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    Cooperative platform for generating, securing, and verifying device graphs and contributions to device graphs
    12.
    发明授权
    Cooperative platform for generating, securing, and verifying device graphs and contributions to device graphs 有权

    公开(公告)号：US11115204B2

    公开(公告)日：2021-09-07

    申请号：US15845948

    申请日：2017-12-18

    Applicant: ADOBE INC.

    Inventor： Subrata Mitra ,  Vishal Babu Bhavani ,  Sunav Choudhary ,  Kishalay Raj ,  Ayush Chauhan

    IPC: H04L9/32 ,  H04L9/08 ,  G06N7/00 ,  G06N5/02

    Abstract: Graphing services are provided to a device cooperative that includes data contributors, e.g., website hosts. Anonymized user data, provided by the data contributors, is accessed, via a blockchain, decrypted, and aggregated. A device graph is generated based on the aggregated user data. Contribution metrics are provided to the data contributors. A first contribution metric for a first data contributor indicates a contribution to the device graph of a first portion of the user data that was provided by the first data contributor. In response to receiving a request for a verification of the first contribution metric, a zero knowledge proof of the first contribution metric is generated and provided to the first data contributor. The first data contributor is enabled to evaluate the zero knowledge proof independent of access to a second portion of the user data that was provided by a second data contributor of the device cooperative.

    Robust anomaly and change detection utilizing sparse decomposition
    13.
    发明授权
    Robust anomaly and change detection utilizing sparse decomposition 有权

    公开(公告)号：US11095544B1

    公开(公告)日：2021-08-17

    申请号：US16904249

    申请日：2020-06-17

    Applicant: Adobe Inc.

    Inventor： Aishwarya Asesh ,  Sunav Choudhary ,  Shiv Kumar Saini ,  Chris Challis

    IPC: G06F16/2458 ,  G06F16/248 ,  G06F7/00 ,  H04L12/26

    Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for determining latent components of a metrics time series and identifying anomalous data within the metrics time series based on one or both of spikes/dips and level changes from the latent components satisfying significance thresholds. To identify such latent components, in some cases, the disclosed systems account for a range of value types by intelligently subjecting real values to a latent-component constraint for decomposing the time series and intelligently excluding non-real values from the latent-component constraint. The disclosed systems can further identify significant anomalous data values from latent components of the metrics time series by jointly determining whether one or both of a subseries of a spike-component series and a level change from a level-component series satisfy significance thresholds.

    Updating Machine Learning Models On Edge Servers
    14.
    发明申请
    Updating Machine Learning Models On Edge Servers 审中-公开

    公开(公告)号：US20200027033A1

    公开(公告)日：2020-01-23

    申请号：US16040057

    申请日：2018-07-19

    Applicant: Adobe Inc.

    Inventor： Ankur Garg ,  Sunav Choudhary ,  Saurabh Kumar Mishra ,  Manoj Ghuhan A.

    IPC: G06N99/00 ,  H04L29/08 ,  G06N7/00 ,  G06F17/18

    Abstract: Systems and techniques are described herein for updating a machine learning model on edge servers. Local parameters of the machine learning model are updated at a plurality of edge servers using fresh data on the edge servers, rather than waiting for the data to reach a global server to update the machine learning model. Hence, latency is significantly reduced, making the systems and techniques described herein suitable for real-time services that support streaming data. Moreover, by updating global parameters of the machine learning model at a global server in a deterministic manner based on parameter updates from the edge servers, rather than by including randomization steps, global parameters of the converge quickly to their optimal values. The global parameters are sent from the global server to the plurality of edge servers at each iteration, thereby synchronizing the machine learning model on the edge servers.

    Scheduling jobs on interruptible cloud computing instances
    18.
    发明授权
    Scheduling jobs on interruptible cloud computing instances 有权

    公开(公告)号：US11915054B2

    公开(公告)日：2024-02-27

    申请号：US17324692

    申请日：2021-05-19

    Applicant: Adobe Inc.

    Inventor： Subrata Mitra ,  Sunav Choudhary ,  Sheng Yang ,  Kanak Vivek Mahadik ,  Samir Khuller

    IPC: G06F9/46 ,  G06F9/50 ,  G06F9/48

    CPC classification number: G06F9/5038 ,  G06F9/4818 ,  G06F9/4856 ,  G06F9/4881

    Abstract: Techniques are provided for scheduling multiple jobs on one or more cloud computing instances, which provide the ability to select a job for execution from among a plurality of jobs, and to further select a designated instance from among a plurality of cloud computing instances for executing the selected job. The job and the designated instance are each selected based on a probability distribution that a cost of executing the job on the designated instance does not exceed the budget. The probability distribution is based on several factors including a cost of prior executions of other jobs on the designated instance and a utility function that represents a value associated with a progress of each job. By scheduling select jobs on discounted cloud computing instances, the aggregate utility of the jobs can be maximized or otherwise improved for a given budget.

    Extracting seasonal, level, and spike components from a time series of metrics data
    20.
    发明授权
    Extracting seasonal, level, and spike components from a time series of metrics data 有权

    公开(公告)号：US11816120B2

    公开(公告)日：2023-11-14

    申请号：US16821132

    申请日：2020-03-17

    Applicant: Adobe Inc.

    Inventor： Shiv Kumar Saini ,  Sunav Choudhary ,  Gaurush Hiranandani

    IPC: G06F16/24 ,  G06F16/2458 ,  G06F16/248 ,  H04L9/40

    CPC classification number: G06F16/2474 ,  G06F16/248 ,  H04L63/1425

    Abstract: Certain embodiments involve extracting seasonal, level, and spike components from a time series of metrics data, which describe interactions with an online service over a time period. For example, an analytical system decomposes the time series into latent components that include a seasonal component series, a level component series, a spike component series, and an error component series. The decomposition involves configuring an optimization algorithm with a constraint indicating that the time series is a sum of these latent components. The decomposition also involves executing the optimization algorithm to minimize an objective function subject to the constraint and identifying, from the executed optimization algorithm, the seasonal component series, the level component series, the spike component series, and the error component series that minimize the objective function. The analytical system outputs at least some latent components for anomaly-detection or data-forecasting.

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