Patents by Inventor Nikaash Puri

Nikaash Puri has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Patent number: 11450310
    Abstract: Systems and methods for spoken language understanding are described. Embodiments of the systems and methods receive audio data for a spoken language expression, encode the audio data using a multi-stage encoder comprising a basic encoder and a sequential encoder, wherein the basic encoder is trained to generate character features during a first training phase and the sequential encoder is trained to generate token features during a second training phase, and decode the token features to generate semantic information representing the spoken language expression.
    Type: Grant
    Filed: August 10, 2020
    Date of Patent: September 20, 2022
    Assignee: ADOBE INC.
    Inventors: Nikita Kapoor, Jaya Dodeja, Nikaash Puri
  • Patent number: 11423264
    Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.
    Type: Grant
    Filed: October 21, 2019
    Date of Patent: August 23, 2022
    Assignee: Adobe Inc.
    Inventors: Pinkesh Badjatiya, Nikaash Puri, Ayush Chopra, Anubha Kabra
  • Publication number: 20220253478
    Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.
    Type: Application
    Filed: April 26, 2022
    Publication date: August 11, 2022
    Applicant: Adobe Inc.
    Inventors: Ajay Jain, Sanjeev Tagra, Sachin Soni, Ryan Timothy Rozich, Nikaash Puri, Jonathan Stephen Roeder
  • Patent number: 11397764
    Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.
    Type: Grant
    Filed: January 28, 2020
    Date of Patent: July 26, 2022
    Assignee: Adobe Inc.
    Inventors: Ajay Jain, Sanjeev Tagra, Sachin Soni, Ryan Timothy Rozich, Nikaash Puri, Jonathan Stephen Roeder
  • Patent number: 11354590
    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.
    Type: Grant
    Filed: November 14, 2017
    Date of Patent: June 7, 2022
    Assignee: Adobe Inc.
    Inventors: Piyush Gupta, Sukriti Verma, Pratiksha Agarwal, Nikaash Puri, Balaji Krishnamurthy
  • Publication number: 20220044671
    Abstract: Systems and methods for spoken language understanding are described. Embodiments of the systems and methods receive audio data for a spoken language expression, encode the audio data using a multi-stage encoder comprising a basic encoder and a sequential encoder, wherein the basic encoder is trained to generate character features during a first training phase and the sequential encoder is trained to generate token features during a second training phase, and decode the token features to generate semantic information representing the spoken language expression.
    Type: Application
    Filed: August 10, 2020
    Publication date: February 10, 2022
    Inventors: NIKITA KAPOOR, Jaya Dodeja, Nikaash Puri
  • Publication number: 20210406935
    Abstract: Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained. A journey can include journey paths indicating workflows through which audience members can traverse. The journey data can include audience member attributes (e.g., demographics) and labels indicating journey paths traversed by audience members. A set of audience segments are determined that describe a set of audience members traversing a particular journey path. The set of audience segments can be determined using the journey data to train a segmentation model and, thereafter, analyzing the segmentation model to identify patterns that indicate audience segments associated with the particular journey path. An indication of the set of audience segments that describe the set of audience members traversing the particular journey path can be provided for display.
    Type: Application
    Filed: June 24, 2020
    Publication date: December 30, 2021
    Inventors: Pankhri SINGHAI, Piyush GUPTA, Balaji KRISHNAMURTHY, Jayakumar SUBRAMANIAN, Nikaash PURI
  • Patent number: 11188579
    Abstract: Systems and methods are described for serving personalized content using content tagging and transfer learning. The method may include identifying content elements in an experience pool, where each of the content element is associated with one or more attribute tags, identifying a user profile comprising characteristics of a user, generating a set of user-tag affinity vectors based on the user profile and the corresponding attribute tags using a content personalization engine, generating a user-content affinity score based on the set of user-tag affinity vectors, selecting a content element from the plurality of content elements based on the corresponding user-content affinity score, and delivering the selected content element to the user.
    Type: Grant
    Filed: April 8, 2019
    Date of Patent: November 30, 2021
    Assignee: ADOBE INC.
    Inventors: Dheeraj Bansal, Sukriti Verma, Pratiksha Agarwal, Piyush Gupta, Nikaash Puri, Vishal Wani, Balaji Krishnamurthy
  • Publication number: 20210319473
    Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.
    Type: Application
    Filed: June 23, 2021
    Publication date: October 14, 2021
    Applicant: Adobe Inc.
    Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nupur Kumari, Nikaash Puri, Mayank Singh, Eshita Shah, Balaji Krishnamurthy, Akash Rupela
  • Patent number: 11107115
    Abstract: Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users.
    Type: Grant
    Filed: August 7, 2018
    Date of Patent: August 31, 2021
    Assignee: Adobe Inc.
    Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nupur Kumari, Nikaash Puri, Mayank Singh, Eshita Shah, Balaji Krishnamurthy, Akash Rupela
  • Patent number: 11109084
    Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).
    Type: Grant
    Filed: November 25, 2019
    Date of Patent: August 31, 2021
    Assignee: Adobe Inc.
    Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nikaash Puri, Eshita Shah, Balaji Krishnamurthy, Nupur Kumari, Mayank Singh, Akash Rupela
  • Publication number: 20210232621
    Abstract: Digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. The plurality of digital images each capture the object for inclusion as part of generating digital content, e.g., a webpage, a thumbnail to represent a digital video, and so on. In one example, digital image selection techniques are described that employ machine learning to select a digital image of an object from a plurality of digital images of the object. As a result, the service provider system may select a digital image of an object from a plurality of digital images of the object that has an increased likelihood of achieving a desired outcome and may address the multitude of different ways in which an object may be presented to a user.
    Type: Application
    Filed: January 28, 2020
    Publication date: July 29, 2021
    Applicant: Adobe Inc.
    Inventors: Ajay Jain, Sanjeev Tagra, Sachin Soni, Ryan Timothy Rozich, Nikaash Puri, Jonathan Stephen Roeder
  • Publication number: 20210117718
    Abstract: A data classification system is trained to classify input data into multiple classes. The system is initially trained by adjusting weights within the system based on a set of training data that includes multiple tuples, each being a training instance and corresponding training label. Two training instances, one from a minority class and one from a majority class, are selected from the set of training data based on entropies for the training instances. A synthetic training instance is generated by combining the two selected training instances and a corresponding training label is generated. A tuple including the synthetic training instance and the synthetic training label is added to the set of training data, resulting in an augmented training data set. One or more such synthetic training instances can be added to the augmented training data set and the system is then re-trained on the augmented training data set.
    Type: Application
    Filed: October 21, 2019
    Publication date: April 22, 2021
    Applicant: Adobe Inc.
    Inventors: Pinkesh Badjatiya, Nikaash Puri, Ayush Chopra, Anubha Kabra
  • Publication number: 20210073671
    Abstract: Systems, methods, and non-transitory computer-readable media are disclosed for generating combined feature embeddings for minority class upsampling in training machine learning models with imbalanced training samples. For example, the disclosed systems can select training sample values from a set of training samples and a combination ratio value from a continuous probability distribution. Additionally, the disclosed systems can generate a combined synthetic training sample value by modifying the selected training sample values using the combination ratio value and combining the modified training sample values. Moreover, the disclosed systems can generate a combined synthetic ground truth label based on the combination ratio value. In addition, the disclosed systems can utilize the combined synthetic training sample value and the combined synthetic ground truth label to generate a combined synthetic training sample and utilize the combined synthetic training sample to train a machine learning model.
    Type: Application
    Filed: September 9, 2019
    Publication date: March 11, 2021
    Applicant: Adobe, Inc.
    Inventors: Nikaash Puri, Balaji Krishnamurthy, Ayush Chopra
  • Publication number: 20200364280
    Abstract: Collaborative-filtered content recommendations with justification in real-time is described. A recommendation system determines these recommendations, in part, by identifying digital content items of a catalog that are associated with a single attribute used to describe digital content. The attribute used for the identification is based on affinity scores computed for a client device user to which the recommendations are being provided. These affinity scores indicate the client device user's affinity for different attributes used to describe the digital content. Once the digital content items are identified based on the one attribute, the recommendation system is then limited to ranking and selecting from the identified digital content items to provide the recommendations. The recommendation system does not process the entire catalog of digital content items at once to rank and select the items.
    Type: Application
    Filed: August 3, 2020
    Publication date: November 19, 2020
    Applicant: Adobe Inc.
    Inventors: Nikaash Puri, Piyush Gupta
  • Publication number: 20200320112
    Abstract: Systems and methods are described for serving personalized content using content tagging and transfer learning. The method may include identifying content elements in an experience pool, where each of the content element is associated with one or more attribute tags, identifying a user profile comprising characteristics of a user, generating a set of user-tag affinity vectors based on the user profile and the corresponding attribute tags using a content personalization engine, generating a user-content affinity score based on the set of user-tag affinity vectors, selecting a content element from the plurality of content elements based on the corresponding user-content affinity score, and delivering the selected content element to the user.
    Type: Application
    Filed: April 8, 2019
    Publication date: October 8, 2020
    Inventors: Dheeraj Bansal, Sukriti Verma, Pratiksha Agarwal, Piyush Gupta, Nikaash Puri, Vishal Wani, Balaji Krishnamurthy
  • Patent number: 10762153
    Abstract: Collaborative-filtered content recommendations with justification in real-time is described. A recommendation system determines these recommendations, in part, by identifying digital content items of a catalog that are associated with a single attribute used to describe digital content. The attribute used for the identification is based on affinity scores computed for a client device user to which the recommendations are being provided. These affinity scores indicate the client device user's affinity for different attributes used to describe the digital content. Once the digital content items are identified based on the one attribute, the recommendation system is then limited to ranking and selecting from the identified digital content items to provide the recommendations. The recommendation system does not process the entire catalog of digital content items at once to rank and select the items.
    Type: Grant
    Filed: November 27, 2017
    Date of Patent: September 1, 2020
    Assignee: Adobe Inc.
    Inventors: Nikaash Puri, Piyush Gupta
  • Patent number: 10699321
    Abstract: A digital medium environment is described to facilitate recommendations based on vectors generated using feature word embeddings. A recommendation system receives data that describes at least one attribute for a user profile, at least one item, and an interaction between the user profile and the at least one item. The recommendation system associates each user profile attribute, each item, and each interaction between a user profile and an item as a word, using natural language processing, and combines the words into sentences. The sentences are input to a word embedding model to determine feature vector representations describing relationships between the user profile attributes, items, and explicit and implicit interactions. From the feature vector representations, the recommendation system ascertains a similarity between different features.
    Type: Grant
    Filed: October 17, 2017
    Date of Patent: June 30, 2020
    Assignee: Adobe Inc.
    Inventors: Balaji Krishnamurthy, Nikaash Puri
  • Publication number: 20200126100
    Abstract: Techniques are described for machine learning-based generation of target segments is leveraged in a digital medium environment. A segment targeting system generates training data to train a machine learning model to predict strength of correlation between a set of users and a defined demographic. Further, a machine learning model is trained with visit statistics for the users to predict the likelihood that the users will visit a particular digital content platform. Those users with the highest predicted correlation with the defined demographic and the highest likelihood to visit the digital content platform can be selected and placed within a target segment, and digital content targeted to the defined demographic can be delivered to users in the target segment.
    Type: Application
    Filed: October 23, 2018
    Publication date: April 23, 2020
    Applicant: Adobe Inc.
    Inventors: Praveen Kumar Goyal, Piyush Gupta, Nikaash Puri, Balaji Krishnamurthy, Arun Kumar, Atul Kumar Shrivastava
  • Patent number: 10609434
    Abstract: Machine-learning based multi-step engagement strategy generation and visualization is described. Rather than rely heavily on human involvement to create delivery strategies, the described learning-based engagement system generates multi-step engagement strategies by leveraging machine-learning models trained using data describing historical user interactions with content delivered in connection with historical campaigns. Initially, the learning-based engagement system obtains data describing an entry condition and an exit condition for a campaign. Based on the entry and exit condition, the learning-based engagement system utilizes the machine-learning models to generate a multi-step engagement strategy, which describes a sequence of content deliveries that are to be served to a particular client device user (or segment of client device users).
    Type: Grant
    Filed: August 7, 2018
    Date of Patent: March 31, 2020
    Assignee: Adobe Inc.
    Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nikaash Puri, Eshita Shah, Balaji Krishnamurthy, Nupur Kumari, Mayank Singh, Akash Rupela