the extraction comprises iteratively: dividing the query or command into overlapping sets of tokens, determining whether a set of tokens corresponds to a named entity from the plurality of named entities, extracting any corresponding set of tokens from the query, and if no corresponding sets remain, decreasing the set size and repeating; and based at least in part on the query or command and on the extracted one or more named entities, act to fulfill a user intent expressed by the query or command.

Abstract: Embodiments of determining a hydraulic fracture completion configuration for a wellbore that extends through a subterranean formation are provided herein. Embodiments of performing a hydraulic fracturing operation on a wellbore that extends through a subterranean formation are also provided herein.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine a video embedding for a video content item based at least in part on a first machine learning model. A set of music embeddings can be determined for a set of music content items based at least in part on a second machine learning model. The set of music content items can be ranked based at least in part on the video embedding and the set of music embeddings.

Abstract: A force-sense system for providing signals to, or receiving signals from, a device under test (DUT) at a first DUT node. The system can include an interface coupling first and second portions of a first force-sense measurement device, such as a parametric measurement unit. The first and second portions of the first force-sense measurement device can be provided using respective different integrated circuits, such as can comprise different semiconductor dies of different die types. In a first test mode, the interface can be configured to communicate a first DUT force signal from the first portion to the second portion of the first force-sense measurement device, and in a second test mode the interface can be configured to communicate DUT sense information, received from the DUT at the first DUT node, from the second portion to the first portion of the first force-sense measurement device.

Abstract: A force-sense system can provide signals to, or receive signals from, a device under test (DUT) at a first DUT node. The system can include output buffer circuitry configured to provide a DUT signal to the DUT in response to a force control signal at a buffer control node, and controller circuitry configured to provide the force control signal at the buffer control node. The system can include bypass circuitry configured to selectively bypass the controller circuitry and provide an auxiliary control signal at the buffer control node. The auxiliary control signal can be used for system calibration. In an example, an external calibration circuit can provide the auxiliary control signal in response to information received from the DUT.

Abstract: Example implementations relate to a retail point of sale (RPOS) device and cloud service. An example system can include an RPOS agent controller and a cloud service controller communicatively coupled to one another. The cloud service controller can provide a notification of a state of the RPOS device.