Apple Inc.

International Business Machines Corporation

Intel Corporation

Alexander Barraclough Brown

Gaurav Kapoor

Michael R Siracusa

Srikrishna Sridhar

Zachary A Nation

Christopher M Hanson

Vrushali Mundhe

Elizabeth Ottens

Debendra Das Sharma

Umesh Suresh Vaishampayan

Prahladachar Jayaprakash Bharadwaj

Junaid Thaliyil

Neil Anthony Campbell

Ryan Jason Cocks

Daniel Ben Pollack

David Michael Chan

Norman Nuo Wang

Keith Stattenfield

James C Wilson

Donald Matthew Firlik

Varaprasad V Lingutla

Kenneth S Orr

Matthew M Patenaude

Murat N Konar

David A McLeod

Jeffrey David Harris

John-Peter E Cafaro

Dheeraj Goswami

Samuel C Page

William Owen Lovett

Patrick R Holley

Nathan C Wertman

Jacob Ty Sawyer

Bruce M Stadnyk

Colter S Reed

Song Li

Gisle Mikal Nitter Dankel

Joseph H Engel

Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.

Techniques for machine language model creation

A computer readable media, a method, and a system registering a third party application providing an available communication system between a local user and a remote user identity, storing information related to the available communication system in a first database, obtaining contact information for the remote user identity from the third party application, determining a communication type for the third party application, pairing the remote user identity with a contact, and updating a graphical representation of contact information.

Integration of third party application as quick actions

Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.

Efficient context monitoring

The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Integration of learning models into a software development system

The subject technology provides for parsing a line of code in a project of an integrated development environment (IDE). The subject technology executes indirectly, using the interpreter, the parsed line of code. The interpreter references a translated source code document generated by a source code translation component from a machine learning (ML) document written in a particular data format. The translated source code document includes code in a chosen programming language specific to the IDE, and the code of the translated source code document is executable by the interpreter. Further the subject technology provides, by the interpreter, an output of the executed parsed line of code.

Integrating machine learning models into an interpreted software development environment

An interactive software development environment in one embodiment can interactively provide outputs of execution or evaluation of software entered into the environment prior to compilation of the software and can automatically add one or more error handling expressions that isolate errors from effecting future software. The environment can automatically add the one or more error handling expressions for each line for software or for a set of software to wrap the set to catch and isolate errors. The execution or evaluation of software in the environment can be, for example, by read-evaluate-print-loop functionality provided by the environment.

Dynamic updates in an interactive programming environment

Methods and systems for developing software can perform both compilation operations and read-evaluate-print-loop (REPL) operations on source code. In one embodiment, an integrated development environment can include a source code editor and a compiler and can perform the following operations: receiving source code and performing REPL operations as the source code is received; receiving edits in the source code for which REPL operations have been performed and compiling, by the compiler, the edited source code; and storing results of execution of the edited source code for use in subsequent REPL operations.

Software development environment with compilation and read-evaluate-print-loop operations

Disclosed are systems, methods, and non-transitory computer-readable storage media for predicting a future context of a computing device. In some implementations, a context daemon can use historical context information to predict future events and/or context changes. For example, the context daemon can analyze historical context information to predict user sleep patterns, user exercise patterns, and/or other user activity. In some implementations, a context client can register a callback for a predicted future context. For example, the context client can request to be notified ten minutes in advance of a predicted event and/or context change. The context daemon can use the prediction to notify a context client in advance of the predicted event.

Context prediction

A packet is identified at a port of a serial data link, and it is determined that the packet is associated with an error. Entry into an error recovery mode is initiated based on the determination that the packet is associated with the error. Entry into the error recovery mode can cause the serial data link to be forced down. In one aspect, forcing the data link down causes all subsequent inbound packets to be dropped and all pending outbound requests and completions to be aborted during the error recovery mode.

Live error recovery

The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.

The subject technology provides for determining that a machine learning model in a first format includes sufficient data to conform to a particular model specification in a second format, the second format corresponding to an object oriented programming language. The subject technology transforms the machine learning model into a transformed machine learning model that is compatible with the particular model specification. The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in an integrated development environment (IDE), the IDE enabling modifying of the generated code interface and the code.

Growing community of inventors

Mountain View, CA, United States of America

Alexander Barraclough Brown