Abstract: A system for managing messaging conversations that can be simultaneous and can be maintained across various different autonomous processing systems is described. In one embodiment, the system (operated by or on behalf of a first organization) can include a messaging manager and a first autonomous processing system (first APS) and a second APS. The system can receive a first message and a second message, both directed to the first organization, and the messaging manager can select the first APS to respond to the first message and route the first message to the first APS which can transmit a response to the first message. The messaging manager can select the second APS to respond to the second message and route the second message to the second APS. The messages can be managed asynchronously without a persistent connection during the one or more conversations.

Abstract: Software performance can be automatically managed in a distributed computing environment. In one example, a system that can receive metrics information describing resource usage by a first instance of a service in a distributed computing environment. The system can also determine a quality-of-service (QoS) constraint for the service. The system can then modify a definition file based on the metrics information and the QoS constraint, the definition file being configured for deploying instances of the service in the distributed computing environment. The system can deploy a second instance of the service in the distributed computing environment using the modified definition file. As a result, the second instance can more closely satisfy the QoS constraint than the first instance.

Abstract: Various embodiments of the present disclosure improve existing multi-layer and other network technologies by routing and processing client requests that require machine learning based on the machine learning capabilities of each network device and/or other computer resource characteristics of different network devices. This ensures that network latency and throughput, among other computer resource consumption characteristics, will be improved as machine learning processing can occur at the most suitable network device or be distributed among various suitable network devices.

Abstract: To optimize content of a webpage in a manner that depends on whether or not a user has linked accounts to each other. The solving means is an information processing apparatus including a storage unit, a communication unit, and a control unit. The storage unit stores information regarding a first account of each of a plurality of users and first content and second content to be placed on a webpage. The communication unit communicates with a user terminal of the user.

Abstract: Provided is a process including: obtaining, for a plurality of entities, entity logs, wherein: the entity logs comprise events involving the entities, a first subset of the events are actions by the entities, at least some of the actions by the entities are targeted actions, and the events are labeled according to an ontology of events having a plurality of event types; training, with one or more processors, based on the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given geographic locale in the future; and storing the training the trained predictive machine learning model in memory.

Abstract: A communications ecosystem and related methods of operation include an ecosystem safety level (ESL), a plurality of vehicles or systems, each vehicle or system having an individual safety level (ISL), a control system operable to determine whether the ISL for each vehicle or system meets or exceeds the ESL, and one or more communications links between any first vehicle or system having a first ISL that does not meet or exceed the ESL and a second vehicle or system with a second ISL that does meet or exceed the ESL such that the first vehicle or system operates at the second ISL.

Abstract: Machine learning model re-training based on distributed feedback received from a plurality of edge computing devices is provided. A trained instance of a machine learning model is transmitted, via one or more communications networks, to the plurality of edge computing devices. Feedback data is collected, via the one or more communications networks, from the plurality of edge computing devices. The feedback data includes labeled observations generated by the execution of the trained instance of the machine learning model at the plurality of edge computing devices on unlabeled observations captured by the plurality of edge computing devices. A re-trained instance of the machine learning model is generated from the trained instance using the collected feedback data. The re-trained instance of the machine learning model is transmitted, via the one or more communications networks, to the plurality of edge computing devices.

Abstract: The present invention extends to methods, systems, and computer program products for upgrading DevOps tools in DevOps toolchains. An upgraded version of a DevOps platform tool is detected. A snapshot of an existing tool image corresponding to a current version of the DevOps platform tool is taken. Cloud service provider profile information, existing tool profile information corresponding to the existing tool image, and replacement tool profile information corresponding to replacement tool image are accessed. A DevOps platform is upgraded including deploying a replacement tool image corresponding to the upgraded version in accordance with the cloud service provider profile information, the existing tool profile information, the replacement tool profile information, and a DevOps platform category. When the upgrade is complete, the existing tool image is removed and the DevOps platform pointed to the replacement tool image. The snapshot is retained in accordance with rollback settings.

Abstract: Described are techniques for data ingestion including determining a respective moving average streaming rate for each of a plurality of incoming data streams to a cluster-computing framework. The techniques further include determining a respective ingestion frequency for each of the plurality of incoming data streams by dividing a platform-preferred ingestion rate of the cluster-computing framework by a respective moving average streaming rate of each of the plurality of incoming data streams. The techniques further include ingesting each of the plurality of incoming data streams to the cluster-computing framework at the respective ingestion frequency.

Abstract: The present invention extends to methods, systems, and computer program products for removing DevOps tools from DevOps toolchains. A user selection requesting DevOps tool deletion is received. A snapshot of a tool image corresponding to the DevOps tool is taken. Cloud service provider profile information and tool profile information corresponding to the tool image are accessed. The DevOps tool is deleted from a DevOps platform, including removing the tool image, in accordance with the cloud service provider profile information and the tool profile information. A tool registry is updated to indicate removal of the tool image from the DevOps platform. The snapshot is stored and retained in accordance with a time period specified in rollback settings.

Abstract: The present invention extends to methods, systems, and computer program products for replacing DevOps tools in DevOps toolchains. A user selection of a replacement DevOps platform tool associated with a DevOps platform category is received. Configuration information for the DevOps platform, including cloud service provider profile information and existing tool profile information is accessed. The existing tool is removed from the DevOps platform in accordance with the existing tool profile information, the DevOps platform category, and the service provider profile information. A replacement tool and replacement tool profile information are accessed. The replacement tool is deployed to the DevOps platform in accordance with the replacement tool profile information, the DevOps platform category, and the cloud service provider profile information.

Abstract: Integrating cloud storage services includes: receiving, by a storage integration service, a request to perform a first data storage operation corresponding to first data; generating, by applying one or more functional operations to the first data, second data; generating, by applying one or more translation operations to the first data storage operation, one or more second data storage operations that correspond to the second data, wherein the one or more second data storage operations correspond to respective one or more cloud storage services; and issuing, by the storage integration service, the one or more second data storage operations to the corresponding one or more cloud storage services.

Abstract: At a network manager of an extension resource group of a provider network, a message comprising a command to launch a compute instance is received at an address which is part of a first network configured at a premise external to the provider network. The extension resource group includes a first host at the external premise. Within a second network configured at the external premise, the first host is assigned an address within a second address range. Addresses within the second range are also assigned to hosts within the provider network. The command is transmitted to the first host, and a compute instance is instantiated.

Abstract: An electronic device that includes a communication module and a processor operatively connected to the communication module to exchange data with an external electronic device through the communication module is provided. The processor generates user data and a user profile based on collected user information related to a plurality of items, receive learning information data related to the plurality of items from the external electronic device, generate user learning data by applying the learning information data to the user data, learn a user preference pattern based on the user learning data, update the user profile based on the learned user preference pattern, and receive recommended item data from the external electronic device based on the updated user profile.

Abstract: Systems and methods for providing resilient persistent connections for communication are disclosed. Methods include identifying, at a first device, a second device registered with a client connection service; obtaining, from the client connection service, a first resource identifier for delivering request messages to the second device via the client connection service; identifying, based on the obtained first resource identifier, a first target resource for a first request message directed to the second device, wherein the first target resource specifies a first host included in the client connection service; sending, to the client connection service via the data communication network, the first request message to the first target resource for delivery to the second device by the client connection service; and receiving, from the client connection service via the data communication network, a first response message provided by the second device as a response to the first request message.

Abstract: A method for improving the estimation of relocation costs including the steps of: generating a relocation costs data-model; performing a dual-model algorithm on the relocation costs data-model to determine a preliminary relocation costs predictive model for a relocation service; receiving a first dataset of a subject to be relocated; analyzing the first dataset with the preliminary relocation costs predictive model to generate a preliminary relocation costs for the relocation service; displaying, on a display of a remote device, the preliminary relocation costs; performing the dual-model algorithm on the relocation costs data-model to determine a supplemental relocation costs predictive model for the relocation service; receiving a second dataset of the subject to be relocated; analyzing the second dataset with the supplemental relocation costs predictive model to generate a supplemental relocation costs for the relocation service; and displaying, on the display of the remote device, the supplemental estimate

Abstract: Embodiments disclosed herein generally relate to a system and method for proactively generating an intervening message for a remote client device in response to an anticipated user action. A computing system receives one or more streams of user activity. The one or more streams of user activity include interaction with a server of an organization via an application executing on the remote client device. The computing system inputs the one or more streams of user activity into a prediction model. The computing system identifies an anticipated user action based on a prediction output from the prediction model. The computing system determines, based on a solution model, a proposed solution to the anticipated user action. The computing system generates an anticipated message to be transmitted to the remote client device of the user. The computing system transmits the anticipated message to the remote client device of the user.

Abstract: Described herein is a methods and systems for providing a digital interactive experience. Interactive experiences include, but not limited to, synchronized video playback, video and text chat between two or more participants, and interactive live event participation combining synchronized video, text and video chatting. The system comprises persistent and non-persistent components to which a plurality of participants in the interactive experience may connect.

Abstract: A networkable device is coupled to a wireless IP gateway or paired to a personal area network (PAN) via the use of a sonic tone, which may be a human audible tone, which encodes a symbol sequence representing a binary message useful for implementing connection to a wireless IP gateway or pairing with a PAN. In one embodiment the sonic tone conveys a media access controller (MAC) address, to facilitate connection and/or pairing. Alternatively, or in addition, the sonic tone may encode a Service Set Identifier (SSID) and password for connection to a wireless network (Wi-Fi), or a BLUETOOTH (IEEE 802.15.1) identifier and/or PIN for connection to a BLUETOOTH (IEEE 802.15.1) PAN. In particular implementation of this embodiment the tone is produced by the IP gateway and used to initiate Wi-Fi connection, and/or the tone is produced by the master device of a PAN to initiate BLUETOOTH (IEEE 802.15.1) connection.

Abstract: An indication of training artifacts for a machine learning model to be trained with an input data set having an access restriction is obtained. A representation of a software execution environment containing the artifacts is deployed to a computing platform within an isolated resource group which satisfies the access restriction. A trained version of the machine learning model is generated at the computing platform, and transferred outside the isolated resource group.