SYSTEM AND METHOD FOR CAUSAL ANALYSIS OF BIOSYSTEMS ON CHIPS

    公开(公告)号:US20240028865A1

    公开(公告)日:2024-01-25

    申请号:US17872990

    申请日:2022-07-25

    IPC分类号: G06N3/00

    CPC分类号: G06N3/002

    摘要: Methods and systems for operating biosystem on a chip are disclosed. To operate biosystem on a chip based systems, causal mechanisms may be identified based on previous operations performed by the biosystem chip based systems. The causal mechanisms may be used to develop new operation plans, refine existing operation plans, and/or develop new biosystem on a chip architecture. The causal mechanism may be derived from a causal graph that includes nodes representing unknown causal mechanisms. Data regarding previous operations in combination with the causal graph may be used to learn the unknown causal mechanisms.

    SYSTEM AND METHOD FOR MANAGING CONTROL DATA FOR OPERATION OF BIOSYSTEMS ON CHIPS

    公开(公告)号:US20240028434A1

    公开(公告)日:2024-01-25

    申请号:US17872980

    申请日:2022-07-25

    IPC分类号: G06F11/07

    CPC分类号: G06F11/076 G06F11/0736

    摘要: Methods and systems for operating biosystem on a chip are disclosed. To operate biosystem on a chip based systems, likely faults in the operation of the system may be predicted. Algorithms usable to mitigate the predicted likely faults may be identified, and ranked based on a level of impact that access to data reelecting the operation of the system may have on the utility of the algorithms. Higher ranked algorithms may be deployed for execution during operation of the system to lower latency locations while lower ranked algorithms may be deployed for execution to higher latency locations. The lower latency locations may include computing resources that are local to the biosystem on a chip, but that may be limited in quantity.

    MACHINE LEARNING SYSTEM AND METHOD OF DETECTING IMPACTFUL PERFORMANCE ANOMALIES

    公开(公告)号:US20230229733A1

    公开(公告)日:2023-07-20

    申请号:US17579769

    申请日:2022-01-20

    IPC分类号: G06K9/62 G06N20/20 G06F11/34

    摘要: Techniques for detecting impactful performance anomalies in storage systems. The techniques include obtaining, for each performance metric of a storage system's workload, a training set of series diffs based on a threshold. Each diff represents a difference between an observed value from an observed set of time series values for the performance metric and a normalized value from a corresponding set of normalized time series values. The techniques include applying the training set of series diffs for each performance metric to an unsupervised anomaly detection algorithm and running the algorithm to identify potentially impactful anomalies in a multi-dimensional search space. The techniques include identifying impactful anomalies from among the potentially impactful anomalies that exceed an anomaly score. In this way, impactful anomalies having a causal effect on multiple performance metrics of the storage system's workload can be identified in a manner less complex and less costly than prior multivariate approaches.

    POLYSACCHARIDE ARCHIVAL STORAGE
    36.
    发明公开

    公开(公告)号:US20230222313A1

    公开(公告)日:2023-07-13

    申请号:US17647783

    申请日:2022-01-12

    IPC分类号: G06N3/00 G06F3/06

    摘要: One example method includes encoding data as a polysaccharide structure, synthesizing the polysaccharide structure to create polysaccharide storage media that comprises the data, and storing the polysaccharide storage media. The example method may also include receiving a read request directed to the polysaccharide storage media, mapping the polysaccharide structure to create a map in response to the read request, traversing the map of the polysaccharide structure to determine an X-base number, and obtaining the data by converting the X-base number to a binary form.

    Chaos Engineering in Microservices Using a Service Mesh

    公开(公告)号:US20220141304A1

    公开(公告)日:2022-05-05

    申请号:US17086540

    申请日:2020-11-02

    摘要: Techniques are provided for chaos engineering in microservices using a service mesh. In an example, a plurality of microservices can operate together as part of a software as a service product. A graph of the service mesh of the plurality of microservices can be determined. From that graph, weight can be assigned to the respective nodes. Those weights can be used to determine a probability of where chaos is introduced in the corresponding microservice architecture as part of chaos testing.

    Customer Service Ticket Processing Using Cluster-Based Data Driven Guidebooks

    公开(公告)号:US20210342857A1

    公开(公告)日:2021-11-04

    申请号:US16864396

    申请日:2020-05-01

    摘要: Techniques are provided for customer service ticket processing using cluster-based data driven guidebooks. One method comprises obtaining a customer service ticket; extracting features related to the customer service ticket, wherein the features comprise a representation of a problem associated with the customer service ticket; assigning the customer service ticket to a given cluster of multiple of customer service ticket clusters based on the features; obtaining a customer service ticket processing guidebook associated with the given cluster that identifies independent actions to perform to address the problem; and processing the customer service ticket based on the customer service ticket processing guidebook. A customer service ticket processing guidebook may be generated for each customer service ticket cluster using historical customer service tickets from the respective cluster. The customer service ticket processing guidebooks can be generated by clustering (i) possible independent actions and (ii) possible solutions identified in the historical customer service tickets of the given cluster.