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公开(公告)号：US20210192549A1
公开(公告)日：2021-06-24
申请号：US16722626
申请日：2019-12-20
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Paridhi Maheshwari , Ayalur Vedpuriswar Lakshmy , Tanay Anand , Vishal Manohar Jain
IPC: G06Q30/02
Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for easily, accurately, and efficiently determining a personalized market share of a user with a company versus that of its competitors using only focal company's own clickstream data. For instance, the disclosed systems can infer a mapping of purchases to product categories from clickstream data of a company and use the mappings to generate a dataset of observable conversions (with interconversion times) for one or more product categories. Then, the disclosed systems can utilize models for a category level interconversion time and for transition probabilities of a user to determine a personalized market share and an interconversion time for an individual user (between the company and competitors of the company). In addition, the disclosed systems can generate graphical user interfaces that efficiently provide personalized customer statistics based at least on the determined personalized market share and interconversion times for the individual user.
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公开(公告)号：US11960520B2
公开(公告)日：2024-04-16
申请号：US17853141
申请日：2022-06-29
Applicant: Adobe Inc.
Inventor： Tanay Anand , Sumit Bhatia , Simra Shahid , Nikitha Srikanth , Nikaash Puri
IPC: G06F16/30 , G06F16/33 , G06F16/35 , G06F16/93 , G06F18/2133 , G06F18/2413 , G06F40/30
CPC classification number: G06F16/35 , G06F16/3347 , G06F16/93 , G06F18/2133 , G06F18/24147 , G06F40/30
Abstract: Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.
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公开(公告)号：US11682031B2
公开(公告)日：2023-06-20
申请号：US17376405
申请日：2021-07-15
Applicant: ADOBE INC.
Inventor： Paridhi Maheshwari , Tanay Anand , Atanu Sinha
IPC: G06Q30/0202 , G06Q30/0226 , G06Q30/0201 , G06F18/2415
CPC classification number: G06Q30/0202 , G06F18/2415 , G06Q30/0201 , G06Q30/0226
Abstract: A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.
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公开(公告)号：US20230015978A1
公开(公告)日：2023-01-19
申请号：US17376405
申请日：2021-07-15
Applicant: ADOBE INC.
Inventor： Paridhi Maheshwari , Tanay Anand , Atanu Sinha
Abstract: A method for predicting user purchase by a user of a first site includes: selecting a distribution representing a probability distribution (PD) of inter-purchase-times (IPTs) across the first site and a second other site for each user, assigning each purchase of each user to one of the first site and the second site according to a Stochastic model, combining the selected PD with the Stochastic model to generate a PD of IPTs for only the first online site, estimating parameters of the probability distribution of IPTs for the first site by applying a Statistical modeling approach to features of each user, applying a sequence of observed IPTs of a given user for the first site and the parameters of the given user to the selected distribution to generate a probability, and determining whether the next purchase occurs on the second site based on the probability.
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Patent Agency Ranking
公开(公告)号：US12223002B2
公开(公告)日：2025-02-11
申请号：US17454445
申请日：2021-11-10
Applicant: ADOBE INC.
Inventor： Pinkesh Badjatiya , Tanay Anand , Simra Shahid , Nikaash Puri , Milan Aggarwal , S Sejal Naidu , Sharat Chandra Racha
IPC: G06F16/9536 , G06F16/9538 , G06F40/20
Abstract: A method of finding online relevant conversing posts, comprises receiving, by a web server serving an online forum, a query post from an inquirer using the online forum, computing a contextual similarity score between each conversing post of a set of conversing posts with a query post, wherein the contextual similarity score is computed between the body of each of conversing posts and of the query post, wherein N1 conversing posts with a highest contextual similarity score are selected; computing a fine grained similarity score between the subject of the query post and of each of the N1 conversing posts, wherein N2 conversing posts with a highest fine grained similarity score are selected; and boosting the fine grained similarity score of the N2 conversing posts based on relevance metrics, wherein N3 highest ranked conversing posts are selected as a list of conversing posts most relevant to the query post.
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公开(公告)号：US12111884B2
公开(公告)日：2024-10-08
申请号：US17659983
申请日：2022-04-20
Applicant: ADOBE INC.
Inventor： Tanay Anand , Pinkesh Badjatiya , Sriyash Poddar , Jayakumar Subramanian , Georgios Theocharous , Balaji Krishnamurthy
IPC: G06F18/2137 , G06N3/088
CPC classification number: G06F18/2137 , G06N3/088
Abstract: Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.
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公开(公告)号：US20240005146A1
公开(公告)日：2024-01-04
申请号：US17855085
申请日：2022-06-30
Applicant: Adobe Inc. , Delhi Technological University
Inventor： Tanay Anand , Piyush Gupta , Pinkesh Badjatiya , Nikaash Puri , Jayakumar Subramanian , Balaji Krishnamurthy , Chirag Singla , Rachit Bansal , Anil Singh Parihar
CPC classification number: G06N3/08 , G06N3/0445
Abstract: In some embodiments, techniques for extracting high-value sequential patterns are provided. For example, a process may involve training a machine learning model to learn a state-action map that contains high-utility sequential patterns; extracting at least one high-utility sequential pattern from the trained machine learning model; and causing a user interface of a computing environment to be modified based on information from the at least one high-utility sequential pattern.
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公开(公告)号：US20240004912A1
公开(公告)日：2024-01-04
申请号：US17853141
申请日：2022-06-29
Applicant: Adobe Inc.
Inventor： Tanay Anand , Sumit Bhatia , Simra Shahid , Nikitha Srikanth , Nikaash Puri
CPC classification number: G06F16/35 , G06K9/6239 , G06K9/6276 , G06F16/93 , G06F40/30 , G06F16/3347
Abstract: Some techniques described herein relate to generating a hierarchical topic model (HTM), which can be used to generate custom content. In one example, a method includes determining first-level topics in a topic hierarchy related to a corpus of documents. A first-level topic of the first-level topics includes multiple words. The multiple words are grouped into clusters based on word embeddings of the multiple words. The multiple words are then subdivided into second-level topics as subtopics of the first-level topic, such that the number of second-level topics equals the number of clusters. A document of the corpus of documents is assigned to the first-level topic and to a second-level topic of the second-level topics, and an indication is received of access by a user to the document. Custom content is generated for the user based on one or more other documents assigned to the first-level topic and the second-level topic.
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公开(公告)号：US20230342425A1
公开(公告)日：2023-10-26
申请号：US17659983
申请日：2022-04-20
Applicant: ADOBE INC.
Inventor： Tanay Anand , Pinkesh Badjatiya , Sriyash Poddar , Jayakumar Subramanian , Georgios Theocharous , Balaji Krishnamurthy
CPC classification number: G06K9/6251 , G06N3/088
Abstract: Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.
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