Internet of things fleet management with out of sequence stop detection

    公开(公告)号:US12154068B2

    公开(公告)日:2024-11-26

    申请号:US17809400

    申请日:2022-06-28

    Abstract: Embodiments detect stops by an entity on a pre-planned trip that includes a plurality of stops and a planned sequence of stops. Embodiments receive a first geo-location message indicating a first location and corresponding first time for the entity. Embodiments, based on the first geo-location message and a previous geo-location message, generate an enter geofence set and an exit geofence set. When the entity is currently, based on the previous geo-location message, at one of the stops and the current stop is in the exit geofence set, embodiments store the first time as a departure time for the current stop. When the entity is not currently, based on the previous geo-location message, at one of the stops and the enter geofence set is non-empty, embodiments store the first time as an arrival time for a stop corresponding to the first location.

    SOVEREIGN DATA CENTER INCOMING DATA MANAGEMENT

    公开(公告)号:US20240386124A1

    公开(公告)日:2024-11-21

    申请号:US18654448

    申请日:2024-05-03

    Abstract: Techniques are described for data management. An example method can include processing a first message indicating that an intermediate computing system managed by a data center in a first region has received data from a second data center in a second region, the data stored in an isolated environment of the intermediate computing system. The method can further include transmitting first control instructions to the intermediate computing system to validate the data based at least in part on a first criteria. The method can further include processing a second message indicating to release the data from the isolated environment of the intermediate computing system and processing a third message indicating that the second message originated from the first region. The method can further include causing the data to be released from the isolated environment based at least in part on the validation results.

    COLD-START FORECASTING VIA BACKCASTING AND COMPOSITE EMBEDDING

    公开(公告)号:US20240386047A1

    公开(公告)日:2024-11-21

    申请号:US18198975

    申请日:2023-05-18

    Abstract: Techniques are described herein for cold-start forecasting datasets using backcasting and composite embedding. An example method can include a system receiving a set of time series and metadata text comprising a first subset of metadata text and a second subset of metadata text. The system can generate a plurality of embeddings, each embedding comprising a numerical representation of a metadata text of the set of metadata text. The system can generate a plurality of vectors, each vector comprising a time series of the set of time series each time series associated with a metadata text of the first subset of metadata text. The system can generate a plurality of composite embeddings based at least in part on combining each embedding with a respective vector of the plurality of vectors. The system can determine a forecasted value associated with the second subset of metadata text based on the composite embeddings.

    BUILDING A DATA CENTER USING EXECUTION TARGET CHECKPOINTS

    公开(公告)号:US20240385889A1

    公开(公告)日:2024-11-21

    申请号:US18661396

    申请日:2024-05-10

    Abstract: A cloud infrastructure orchestration service (CIOS) may track build progress. A service plan may define a first execution order of releases for bootstrapping a service (e.g., provisioning and deploying resources of the service) to an execution target (ET) (e.g., a set of devices of a data center). The first execution order may be defined using transitions between ET checkpoints, with each transition and checkpoint being associated with a corresponding release. A directed acyclic graph (DAG) may be generated from any suitable number of service plans associated with various services to define a second execution order for the releases needed to bootstrap the services. At build time, CIOS may track release execution by updating the state of an ET to correspond to an ET checkpoint when the release is successful. ET states may be used by CIOS to enforce the second execution order.

    Sharing Secure User Information Using Near-Field Communication

    公开(公告)号:US20240380433A1

    公开(公告)日:2024-11-14

    申请号:US18356684

    申请日:2023-07-21

    Abstract: Embodiments permit secure information exchange using lightweight data and near-field communication (NFC). A user can transmit lightweight data, such as one or more indicators (e.g., user indicator, scope indicator(s), documents indicator(s), etc.), to a receiving computing system via the user's wireless device and an NFC protocol. Because NFC transmissions are performed by co-located devices, this lightweight data transmission can trigger and/or continue a sophisticated workflow. For example, the receiving computing system can be associated with a product or service provider, and the lightweight data transmission can progress a workflow related to a particular product and/or service. The workflow progression can include accessing secure user information via the indicator(s) received over the NFC transmission.

    SCALABLE RANGE LOCKS
    138.
    发明申请

    公开(公告)号:US20240378242A1

    公开(公告)日:2024-11-14

    申请号:US18780266

    申请日:2024-07-22

    Abstract: A computer comprising one or more processors and memory may implement multiple threads performing mutually exclusive lock acquisition operations on disjoint ranges of a shared resource each using atomic compare and swap (CAS) operations. A linked list of currently locked ranges is maintained and, upon entry to a lock acquisition operation, a thread waits for all locked ranges overlapping the desired range to be released then inserts a descriptor for the desired range into the linked list using a single CAS operation. To release a locked range, a thread executes a single fetch and add (FAA) operation. The operation may be extended to support simultaneous exclusive and non-exclusive access by allowing overlapping ranges to be locked for non-exclusive access and by performing an additional validation after locking to provide conflict resolution should a conflict be detected.

    Semi-supervised framework for purpose-oriented anomaly detection

    公开(公告)号:US12143408B2

    公开(公告)日:2024-11-12

    申请号:US17739968

    申请日:2022-05-09

    Abstract: Techniques for implementing a semi-supervised framework for purpose-oriented anomaly detection are provided. In one technique, a data item in inputted into an unsupervised anomaly detection model, which generates first output. Based on the first output, it is determined whether the data item represents an anomaly. In response to determining that the data item represents an anomaly, the data item is inputted into a supervised classification model, which generates second output that indicates whether the data item is unknown. In response to determining that the data item is unknown, a training instance is generated based on the data item. The supervised classification model is updated based on the training instance.

    Accessing a parametric field within a specialized context

    公开(公告)号:US12141629B2

    公开(公告)日:2024-11-12

    申请号:US18497634

    申请日:2023-10-30

    Abstract: A parametric constant resolves to different values in different contexts, but a single value within a particular context. An anchor constant is a parametric constant that allows for a degree of parametricity for an API point. The context for the anchor constant is provided by a caller to the API point. The anchor constant resolves to an anchor value that records specialization decisions for the API point within the provided context. Specialization decisions may include type restrictions, memory layout, and/or memory size. The anchor value together with an unspecialized type of the API point result in a specialized type of the API point. A class object representing the specialized type is created. The class object may be accessible to the caller, but the full value of the anchor value is not accessible to the caller. The API point is executed based on the specialization decisions embodied in the anchor value.

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