Daniel R Miranda

US Patent:

58128018, Sep 22, 1998

Filed:

Feb 20, 1996

Appl. No.:

8/603508

Inventors:

William A. Saperstein - San Carlos CA
David B. Townsley - Cupertino CA
Daniel G. Miranda - Campbell CA

Assignee:

Apple Computer, Inc. - Cupertino CA

International Classification:

G06F 1300

US Classification:

395308

Abstract:

An apparatus and method to accomodate a card designed to operate, with an alternate bus standard within a computer designed to operate with a primary bus standard, where the primary bus standard and the alternate bus standard are not directly compatible. The card is designed to fit within the computer in the available physical space and to connect to a card connector as required for the primary bus. However, the card is designed to be electrically compatible with the alternate bus standard. In a preferred embodiment the card draws power and any other signals, perhaps including clock, that are useful to both bus standards, but the address, data and control bus signals for the alternate bus are delivered to the card through an independent channel. This independent channel can be a separate connector to carry any signals needed for the alternate bus standard, which according to the application may or may not repeat signals already available through the primary bus.

US Patent:

20210272253, Sep 2, 2021

Filed:

Feb 27, 2020

Appl. No.:

16/803332

Inventors:

- San Jose CA, US
Vipul Dalal - Cupertino CA, US
Vera Lychagina - San Jose CA, US
Shabnam Ghadar - Menlo Park CA, US
Rohith mohan Dodle - Fremont CA, US
Mina Doroudi - San Francisco CA, US
Midhun Harikumar - Santa Clara CA, US
Kannan Iyer - San Ramon CA, US
Jayant Kumar - San Jose CA, US
Gaurav Kukal - Fremont CA, US
Daniel Miranda - San Jose CA, US
Charles R. McKinney - Aptos CA, US
Archit Kalra - Fremont CA, US

International Classification:

G06T 5/50
G06K 9/00
G06T 7/11
G06K 9/62
G06T 7/70

Abstract:

The present disclosure relates to an image merging system that automatically and seamlessly detects and merges missing people for a set of digital images into a composite group photo. For instance, the image merging system utilizes a number of models and operations to automatically analyze multiple digital images to identify a missing person from a base image, segment the missing person from the second image, and generate a composite group photo by merging the segmented image of the missing person into the base image. In this manner, the image merging system automatically creates merged group photos that appear natural and realistic.

US Patent:

20210224312, Jul 22, 2021

Filed:

Jan 16, 2020

Appl. No.:

16/745143

Inventors:

- San Jose CA, US
Zhe Lin - Fremont CA, US
Pramod Srinivasan - San Francisco CA, US
Jianming Zhang - Campbell CA, US
Daniel David Miranda - San Jose CA, US
Baldo Antonio Faieta - San Francisco CA, US

Assignee:

Adobe Inc. - San Jose CA

International Classification:

G06F 16/532
G06F 3/0484
G06T 7/11
G06T 7/70
G06F 16/538
G06F 16/587

Abstract:

Object search techniques for digital images are described. In the techniques described herein, semantic features are extracted on a per-object basis form a digital image. This supports location of objects within digital images and is not limited to semantic features of an entirety of the digital image as involved in conventional image similarity search techniques. This may be combined with indications a location of the object globally with respect to the digital image through use of a global segmentation mask, use of a local segmentation mask to capture post and characteristics of the object itself, and so on.

US Patent:

20190392082, Dec 26, 2019

Filed:

Jun 25, 2018

Appl. No.:

16/017700

Inventors:

- San Jose CA, US
Daniel Miranda - San Jose CA, US
Prathyusha Senthil Kumar - Morgan Hill CA, US

International Classification:

G06F 17/30
G06Q 30/06
G06N 99/00

Abstract:

A query for one or more resources is received. One or more tokens associated with the query is identified based on running the query through a learning model. The one or more tokens correspond to one or more terms that the query shares context similarity to based on a history of user selections. One or more search result candidates are scored based at least on the context similarity between the one or more tokens and the query.