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公开(公告)号：US20190147369A1
公开(公告)日：2019-05-16
申请号：US15812991
申请日：2017-11-14
Applicant: Adobe Inc.
Inventor： Piyush Gupta , Sukriti Verma , Pratiksha Agarwal , Nikaash Puri , Balaji Krishnamurthy
Abstract: Rule determination for black-box machine-learning models (BBMLMs) is described. These rules are determined by an interpretation system to describe operation of a BBMLM to associate inputs to the BBMLM with observed outputs of the BBMLM and without knowledge of the logic used in operation by the BBMLM to make these associations. To determine these rules, the interpretation system initially generates a proxy black-box model to imitate the behavior of the BBMLM based solely on data indicative of the inputs and observed outputs—since the logic actually used is not available to the system. The interpretation system generates rules describing the operation of the BBMLM by combining conditions—identified based on output of the proxy black-box model—using a genetic algorithm. These rules are output as if-then statements configured with an if-portion formed as a list of the conditions and a then-portion having an indication of the associated observed output.
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公开(公告)号：US10268883B2
公开(公告)日：2019-04-23
申请号：US15674100
申请日：2017-08-10
Applicant: Adobe Inc.
Inventor： Mausoom Sarkar , Balaji Krishnamurthy
Abstract: A method and system for detecting and extracting accurate and precise structure in documents. A high-resolution image of documents is segmented into a set of tiles. Each tile is processed by a convolutional network and subsequently by a set of recurrent networks for each row and column. A global-lookup process is disclosed that allows “future” information required for accurate assessment by the recurrent neural networks to be considered. Utilization of high-resolution image allows for precise and accurate feature extraction while segmentation into tiles facilitates the tractable processing of the high-resolution image within reasonable computational resource bounds.
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公开(公告)号：US20190114673A1
公开(公告)日：2019-04-18
申请号：US15787369
申请日：2017-10-18
Applicant: Adobe Inc.
Inventor： Piyush Gupta , Nikaash Puri , Balaji Krishnamurthy
Abstract: Digital experience targeting techniques are disclosed which serve digital experiences that have a high probability of conversion with regard to a given user visit profile. In some examples, a method may include predicting a probability of each digital experience in a campaign being served based on a user visit profile and an indication that a user exhibiting the user visit profile is going to convert, predicting a probability of each digital experience in the campaign being served based on the user visit profile and an indication that the user exhibiting the user visit profile is not going to convert, and deriving, for the user visit profile, a probability of conversion for each digital experience in the campaign. The probability of conversion for each digital experience in the campaign for the user visit profile may be derived using a Bayesian framework.
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公开(公告)号：US20240403651A1
公开(公告)日：2024-12-05
申请号：US18328174
申请日：2023-06-02
Applicant: Adobe Inc.
Inventor： Shripad Vilasrao Deshmukh , Arpan Dasgupta , Balaji Krishnamurthy , Chirag Agarwal , Georgios Theocharous , Jayakumar Subramanian
IPC: G06N3/092
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that provide a trajectory-based explainability framework for reinforcement learning models. For example, the disclosed systems generate trajectory clusters from trajectories utilized to train a reinforcement learning agent. In some embodiments, the disclosed system generates a complementary target data set by removing a target trajectory cluster from the trajectory clusters. In some cases, the disclosed system trains a test reinforcement learning agent utilizing the complementary target data set and generates a cluster attribution by comparing the result of the test reinforcement learning agent with the result of the reinforcement learning agent.
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公开(公告)号：US12124497B1
公开(公告)日：2024-10-22
申请号：US18190686
申请日：2023-03-27
Applicant: Adobe Inc.
Inventor： Abhinav Java , Surgan Jandial , Shripad Vilasrao Deshmukh , Milan Aggarwal , Mausoom Sarkar , Balaji Krishnamurthy , Arneh Jain
IPC: G06F16/383 , G06F16/332 , G06V30/19 , G06V30/412
CPC classification number: G06F16/383 , G06F16/332 , G06V30/19147 , G06V30/412
Abstract: Form structure similarity detection techniques are described. A content processing system, for instance, receives a query snippet that depicts a query form structure. The content processing system generates a query layout string that includes semantic indicators to represent the query form structure and generates candidate layout strings that represent form structures from a target document. The content processing system calculates similarity scores between the query layout string and the candidate layout strings. Based on the similarity scores, the content processing system generates a target snippet for display that depicts a form structure that is structurally similar to the query form structure. The content processing system is further operable to generate a training dataset that includes image pairs of snippets depicting form structures that are structurally similar. The content processing system utilizes the training dataset to train a machine learning model to perform form structure similarity matching.
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Patent Agency Ranking
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：US12086728B2
公开(公告)日：2024-09-10
申请号：US18135948
申请日：2023-04-18
Applicant: Adobe Inc.
Inventor： Milan Aggarwal , Mausoom Sarkar , Balaji Krishnamurthy
Abstract: Techniques described herein extract form structures from a static form to facilitate making that static form reflowable. A method described herein includes accessing low-level form elements extracted from a static form. The method includes determining, using a first set of prediction models, second-level form elements based on the low-level form elements. Each second-level form element includes a respective one or more low-level form elements. The method further includes determining, using a second set of prediction models, high-level form elements based on the second-level form elements and the low-level form elements. Each high-level form element includes a respective one or more second-level form elements or low-level form elements. The method further includes generating a reflowable form based on the static form by, for each high-level form element, linking together the respective one or more second-level form elements or low-level form elements.
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公开(公告)号：US20240296335A1
公开(公告)日：2024-09-05
申请号：US18112911
申请日：2023-02-22
Applicant: ADOBE INC.
Inventor： Surgan Jandial , Shripad Vilasrao Deshmukh , Balaji Krishnamurthy
Abstract: In various examples, a student model is trained based on a teacher model and a past student model. For example, a first set of labels are generated by a teacher model based on training data, a subset of labels are replace with labels generated by a past student model based on the training data, and a student model it trained based on these labels and the training data.
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公开(公告)号：US20240242394A1
公开(公告)日：2024-07-18
申请号：US18097856
申请日：2023-01-17
Applicant: Adobe Inc.
Inventor： Puneet Mangla , Balaji Krishnamurthy
IPC: G06T11/00 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/82
CPC classification number: G06T11/00 , G06V10/764 , G06V10/774 , G06V10/776 , G06V10/82
Abstract: In implementations of systems for non-adversarial image generation using transfer learning, a computing device implements a generation system to receive input data describing random noise. The generation system generates a latent representation in a latent space of a machine learning model based on the random noise using a transformer model that is trained to generate latent representations in the latent space. A digital image is generated using the machine learning model based on the latent representation that depicts an object that is visually similar to objects depicted in digital images of a training dataset used to train the machine learning model based on a perceptual loss.
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公开(公告)号：US11997056B2
公开(公告)日：2024-05-28
申请号：US17897419
申请日：2022-08-29
Applicant: ADOBE INC.
Inventor： Sumit Bhatia , Jivat Neet Kaur , Rachit Bansal , Milan Aggarwal , Balaji Krishnamurthy
IPC: H04L51/02 , G06F40/295 , G06N5/022
CPC classification number: H04L51/02 , G06F40/295 , G06N5/022
Abstract: The technology described herein receives a natural-language sequence of words comprising multiple entities. The technology then identifies a plurality of entities in the natural-language sequence. The technology generates a masked natural-language sequence by masking a first entity in the natural-language sequence. The technology retrieves, from a knowledge base, information related to a second entity in the plurality of entities. The technology then trains a natural-language model to respond to a query. The training uses a first representation of the masked natural-language sequence, a second representation of the information, and the first entity.
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