Mohammadhadi Kiapour

20230076209, Mar 9, 2023

Nov 14, 2022

18/055273

- San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Nandini Ramakrishnan - San Francisco CA, US
Christophe Boudet, JR. - San Francisco CA, US

G06Q 30/02
G06N 20/00
G06F 40/186

A machine is configured to generate in real time personalized online banner images for users based on data pertaining to user behavior in relation to an image of a product. For example, the machine receives a user selection indicating one or more data features associated with the user. The one or more data features include a data feature pertaining to user behavior in relation to an image of a product. The machine generates, using a machine learning algorithm, a data representation of the machine learning algorithm based on the one or more data features including the data feature pertaining to user behavior in relation to the image of the product. The data representation includes one or more data features pertaining to one or more characteristics of online banner images. The machine generates an online banner image for the user based on the data representation.

20210312180, Oct 7, 2021

Jun 15, 2021

17/348010

- San Jose CA, US
Fan Yang - San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Qiaosong Wang - San Jose CA, US
Japjit S. Tulsi - Los Altos Hills CA, US
Robinson Piramuthu - Oakland CA, US

eBay Inc. - San Jose CA

G06K 9/00
H04N 5/232
G06T 11/60
H04N 7/18
G06Q 30/08
G06Q 30/06
G06K 9/66
G06N 20/00
G06K 9/62
G06K 9/78
G06T 7/60

Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.

20210224876, Jul 22, 2021

Apr 2, 2021

17/221367

- San Jose CA, US
Fan Yang - San Jose CA, US
Qiaosong Wang - San Francisco CA, US
Mohammadhadi Kiapour - Roslyn NY, US
Robinson Piramuthu - Oakland CA, US

eBay Inc. - San Jose CA

G06Q 30/06
G06F 16/583
G06F 16/2452
G06F 16/2457
G06F 16/532
G06N 3/04
G06N 7/00
G06N 5/02
G06N 3/02

Systems, methods, and computer program products for identifying a relevant candidate product in an electronic marketplace. Embodiments perform a visual similarity comparison between candidate product image visual content and input query image visual content, process formal and informal natural language user inputs, and coordinate aggregated past user interactions with the marketplace stored in a knowledge graph. Visually similar items and their corresponding product categories, aspects, and aspect values can determine suggested candidate products without discernible delay during a multi-turn user dialog. The user can then refine the search for the most relevant items available for purchase by providing responses to machine-generated prompts that are based on the initial search results from visual, voice, and/or text inputs. An intelligent online personal assistant can thus guide a user to the most relevant candidate product more efficiently than existing search tools.

20210166086, Jun 3, 2021

Nov 6, 2020

17/091851

- San Jose CA, US
Ajinkya Gorakhnath Kale - San Jose CA, US
Qiaosong Wang - San Francisco CA, US
Mohammadhadi Kiapour - Chapel Hill NC, US
Robinson Piramuthu - Oakland CA, US

G06K 9/62
G06F 16/50
G06K 9/46
G06F 16/532
G06F 16/583
G06N 3/04
G06N 3/08
G06N 5/02

Methods, systems, and computer programs are presented for adding new features to a network service. A method includes receiving an image depicting an object of interest. A category set is determined for the object of interest and an image signature is generated for the image. Using the category set and the image signature, the method identifies a set of publications within a publication database and assigns a rank to each publication. The method causes presentation of the ranked list of publications at a computing device from which the image was received.

20210166146, Jun 3, 2021

Feb 2, 2021

17/165481

- San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Ajinkya Gorakhnath Kale - San Jose CA, US
Robinson Piramuthu - Oakland CA, US

G06N 7/00
G06N 3/08
G06K 9/62
G06K 9/46
G06N 3/04
G06K 9/00
G06N 5/02

Various embodiments use a neural network to analyze images for aspects that characterize the images, to present locations of those aspects on the images, and, additionally, to permit a user to interact with those locations on the images. For example, a user may interact with a visual cue over one of those locations to modify, refine, or filter the results of a visual search, performed on a publication corpus, that uses an input image (e.g., one captured using a mobile device) as a search query.

20210158046, May 27, 2021

Feb 8, 2021

17/170549

- San Jose CA, US
Fan Yang - San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Qiaosong Wang - San Jose CA, US
Japjit S. Tulsi - Los Altos Hills CA, US
Robinson Piramuthu - Oakland CA, US

eBay Inc. - San Jose CA

G06K 9/00
H04N 5/232
G06T 11/60
H04N 7/18
G06Q 30/08
G06Q 30/06
G06K 9/66
G06N 20/00
G06K 9/62
G06K 9/78
G06T 7/60

Camera platform techniques are described. In an implementation, a plurality of digital images and data describing times, at which, the plurality of digital images are captured is received by a computing device. Objects of clothing are recognized from the digital images by the computing device using object recognition as part of machine learning. A user schedule is also received by the computing device that describes user appointments and times, at which, the appointments are scheduled. A user profile is generated by the computing device by training a model using machine learning based on the recognized objects of clothing, times at which corresponding digital images are captured, and the user schedule. From the user profile, a recommendation is generated by processing a subsequent user schedule using the model as part of machine learning by the computing device.

20210103957, Apr 8, 2021

Nov 30, 2020

17/107454

- San Jose CA, US
Mohammadhadi Kiapour - San Francisco CA, US
Nandini Ramakrishnan - San Francisco CA, US
Christophe Boudet - San Francisco CA, US

G06Q 30/02
G06N 20/00
G06F 40/186

A machine is configured to generate in real time personalized online banner images for users based on data pertaining to user behavior in relation to an image of a product. For example, the machine receives a user selection indicating one or more data features associated with the user. The one or more data features include a data feature pertaining to user behavior in relation to an image of a product. The machine generates, using a machine learning algorithm, a data representation of the machine learning algorithm based on the one or more data features including the data feature pertaining to user behavior in relation to the image of the product. The data representation includes one or more data features pertaining to one or more characteristics of online banner images. The machine generates an online banner image for the user based on the data representation.

20210063214, Mar 4, 2021

Aug 26, 2019

16/551191

- Palo Alto CA, US
Ning Zhang - Mountain View CA, US
Shuai Zheng - Campbell CA, US
Jordan Hill Hurwitz - Burlingame CA, US
Ziyu Zhang - Mountain View CA, US
Mohammadhadi Kiapour - San Francisco CA, US

G01D 21/02
G06N 3/08

Systems and methods configured to perform monitoring of one or more user activities are described. In one embodiment, a plurality of sensors is configured for contact-free monitoring of at least one bodily function. A signal processing module communicatively coupled to the sensors is configured to receive data from the sensors. A first sensor is configured to generate a first set of quantitative data associated with a user speed and a user position. A second sensor is configured to generate a second set of quantitative data associated with a user action. A third sensor is configured to generate a third set of quantitative data associated with a user movement. The signal processing module is configured to process the three sets of quantitative data using a machine learning module, and identify a user activity and detect a condition associated with the user.