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公开(公告)号：US20210311751A1
公开(公告)日：2021-10-07
申请号：US17350889
申请日：2021-06-17
Applicant: Adobe Inc.
Inventor： Nikhil Sheoran , Nayan Raju Vysyaraju , Varun Srivastava , Nisheeth Golakiya , Dhruv Singal , Deepali Jain , Atanu Sinha
Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.
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公开(公告)号：US12079217B2
公开(公告)日：2024-09-03
申请号：US17741811
申请日：2022-05-11
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Yash Gadhia , Tong Yu , Shaddy Garg , Nikhil Sheoran , Arjun Kashettiwar , Anjali Yadav
IPC: G06F16/2455 , G06F16/2453 , G06F16/2457 , G06F16/2458 , G06F18/21
CPC classification number: G06F16/2455 , G06F16/24542 , G06F16/2457 , G06F16/2474 , G06F18/217
Abstract: Some techniques described herein relate to utilizing a machine-learning (ML) model to select respective samples for queries of a query sequence. In one example, a method includes receiving a query in a query sequence, where the query is directed toward a dataset. Samples are available as down-sampled versions of the dataset. The method further include applying an agent to select, for the query, a sample from among the samples of the dataset. The agent includes an ML model trained, such as via intent-based reinforcement learning, to select respective samples for queries. The query is then executed against the sample to output a response.
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公开(公告)号：US20230306318A1
公开(公告)日：2023-09-28
申请号：US17656263
申请日：2022-03-24
Applicant: ADOBE INC.
Inventor： Shaddy Garg , Shubham Agarwal , Sumit Bisht , Chahat Jain , Ashritha Gonuguntla , Nikhil Sheoran , Shiv Kumar Saini
Abstract: A method and system for outage forecasting are described. One or more aspects of the method and system include receiving, by a machine learning model, time series data for a service metric of a computer network; generating, by the machine learning model, probability distribution information for the service metric based on the time series data, wherein the probability distribution information is generated using a machine learning model that is trained using a distribution loss and a classification loss; and generating, by a forecasting component, outage forecasting information for the computer network based on the probability distribution information.
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公开(公告)号：US20210241158A1
公开(公告)日：2021-08-05
申请号：US17236506
申请日：2021-04-21
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Deepali Jain , Nikhil Sheoran , Deepali Gupta , Sopan Khosla
Abstract: In some embodiments, a computing system computes, with a state prediction model, probabilities of transitioning from a click state represented by interaction data to various predicted next states. The computing system computes an interface experience metric for the click with an experience valuation model. To do so, the computing system identifies base values for the click state and the predicted next states. The computing system computes value differentials for between the click state's base value and each predicted next state's base value. Value differentials indicate qualities of interface experience. The computing system determines the interface experience metric from a summation that includes the current click state's base value and the value differentials weighted with the predicted next states' probabilities. The computing system transmits the interface experience metric to an online platform, which can cause interface elements of the online platform to be modified based on the interface experience metric.
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公开(公告)号：US11068285B2
公开(公告)日：2021-07-20
申请号：US16576310
申请日：2019-09-19
Applicant: Adobe Inc.
Inventor： Nikhil Sheoran , Nayan Raju Vysyaraju , Varun Srivastava , Nisheeth Golakiya , Dhruv Singal , Deepali Jain , Atanu Sinha
Abstract: In some embodiments, interaction data associated with user interactions with a user interface of an interactive computing environment is identified, and goal clusters of the interaction data are computed based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, likelihood values of additional sequences of user interactions falling within the goal clusters are computed based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Computing interface experience metrics of the additional sequences are computed using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.
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Patent Agency Ranking
6.发明公开公开(公告)号：US20240232775A9
公开(公告)日：2024-07-11
申请号：US17969643
申请日：2022-10-19
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Shiv Kumar Saini , Prithvi Bhutani , Nikhil Sheoran , Kevin Cobourn , Jeff D. Chasin , Fan Du , Eric Matisoff
IPC: G06Q10/0639 , G06F3/0484
CPC classification number: G06Q10/06393 , G06F3/0484
Abstract: In some examples, an environment evaluation system accesses interaction data recording interactions by users with an online platform hosted by a host system and computes, based on the interaction data, interface experience metrics. The interface experience metrics includes an individual experience metric for each user and a transition experience metric for each transition in the interactions by the users with the online platform. The environment evaluation system identifies a user with the individual experience metric below a pre-determined threshold, identifies a transition performed by the user that has a transition experience metric below a second threshold, and analyzes the transition to determine users who have performed the transition. The environment evaluation system updates the host system with the individual experience metrics and the transition metrics, based on which the host system can perform modifications of interface elements of the online platform to improve the experience.
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7.发明授权公开(公告)号：US11551239B2
公开(公告)日：2023-01-10
申请号：US16162023
申请日：2018-10-16
Applicant: Adobe Inc.
Inventor： Deepali Jain , Atanu R. Sinha , Deepali Gupta , Nikhil Sheoran , Sopan Khosla , Reshmi Naduparambil Sasidharan
IPC: G06Q30/02 , G06Q30/00 , G06F17/18 , G06F3/0484
Abstract: There is described a method and system in an interactive computing environment modified with user experience values based on behavior logs. An experience valuation system determines an experience value and an estimated experience value. The experience value is based on a current state of interaction data from a user session, based on a history of past events, and an estimation function defined by parameters to model the user experience values. The estimated experience value is determined based on, in addition to the current state and the estimation function, next states associated with the current state, and a reward function. The parameters of the estimation function are updated based on a comparison of the expected experience value and the estimated experience value. For another aspect, the method and system may further include a state prediction system to determine probabilities of transitioning that may be applied to determine the estimated experience value.
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公开(公告)号：US11544281B2
公开(公告)日：2023-01-03
申请号：US17100618
申请日：2020-11-20
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Nikhil Sheoran , Anup Rao , Tung Mai , Sapthotharan Krishnan Nair , Shivakumar Vaithyanathan , Thomas Jacobs , Ghetia Siddharth , Jatin Varshney , Vikas Maddukuri , Laxmikant Mishra
IPC: G06F16/2458 , G06F16/215 , G06F16/28 , G06F16/22 , G06N20/00 , G06K9/62
Abstract: In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.
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公开(公告)号：US20220164346A1
公开(公告)日：2022-05-26
申请号：US17100618
申请日：2020-11-20
Applicant: Adobe Inc.
Inventor： Subrata Mitra , Nikhil Sheoran , Anup Rao , Tung Mai , Sapthotharan Krishnan Nair , Shivakumar Vaithyanathan , Thomas Jacobs , Ghetia Siddharth , Jatin Varshney , Vikas Maddukuri , Laxmikant Mishra
IPC: G06F16/2458 , G06F16/215 , G06F16/28 , G06F16/22 , G06K9/62 , G06N20/00
Abstract: In some embodiments, a model training system trains a sample generation model configured to generate synthetic data entries for a dataset. The sample generation model includes a prior model for generating an estimated latent vector from a partially observed data entry, a proposal model for generating a latent vector from a data entry of the dataset and a mask corresponding to the partially observed data entry, and a generative model for generating the synthetic data entries from the latent vector and the partially observed data entry. The model training system trains the sample generation model to optimize an objective function that includes a first term determined using the synthetic data entries and a second term determined using the estimated latent vector and the latent vector. The trained sample generation model can be executed on a client computing device to service queries using the generated synthetic data entries.
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公开(公告)号：US20190311279A1
公开(公告)日：2019-10-10
申请号：US15946884
申请日：2018-04-06
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Deepali Jain , Nikhil Sheoran , Deepali Gupta , Sopan Khosla
Abstract: In some embodiments, a computing system computes, with a state prediction model, probabilities of transitioning from a click state represented by interaction data to various predicted next states. The computing system computes an interface experience metric for the click with an experience valuation model. To do so, the computing system identifies base values for the click state and the predicted next states. The computing system computes value differentials for between the click state's base value and each predicted next state's base value. Value differentials indicate qualities of interface experience. The computing system determines the interface experience metric from a summation that includes the current click state's base value and the value differentials weighted with the predicted next states' probabilities. The computing system transmits the interface experience metric to an online platform, which can cause interface elements of the online platform to be modified based on the interface experience metric.
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