Megan H Adams

20090031291, Jan 29, 2009

Jul 8, 2008

12/217671

Megan Adams - San Francisco CA, US

G06F 9/45

717137

A method and system for translating source code in one logically distinct object oriented language on one platform into another object oriented language on another platform. This includes the translation of the classes and statements in the original source. It also includes the creation of object oriented interfaces for all types and services used in the source code whose definitions are not provided. Once the source code classes are translated, a complete object oriented interface to the target platform is generated. Type inference analysis is performed on the input source code in order to infer types for undeclared services and to infer type hierarchies for undeclared types. Type inference analysis is utilized to generate object oriented interfaces for all types and services used in the source code whose definitions are not available. The method and system also have the capability of generating, on an as needed basis, specific bridge classes which support bridging the platform differences between the target and the source platform.

20210406774, Dec 30, 2021

Sep 14, 2021

17/447642

- Redmond WA, US
Marcos Campos - Carlsbad CA, US
Megan Adams - San Francisco CA, US
Mark Hammond - Berkeley CA, US
Matthew Brown - Berkeley CA, US
Tod Frye - El Cerrito CA, US

Microsoft Technology Licensing, LLC - Redmond WA

G06N 20/00
G06F 16/951
G06F 16/22
G06F 8/38
G06Q 10/00
G06N 3/10
G06F 30/20
G06F 8/30
G06N 3/08
G06N 3/04
H04L 29/06
G06F 9/451
G06F 3/0482
G06N 3/00
G06N 5/04
G06F 9/48
G06F 15/80
G06K 9/62

An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

20180293517, Oct 11, 2018

Jun 14, 2018

16/009153

- Berkeley CA, US
Marcos Campos - Carlsbad CA, US
Megan Adams - San Francisco CA, US
Mark Hammond - Berkeley CA, US
Tod Frye - El Ceritto CA, US

G06N 99/00
H04L 29/06
G06F 9/451
G06N 3/08
G06N 3/04
G06N 3/00
G06Q 10/00
G06F 3/0482
G06F 8/30
G06F 17/50
G06F 17/30
G06F 8/38
G06F 17/24
G06F 3/0354

An AI engine having an architect module to create a number of nodes and how the nodes are connected in a graph of concept nodes that make up a resulting AI model. The architect module also creates a first concept node by wrapping an external entity of code into a software container with an interface configured to exchange information in a protocol of a software language used by the external entity of code. The architect module also creates a second concept node derived from its description in a scripted file coded in a pedagogical programming language, and connects the second concept node into the graph of nodes in the resulting AI model.

20170213126, Jul 27, 2017

Jan 26, 2017

15/417056

- Berkeley CA, US
Keen McEwan Browne - Berkeley CA, US
Megan Adams - San Francisco CA, US

G06N 3/04
G06N 3/08
H04L 29/06

Provided in some embodiments is an artificial intelligence (“AI”) engine configured to work with a pedagogical programming language configured to enable an author to 1) define a mental model to be learned by an AI model, the mental model including an input, one or more concept nodes, one or more stream nodes, and an output, as well as 2) define one or more curriculums for training the AI model respectively on the one or more concept nodes. A compiler can be configured to generate an assembly code from a source code authored in the pedagogical programming language. An architect module can be configured to propose a neural-network layout from the assembly code. A learner module can be configured to build the AI model the neural-network layout. An instructor module can be configured to train the AI model on the one or more concept nodes respectively with the one or more curriculums.

20170213128, Jul 27, 2017

Jan 26, 2017

15/416970

- Berkeley CA, US
Keen McEwan Browne - Berkeley CA, US
Marcos Campos - Carlsbad CA, US
Matthew James Brown - San Francisco CA, US
Ruofan Kong - Berkeley CA, US
Megan Adams - San Francisco CA, US

G06N 3/04
G06N 3/08
H04L 29/06

Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more remote servers configured to cooperate with one or more databases including one or more AI-engine modules and one or more server-side client-server interfaces. The one or more AI-engine modules include an instructor module and a learner module configured to train an AI model. An assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model. The one or more server-side client-server interfaces can be configured to enable client interactions from a local client such as submitting the source code for training the AI model and using the trained AI model for one or more predictions.

20170213154, Jul 27, 2017

Jan 26, 2017

15/416904

- Berkeley CA, US
Keen McEwan Browne - Berkeley CA, US
Marcos Campos - Carlsbad CA, US
Matthew James Brown - San Francisco CA, US
Ruofan Kong - Berkeley CA, US
Megan Adams - San Francisco CA, US

G06N 99/00
G06N 3/08
G06F 9/44

Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more servers configured to cooperate with one or more databases including one or more AI-engine modules. The one or more AI-engine modules include an architect module configured to propose an AI model from an assembly code. The assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model on the one or more concept modules in one or more training cycles. The AI engine can be configured to instantiate a trained AI model based on the one or more concept modules learned by the AI model in the one or more training cycles.