Deepak Te PaiSunnyvale, CA

2022030, Sep 22, 2022

Mar 16, 2021

17/203300

- SAN JOSE CA, US
Ganesh Satish Mallya - Santa Clara CA, US
Shankar Venkitachalam - Santa Clara CA, US
Deepak Pai - Sunnyvale CA, US

G06F 16/906
G06F 16/9035
G06F 16/901

Enhanced methods for improving the performance of classifiers are described. A ground-truth labeled dataset is accessed. A classifier predicts a predicted label for datapoints of the dataset. A confusion matrix for the dataset and classifier is generated. A credibility interval is determined for a performance metric for each label. A first labels with a sufficiently large credibility interval is identified. A second label is identified, where the classifier is likely to confuse, in its predictions, the first label with the second label. The identification of the second label is based on instances of incorrect label predictions of the classifier for the first and/or the second labels. The classifier is updated based on a new third label that includes an aggregation of the first label and the second label. The updated classifier model predicts the third label for any datapoint that the classifier previously predicted the first or second labels.

2022021, Jul 7, 2022

Mar 24, 2022

17/703188

- San Jose CA, US
Shankar Venkitachalam - Sunnyvale CA, US
Deepak Pai - Santa Clara CA, US

G06F 11/34
G06N 20/00
G06F 9/451

In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

2023008, Mar 16, 2023

Sep 16, 2021

17/447908

- SAN JOSE CA, US
Vinh Ngoc Khuc - Campbell CA, US
Vijay Srivastava - Cupertino CA, US
Umang Moorarka - Chhattisgarh, IN
Sukriti Verma - Rohini, IN
Simra Shahid - Uttar Pradesh, IN
Shirsh Bansal - Uttar Pradesh, IN
Shankar Venkitachalam - Santa Clara CA, US
Sean Steimer - San Jose CA, US
Sandipan Karmakar - Fremont CA, US
Nimish Srivastav - Santa Clara CA, US
Nikaash Puri - New Delhi, IN
Mihir Naware - Redwood City CA, US
Kunal Kumar Jain - Chennai, IN
Kumar Mrityunjay Singh - Bengaluru, IN
Hyman Chung - San Ramon CA, US
Horea Bacila - London, GB
Florin Silviu Iordache - Bucharest, RO
Deepak Pai - Sunnyvale CA, US
Balaji Krishnamurthy - Uttar Pradesh, IN

G06F 16/58
G06N 20/00
G06F 16/535
G06F 16/583
G06F 16/54

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.

2021024, Aug 5, 2021

Jan 31, 2020

16/778906

- San Jose CA, US
Deepak Pai - Santa Clara CA, US
Dhanya Raghu - Los Angeles CA, US

G06T 11/00
G06K 9/62

Embodiments of the present disclosure are directed towards generating images conditioned on a desired attribute. In particular, an attribute-based image generation system can use a directional-GAN architecture to generate images conditioned on a desired attribute. A latent vector and a desired attribute are received. A feature subspace is determined for the latent vector using a latent-attribute linear classifier trained to determine a relationship between the latent vector and the desired attribute. An image is generated using the latent vector such that the image contains the desired attribute. In embodiments, where the feature space differs from a desired feature subspace, a directional vector is applied to the latent vector that shifts the latent vector from the feature subspace to the desired feature subspace. This modified latent vector is then used during generation of the image.

2021014, May 13, 2021

Nov 12, 2019

16/681056

- San Jose CA, US
Deepak Pai - Santa Clara CA, US

Adobe Inc. - San Jose CA

G06Q 10/06
G06Q 30/06

A user segmentation system is described that is configured to generate use segments and summarize user segments. In one example, the user segmentation system is configured to identify which attributes support a key performance indicator. This is used to generate rules that act as user segments of a user population. Further, the user segmentation system is configured to reduce overlap of user segments through summarization.

2018024, Aug 23, 2018

Feb 17, 2017

15/435869

- San Jose CA, US
Kushal Chawla - Delhi, IN
Yash Shrivastava - West Bengal, IN
Dhruv Singal - New Delhi, IN
Atanu Ranjan Sinha - Bangalore, IN
Deepak Pai - Santa Clara CA, US

G06Q 30/02

Forecasting a potential audience size and an unduplicated audience size for a digital campaign includes receiving an audience segment input and a time period input. The audience segment input is converted into multiple atomic target specifications. For each of the multiple atomic target specifications, a potential audience size is determined during the time period input by selecting a time series model based on a frequency of attribute values from the atomic target specification and combining the selected time series model with a frequent item set model. The potential audience size for each of atomic target specifications is aggregated over the time period input into a total potential audience size. The total potential audience size is output. The time series model and the frequent item set model are obtained using data from a historic bid request database.