Abstract: Techniques for feedback alerts for data transactions are described and are implementable to enable users to provide transaction feedback for data transactions and to receive feedback alerts for data transactions. In implementations, a user can initiate a data transaction with a particular party and can be presented with a feedback alert describing transaction feedback for previous data transactions with the particular party.

Abstract: A method in a conferencing system terminal device includes detecting, with one or more processors during a videoconference, a communication device electronically in communication with a plurality of remote electronic devices engaged in the videoconference. The method includes receiving, with a user interface operable with the one or more processors, user input initiating a communication operation at the conferencing system terminal device. In response to the user input, one or more processors of the conferencing system terminal device present a prompt allowing a selection of a subset of the plurality of remote electronic devices for a side call during the videoconference.

Abstract: A method in a conferencing system terminal device includes detecting, with one or more processors during a videoconference, a communication device electronically in communication with a plurality of remote electronic devices engaged in the videoconference. The method includes also detecting, with one or more sensors during the videoconference, an authorized user of the conferencing system terminal device engaging in a side conversation. One or more processors cause the communication device to transmit a video overlay for the authorized user of the conferencing system terminal device to the plurality of remote electronic devices while the side conversation occurs. This conceals the authorized user's participation in the side conversation.

Abstract: Techniques for feedback for data transactions are described and are implementable to enable users to provide transaction feedback for data transactions. In implementations, a user can be presented with indicia of transaction feedback at various points in a data transaction process. The user can interact with the indicia of transaction feedback to indicate transaction feedback for a data transaction. The user can be presented with information regarding data transactions, such as transaction feedback provided by the user for data transactions.

Abstract: Systems and techniques for generating and animating non-player characters (NPCs) within virtual digital environments are provided. Multimodal input data is received that comprises a plurality of input modalities for interaction with an NPC having a set of body features and a set of facial features. The multimodal input data is processed through one or more neural networks to generate animation sequences for both the body features and facial features of the NPC. Generating such animation sequences includes disentangling the multimodal input data to generate substantially disentangled latent representations, combining these representations with the multimodal input data, and using a large-language model (LLM) to generate speech data for the NPC. Further processing using reverse diffusion generates face vertex displacement data and joint trajectory data based on the combined representation and generated speech data.

Abstract: Systems and techniques for generating and animating non-player characters (NPCs) within virtual digital environments are provided. Multimodal input data is received that comprises a plurality of input modalities for interaction with an NPC having a set of body features and a set of facial features. The multimodal input data is processed through one or more neural networks to generate animation sequences for both the body features and facial features of the NPC. Generating such animation sequences includes disentangling the multimodal input data to generate substantially disentangled latent representations, combining these representations with the multimodal input data, and using a large-language model (LLM) to generate speech data for the NPC. Further processing using reverse diffusion generates face vertex displacement data and joint trajectory data based on the combined representation and generated speech data.

Abstract: Systems and techniques for generating and animating non-player characters (NPCs) within virtual digital environments are provided. Multimodal input data is received that comprises a plurality of input modalities for interaction with an NPC having a set of body features and a set of facial features. The multimodal input data is processed through one or more neural networks to generate animation sequences for both the body features and facial features of the NPC. Generating such animation sequences includes disentangling the multimodal input data to generate substantially disentangled latent representations, combining these representations with the multimodal input data, and using a large-language model (LLM) to generate speech data for the NPC. Further processing using reverse diffusion generates face vertex displacement data and joint trajectory data based on the combined representation and generated speech data.

Abstract: A communication device, a method, and a computer program product for surfacing notifications at a second display of a connected second device based on network conditions and device characteristics at a consumer device and connected second device. The method includes receiving, by a communication device, a notification associated with consumption of content available for presentation by the communication device that interfaces with another electronic device that includes an auxiliary display capable of mirroring a user interface of the communication device. The method includes detecting one or more network conditions and device resource characteristics corresponding to the content consumption criteria encoded in the metadata. The method includes selectively enabling presentation of the content on the auxiliary display for consumption based, at least in part, by the detected one or more network conditions and device resource characteristics.

Abstract: An electronic device, a method and a computer program product for surfacing notifications based on optimal network and device conditions at a consumer device. The method includes receiving from a network connected device, a notification associated with local consumption of content available for presentation by a communication device, the notification including metadata associated with the content, the metadata encoding content consumption criteria related to preferences for when and how to present the content, in part based on network conditions at the communication device. The method includes detecting one or more current network conditions and comparing the current network conditions with network constraints within the content consumption criteria encoded in the metadata. The method includes selectively presenting the content for consumption at one or more of a time or in a format determined, at least in part, by the detected one or more network conditions.

Abstract: An electronic device, a method, and a computer program product generate push notifications directing optimal time for surfacing notifications at a consumer device. The method includes selecting content for consumption at a second device communicatively coupled to a computing device, and encoding into metadata associated with the content, content consumption criteria related to at least one of a network quality and a second device capability for presenting the content at the second device. The method includes transmitting a notification to the second device, the notification including the metadata associated with the content, the metadata configuring the second device to determine at least one of a timing for presenting the content for consumption at the second device and presentation parameters related to the content based on the content consumption criteria.

Abstract: Techniques for object tracking via glasses device are described and are implementable to enable different attributes of objects to be tracked via a glasses device. In implementations, user engagement with an object of interest is detected and tracked. Based on the user engagement with the object of interest, engagement data is generated that includes attributes of the user engagement. Further, cost tracking for objects can be implemented based on images of objects captured via a glasses device. Object records can be created for detected objects and can include object attributes such as object identifiers, object costs, object locations, etc.

Abstract: An electronic device, a method, and a computer program product generate push notifications directing optimal time for surfacing notifications at a consumer device. The method includes selecting content for consumption at a second device communicatively coupled to a computing device, and encoding into metadata associated with the content, content consumption criteria related to at least one of a network quality and a second device capability for presenting the content at the second device. The method includes transmitting a notification to the second device, the notification including the metadata associated with the content, the metadata configuring the second device to determine at least one of a timing for presenting the content for consumption at the second device and presentation parameters related to the content based on the content consumption criteria.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: A method of assembling a pipeline at a seabed location comprises landing a connection tool on the seabed over a free end portion of a first pipeline section already placed on the seabed. The connection tool is locked to the free end portion of the first pipeline section, a lower end of a second pipeline section is connected to the connection tool via an initiation line. While applying tension to the initiation line against reaction force of the connection tool, at least a lower end portion of the second pipeline section is landed on the seabed with the lower end facing a free end of the first pipeline section. The lower end of the second pipeline section is then pulled into mechanical engagement with the free end of the first pipeline section.

Abstract: A method of installing a subsea pipeline supports at least one elongate pipe stalk at the surface of the sea at a first, relatively shallow-water location by virtue of buoyancy added to the pipe stalk. The pipe stalk is then towed at the surface to a second location that is in deeper water. There, with the pipe stalk supported between leading and trailing towing vessels, at least some of the added buoyancy is removed. This causes the pipe stalk to hang with catenary curvature beneath the surface between the vessels. The catenary-curved pipe stalk hanging between the vessels is then towed to a third location for subsea installation, which may involve upending the pipe stalk before landing a lower end portion of it on the seabed.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: Systems, devices, and techniques are disclosed for multi-threaded dynamic queries on an unpartitioned database. An instruction to perform a database operation on rows of a database may be received. Processing threads may be generated by generating three random numbers. A starting point of a processing thread may be set to a first random number, an offset may be set to a second random number, and an order may be based on a third random number. The processing threads may each generate a dynamic query. A dynamic query generated by a processing thread may include an instruction to perform the database operation on rows of the database based on the offset, starting point, and order for the processing thread. Each dynamic query may be run against the database to perform the database operation.

Abstract: Examples include a ticketing system for managing performance of computer services. The ticketing system includes a ticket database to store a plurality of tickets, each ticket defining a job to be performed on a computing system, and a master to create a ticket, store the ticket in the ticket database, and update the ticket. The ticketing system includes a scheduler to determine whether to create the ticket based at least in part on a set of conditions, and to request the master to create the ticket when the ticket is to be created, and a worker to get the ticket from the master, perform the job defined by the ticket, and request the master to update the ticket with a status of the ticket. The ticketing system includes a supervisor to request the master to find a stale ticket, and, if found, to request the worker to abort performance of the job defined by the stale ticket.

Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

Abstract: A method of assembling a pipeline at a seabed location comprises landing a connection tool (10) on the seabed over a free end portion of a first pipeline section (12) already placed on the seabed. The connection tool is locked to the free end portion of the first pipeline section, a lower end of a second pipeline section (26) is connected to the connection tool via an initiation line (68). While applying tension to the initiation line against reaction force of the connection tool, at least a lower end portion of the second pipeline section is landed on the seabed with the lower end facing a free end of the first pipeline section. The lower end of the second pipeline section is then pulled into mechanical engagement with the free end of the first pipeline section.