-
公开(公告)号：US11561750B2
公开(公告)日：2023-01-24
申请号：US17091335
申请日：2020-11-06
Applicant: Adobe Inc.
Inventor： Jennifer Healey , Haoliang Wang , Georgios Theocharous
IPC: G06Q30/00 , G06F3/14 , G06Q30/06 , G06Q10/08 , G06K7/10 , G06K19/07 , G06Q30/02 , G06F16/54 , G06F16/535 , G01C21/20 , G06F40/279 , G06Q20/20
Abstract: This disclosure describes embodiments of methods, systems, and non-transitory-computer readable media that personalize visual content for display on digital signage near a projected location of a person by mapping visual content to physical items selected by the person. In some examples, the disclosed system identifies physical items selected by a person based on signals from the physical items, such as signals emitted by RFID tags affixed to (or other devices associated with) the physical items. The disclosed system analyzes the collection of physical items—as identified by the signals—to tailor digital signage content specific to the person. The disclosed system further tracks the location of the person as the person moves through a physical space and interacts with the physical items. Based on the tracked positions, the disclosed system determines a digital sign in proximity to a predicted location of the person to display the personalized visual content.
-
12.发明申请公开(公告)号：US20220343155A1
公开(公告)日：2022-10-27
申请号：US17337998
申请日：2021-06-03
Applicant: Adobe Inc.
Inventor： Saayan Mitra , Gang Wu , Georgios Theocharous , Richard Whitehead , Viswanathan Swaminathan , Zahraa Parekh , Ben Tepfer
Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that intelligently generate and modify schedules of task sequences utilizing a graph neural network and/or reinforcement learning model. For example, the disclosed system utilizes a graph neural network to generate performance efficiency scores indicating predicted performances of the sets of tasks. Additionally, the disclosed systems utilizes the performance efficiency scores to rank sets of tasks and then determine a schedule including an ordered sequence of tasks. Furthermore, disclosed system generates modified schedules in response to detecting a modification to the schedule. For example, the disclosed system utilizes a reinforcement learning model to provide recommendations of new tasks or task sequences deviating from the schedule in the event of an interruption. The disclosed system also utilizes the reinforcement learning model to learn from user choices to inform future scheduling of tasks.
-
公开(公告)号：US20220148015A1
公开(公告)日：2022-05-12
申请号：US17096255
申请日：2020-11-12
Applicant: Adobe Inc.
Inventor： Tung Mai , Iftikhar Ahamath Burhanuddin , Georgios Theocharous , Anup Rao
Abstract: Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.
-
公开(公告)号：US20210357255A1
公开(公告)日：2021-11-18
申请号：US16867104
申请日：2020-05-05
Applicant: ADOBE INC.
Inventor： Kanak Vivek Mahadik , Ryan A. Rossi , Sana Malik Lee , Georgios Theocharous , Handong Zhao , Gang Wu , Youngsuk Park
Abstract: A system and method for automatically adjusting computing resources provisioned for a computer service or application by applying historical resource usage data to a predictive model to generate predictive resource usage. The predictive resource usage is then simulated for various service configurations, determining scaling requirements and resource wastage for each configuration. A cost value is generated based on the scaling requirement and resource wastage, with the cost value for each service configuration used to automatically select a configuration to apply to the service. Alternatively, the method for automatically adjusting computer resources provisioned for a service may include receiving resource usage data of the service, applying it to a linear quadratic regulator (LQR) to find an optimal stationary policy (treating the resource usage data as states and resource-provisioning variables as actions), and providing instructions for configuring the service based on the optimal stationary policy.
-
公开(公告)号：US20210097350A1
公开(公告)日：2021-04-01
申请号：US16584082
申请日：2019-09-26
Applicant: Adobe Inc.
Inventor： Georgios Theocharous , Zheng Wen , Yasin Abbasi Yadkori , Qingyun Wu
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing offline models to warm start online bandit learner models. For example, the disclosed system can determine relevant offline models for an environment based on reward estimate differences between the offline models and the online model. The disclosed system can then utilize the relevant offline models (if any) to select an arm for the environment. The disclosed system can update the online model based on observed rewards for the selected arm. Additionally, the disclosed system can also use entropy reduction of arms to determine the utility of the arms in differentiating relevant and irrelevant offline models. For example, the disclosed system can select an arm based on a combination of the entropy reduction of the arm and the reward estimate for the arm and use the observed reward to update an observation history.
-
Patent Agency Ranking
公开(公告)号：US12130841B2
公开(公告)日：2024-10-29
申请号：US16933361
申请日：2020-07-20
Applicant: Adobe Inc.
Inventor： Karan Aggarwal , Georgios Theocharous , Anup Rao
IPC: G06F16/28 , G05B23/02 , G06F16/35 , G06F16/45 , G06F18/23213 , G06N3/02 , G06N3/044 , G06N3/0442 , G06N3/08 , G06V30/19 , G06F9/54 , G06N3/043 , G06N7/04
CPC classification number: G06F16/285 , G05B23/0281 , G06F16/35 , G06F16/45 , G06F18/23213 , G06N3/02 , G06N3/044 , G06N3/0442 , G06N3/08 , G06V30/19107 , G06F9/542 , G06N3/043 , G06N7/046
Abstract: A single unified machine learning model (e.g., a neural network) is trained to perform both supervised event predictions and unsupervised time-varying clustering for a sequence of events (e.g., a sequence representing a user behavior) using sequences of events for multiple users using a combined loss function. The unified model can then be used for, given a sequence of events as input, predict a next event to occur after the last event in the sequence and generate a clustering result by performing a clustering operation on the sequence of events. As part of predicting the next event, the unified model is trained to predict an event type for the next event and a time of occurrence for the next event. In certain embodiments, the unified model is a neural network comprising a recurrent neural network (RNN) such as an Long Short Term Memory (LSTM) network.
-
公开(公告)号：US11978067B2
公开(公告)日：2024-05-07
申请号：US17096255
申请日：2020-11-12
Applicant: Adobe Inc.
Inventor： Tung Mai , Iftikhar Ahamath Burhanuddin , Georgios Theocharous , Anup Rao
IPC: G06Q30/0201 , G06F3/14 , G06F7/08 , G06Q10/10
CPC classification number: G06Q30/0201 , G06F7/08 , G06Q10/10 , G06F3/14
Abstract: Techniques are provided for analyzing user actions that have occurred over a time period. The user actions can be, for example, with respect to the user's navigation of content or interaction with an application. Such user data is provided in an action string, which is converted into a highly searchable format. As such, the presence and frequency of particular user actions and patterns of user actions within an action string of a particular user, as well as among multiple action strings of multiple users, are determinable. Subsequences of one or more action strings are identified and both the number of action strings that include a particular subsequence and the frequency that a particular subsequence is present in a given action string are determinable. The conversion involves breaking that string into a sorted list of locations for the actions within that string. Queries can be readily applied against the sorted list.
-
公开(公告)号：US20230259829A1
公开(公告)日：2023-08-17
申请号：US18306449
申请日：2023-04-25
Applicant: Adobe Inc.
Inventor： Georgios Theocharous , Zheng Wen , Yasin Abbasi Yadkori , Qingyun Wu
CPC classification number: G06N20/00 , G06N5/04 , G06F18/2193
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing offline models to warm start online bandit learner models. For example, the disclosed system can determine relevant offline models for an environment based on reward estimate differences between the offline models and the online model. The disclosed system can then utilize the relevant offline models (if any) to select an arm for the environment. The disclosed system can update the online model based on observed rewards for the selected arm. Additionally, the disclosed system can also use entropy reduction of arms to determine the utility of the arms in differentiating relevant and irrelevant offline models. For example, the disclosed system can select an arm based on a combination of the entropy reduction of the arm and the reward estimate for the arm and use the observed reward to update an observation history.
-
公开(公告)号：US11669768B2
公开(公告)日：2023-06-06
申请号：US16584082
申请日：2019-09-26
Applicant: Adobe Inc.
Inventor： Georgios Theocharous , Zheng Wen , Yasin Abbasi Yadkori , Qingyun Wu
CPC classification number: G06F18/2193 , G06N5/04 , G06N20/00
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for utilizing offline models to warm start online bandit learner models. For example, the disclosed system can determine relevant offline models for an environment based on reward estimate differences between the offline models and the online model. The disclosed system can then utilize the relevant offline models (if any) to select an arm for the environment. The disclosed system can update the online model based on observed rewards for the selected arm. Additionally, the disclosed system can also use entropy reduction of arms to determine the utility of the arms in differentiating relevant and irrelevant offline models. For example, the disclosed system can select an arm based on a combination of the entropy reduction of the arm and the reward estimate for the arm and use the observed reward to update an observation history.
-
公开(公告)号：US11669755B2
公开(公告)日：2023-06-06
申请号：US16921202
申请日：2020-07-06
Applicant: Adobe Inc.
Inventor： Atanu R Sinha , Tanay Asija , Sunny Dhamnani , Raja Kumar Dubey , Navita Goyal , Kaarthik Raja Meenakshi Viswanathan , Georgios Theocharous
Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.
-
-
-
-
-
-
-
-
-
Search Results
53
records
(took 0.018 seconds)
