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).
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Publication number: 20230196191Abstract: 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: ApplicationFiled: August 22, 2022Publication date: June 22, 2023Applicant: Adobe Inc.Inventors: Pinkesh Badjatiya, Nikaash Puri, Ayush Chopra, Anubha Kabra
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Publication number: 20230169271Abstract: Systems and methods for topic modeling are described. The systems and methods include encoding words of a document using an embedding matrix to obtain word embeddings for the document. The words of the document comprise a subset of words in a vocabulary, and the embedding matrix is trained as part of a topic attention network based on a plurality of topics. The systems and methods further include encoding a topic-word distribution matrix using the embedding matrix to obtain a topic embedding matrix. The topic-word distribution matrix represents relationships between the plurality of topics and the words of the vocabulary. The systems and methods further include computing a topic context matrix based on the topic embedding matrix and the word embeddings and identifying a topic for the document based on the topic context matrix.Type: ApplicationFiled: December 17, 2021Publication date: June 1, 2023Inventors: Shashank Shailabh, Madhur Panwar, Milan Aggarwal, Pinkesh Badjatiya, Simra Shahid, Nikaash Puri, S Sejal Naidu, Sharat Chandra Racha, Balaji Krishnamurthy, Ganesh Karbhari Palwe
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Publication number: 20230154186Abstract: Systems and methods for video processing are described. Embodiments of the present disclosure generate a plurality of image feature vectors corresponding to a plurality of frames of a video; generate a plurality of low-level event representation vectors based on the plurality of image feature vectors, wherein a number of the low-level event representation vectors is less than a number of the image feature vectors; generate a plurality of high-level event representation vectors based on the plurality of low-level event representation vectors, wherein a number of the high-level event representation vectors is less than the number of the low-level event representation vectors; and identify a plurality of high-level events occurring in the video based on the plurality of high-level event representation vectors.Type: ApplicationFiled: November 16, 2021Publication date: May 18, 2023Inventors: Sumegh Roychowdhury, Sumedh A. Sontakke, Mausoom Sarkar, Nikaash Puri, Pinkesh Badjatiya, Milan Aggarwal
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Publication number: 20230143777Abstract: 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.Type: ApplicationFiled: November 10, 2021Publication date: May 11, 2023Inventors: Pinkesh BADJATIYA, Tanay ANAND, Simra SHAHID, Nikaash PURI, Milan AGGARWAL, S Sejal NAIDU, Sharat Chandra RACHA
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Patent number: 11645541Abstract: A technique is disclosed for generating class level rules that globally explain the behavior of a machine learning model, such as a model that has been used to solve a classification problem. Each class level rule represents a logical conditional statement that, when the statement holds true for one or more instances of a particular class, predicts that the respective instances are members of the particular class. Collectively, these rules represent the pattern followed by the machine learning model. The techniques are model agnostic, and explain model behavior in a relatively easy to understand manner by outputting a set of logical rules that can be readily parsed. Although the techniques can be applied to any number of applications, in some embodiments, the techniques are suitable for interpreting models that perform the task of classification. Other machine learning model applications can equally benefit.Type: GrantFiled: November 17, 2017Date of Patent: May 9, 2023Assignee: Adobe Inc.Inventors: Piyush Gupta, Nikaash Puri, Balaji Krishnamurthy
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Patent number: 11631029Abstract: 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: GrantFiled: September 9, 2019Date of Patent: April 18, 2023Assignee: Adobe Inc.Inventors: Nikaash Puri, Balaji Krishnamurthy, Ayush Chopra
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Publication number: 20230085466Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for determining user affinities by tracking historical user interactions with tagged digital content and using the user affinities in content generation applications. Accordingly, the system may track user interactions with published digital content in order to generate user interaction reports whenever a user engages with the digital content. The system may aggregate the interaction reports to generate an affinity profile for a user or audience of users. A marketer may then generate digital content for a target user or audience of users and the system may process the digital content to generate a set of tags for the digital content. Based on the set of tags, the system may then evaluate the digital content in view of the affinity profile for the target user/audience to determine similarities or differences between the digital content and the affinity profile.Type: ApplicationFiled: September 16, 2021Publication date: March 16, 2023Inventors: Yaman Kumar, Vinh Ngoc Khuc, Vijay Srivastava, Umang Moorarka, Sukriti Verma, Simra Shahid, Shirsh Bansal, Shankar Venkitachalam, Sean Steimer, Sandipan Karmakar, Nimish Srivastav, Nikaash Puri, Mihir Naware, Kunal Kumar Jain, Kumar Mrityunjay Singh, Hyman Chung, Horea Bacila, Florin Silviu Iordache, Deepak Pai, Balaji Krishnamurthy
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Patent number: 11544336Abstract: 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: GrantFiled: August 3, 2020Date of Patent: January 3, 2023Assignee: Adobe Inc.Inventors: Nikaash Puri, Piyush Gupta
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Patent number: 11538051Abstract: 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: GrantFiled: October 23, 2018Date of Patent: December 27, 2022Assignee: Adobe Inc.Inventors: Praveen Kumar Goyal, Piyush Gupta, Nikaash Puri, Balaji Krishnamurthy, Arun Kumar, Atul Kumar Shrivastava
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Publication number: 20220335508Abstract: Interactions between a user and an e-commerce platform are automatically guided to increase the chances of a conversion. Previous sequences of interactions (e.g., conversion journeys and non-conversion journeys) with the e-commerce platform are collected, an artificial neural network (ANN) learns how to estimate a safety value a current user state by learning from previous user interactions (e.g., conversion and non-conversion journeys), a software agent of the e-commerce platform applies a current user state of the user to the ANN to determine a current safety value, and the software agent provides content to the user based on the current safety value and the current user state.Type: ApplicationFiled: April 16, 2021Publication date: October 20, 2022Inventors: Sukriti Verma, Shripad Deshmukh, Jayakumar Subramanian, Piyush Gupta, Nikaash Puri
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Patent number: 11450310Abstract: 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: GrantFiled: August 10, 2020Date of Patent: September 20, 2022Assignee: ADOBE INC.Inventors: Nikita Kapoor, Jaya Dodeja, Nikaash Puri
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Patent number: 11423264Abstract: 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: GrantFiled: October 21, 2019Date of Patent: August 23, 2022Assignee: Adobe Inc.Inventors: Pinkesh Badjatiya, Nikaash Puri, Ayush Chopra, Anubha Kabra
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Publication number: 20220253478Abstract: 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: ApplicationFiled: April 26, 2022Publication date: August 11, 2022Applicant: Adobe Inc.Inventors: Ajay Jain, Sanjeev Tagra, Sachin Soni, Ryan Timothy Rozich, Nikaash Puri, Jonathan Stephen Roeder
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Patent number: 11397764Abstract: 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: GrantFiled: January 28, 2020Date of Patent: July 26, 2022Assignee: Adobe Inc.Inventors: Ajay Jain, Sanjeev Tagra, Sachin Soni, Ryan Timothy Rozich, Nikaash Puri, Jonathan Stephen Roeder
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Patent number: 11354590Abstract: 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: GrantFiled: November 14, 2017Date of Patent: June 7, 2022Assignee: Adobe Inc.Inventors: Piyush Gupta, Sukriti Verma, Pratiksha Agarwal, Nikaash Puri, Balaji Krishnamurthy
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Publication number: 20220044671Abstract: 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: ApplicationFiled: August 10, 2020Publication date: February 10, 2022Inventors: NIKITA KAPOOR, Jaya Dodeja, Nikaash Puri
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Publication number: 20210406935Abstract: 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: ApplicationFiled: June 24, 2020Publication date: December 30, 2021Inventors: Pankhri SINGHAI, Piyush GUPTA, Balaji KRISHNAMURTHY, Jayakumar SUBRAMANIAN, Nikaash PURI
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Patent number: 11188579Abstract: 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: GrantFiled: April 8, 2019Date of Patent: November 30, 2021Assignee: ADOBE INC.Inventors: Dheeraj Bansal, Sukriti Verma, Pratiksha Agarwal, Piyush Gupta, Nikaash Puri, Vishal Wani, Balaji Krishnamurthy
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Publication number: 20210319473Abstract: 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: ApplicationFiled: June 23, 2021Publication date: October 14, 2021Applicant: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nupur Kumari, Nikaash Puri, Mayank Singh, Eshita Shah, Balaji Krishnamurthy, Akash Rupela
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Patent number: 11109084Abstract: 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: GrantFiled: November 25, 2019Date of Patent: August 31, 2021Assignee: Adobe Inc.Inventors: Pankhri Singhai, Sundeep Parsa, Piyush Gupta, Nikaash Puri, Eshita Shah, Balaji Krishnamurthy, Nupur Kumari, Mayank Singh, Akash Rupela