PATENT DOCUMENT

Publication Number: US-11496600-B2
Application Number: US-201916554518-A
Country: US
Kind Code: B2

Title: Remote execution of machine-learned models

Abstract:
In an exemplary process for remote execution of machine-learned models, one or more signals from a second electronic device is detected by a first electronic device. The second electronic device includes a machine-learned model associated with an application implemented on the first electronic device. Based on the one or more signals, a communication connection is established with the second electronic device and a proxy to the machine-learned model is generated. Input data is obtained via a sensor of the first electronic device. A representation of the input data is sent to the second electronic device via the proxy and the established communication connection. The representation of the input data is processed through the machine-learned model to generate an output. A result derived from the output is received via the communication connection and a representation of the result is outputted.

Claims:
What is claimed is: 
     
       1. A method, performed by a first electronic device having one or more processors and memory storing, the method comprising:
 at the first electronic device:
 detecting one or more wireless signals from a second electronic device positioned within a threshold proximity of the first electronic device, wherein the second electronic device includes a machine-learned model associated with an application implemented on the first electronic device; 
 based on the one or more wireless signals, establishing a communication connection with the second electronic device; 
 upon establishing the communication connection, generating, for the application, a proxy to the machine-learned model, wherein the proxy enables the first electronic device to access the machine-learned model of the second electronic device;
 obtaining input data via a sensor of the first electronic device; 
 
 sending, via the proxy and the communication connection, a representation of the input data to the second electronic device, wherein sending the representation of the input data initiates the second electronic device to generate an output from processing the representation of the input data through the machine-learned model; 
 receiving a result from the second electronic device via the communication connection, wherein the result is derived from the output; and 
 outputting a representation of the result via a user interface of the application. 
 
 
     
     
       2. The method of  claim 1 , wherein the one or more wireless signals includes identification information for the machine-learned model stored on the second electronic device. 
     
     
       3. The method of  claim 2 , wherein the identification information includes an input dimension of the machine-learned model. 
     
     
       4. The method of  claim 1 , further comprising, at the first electronic device:
 based on the one or more wireless signals, determining whether the machine-learned model satisfies one or more predetermined conditions associated with the application; and 
 in accordance with a determination that the machine-learned model satisfies the one or more predetermined conditions, sending, to the second electronic device, a request to establish the communication connection. 
 
     
     
       5. The method of  claim 4 , wherein the one or more predetermined conditions is based on a location of the first electronic device. 
     
     
       6. The method of  claim 1 , further comprising, at the first electronic device:
 prior to establishing the communication connection with the second electronic device, connecting to a network of the second electronic device in accordance with an obtained user permission, wherein the communication connection with the second electronic device is established based on the obtained user permission. 
 
     
     
       7. The method of  claim 1 , wherein the communication connection is a full duplex point-to-point connection between the first electronic device and the second electronic device. 
     
     
       8. The method of  claim 1 , wherein establishing the communication connection causes the second electronic device to load the machine-learned model. 
     
     
       9. The method of  claim 1 , wherein a property of the communication connection is uniquely mapped to the machine-learned model. 
     
     
       10. The method of  claim 1 , wherein the proxy maps one or more resources of the second electronic device to the application. 
     
     
       11. The method of  claim 1 , wherein the proxy is configured to appear, to the application running on the first electronic device, as a local machine-learned model operating on the first electronic device. 
     
     
       12. The method of  claim 1 , wherein the first electronic device does not store a local machine-learned model for the application. 
     
     
       13. The method of  claim 1 , wherein the first electronic device stores a local machine-learned model, and further comprising:
 in accordance with establishing the communication connection with the second electronic device, forgo processing the representation of the input data through the local machine-learned model to generate a local result. 
 
     
     
       14. The method of  claim 1 , further comprising, at the first electronic device:
 generating the representation of the input data by adjusting a format or size of the input data. 
 
     
     
       15. The method of  claim 14 , wherein the representation of the input data is generated such that a dimension of the representation of the input data corresponds to an input dimension of the machine-learned model. 
     
     
       16. The method of  claim 1 , wherein the result is generated by mapping the output from the machine-learned model to the result based on one or more predetermined relationships. 
     
     
       17. The method of  claim 16 , further comprising, at the first electronic device:
 sending, with the representation of the input data via the communication connection, processing instructions for the representation of the input data, wherein the result is generated from the output based on the processing instructions. 
 
     
     
       18. The method of  claim 1 , wherein the first electronic device implements a first version of the application and the second electronic device implements a second version of the application, wherein an integrated development environment compiles the application into the first version in accordance with a setting of the integrated development environment being set at a first state, and wherein the integrated development environment compiles the application into the second version in accordance with the setting being set at a second state. 
     
     
       19. A non-transitory computer-readable storage medium storing one or more programs configured to be executed by one or more processors of a first electronic device having a sensor, the one or more programs including instructions for:
 detecting one or more wireless signals from a second electronic device positioned within a threshold proximity of the first electronic device, wherein the second electronic device includes a machine-learned model associated with an application implemented on the first electronic device; 
 based on the one or more wireless signals, establishing a communication connection with the second electronic device; 
 upon establishing the communication connection, generating, for the application, a proxy to the machine-learned model, wherein the proxy enables the first electronic device to access the machine-learned model of the second electronic device; 
 obtaining input data via the sensor; 
 sending, via the proxy and the communication connection, a representation of the input data to the second electronic device, wherein sending the representation of the input data initiates the second electronic device to generate an output from processing the representation of the input data through the machine-learned model; 
 receiving a result from the second electronic device via the communication connection, wherein the result is derived from the output; and 
 outputting a representation of the result via a user interface of the application. 
 
     
     
       20. The non-transitory computer-readable storage medium of  claim 19 , wherein the one or more wireless signals includes identification information for the machine-learned model stored on the second electronic device. 
     
     
       21. The non-transitory computer-readable storage medium of  claim 20 , wherein the identification information includes an input dimension of the machine-learned model. 
     
     
       22. The non-transitory computer-readable storage medium of  claim 19 , wherein the one or more programs include further instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 based on the one or more wireless signals, determine whether the machine-learned model satisfies one or more predetermined conditions associated with the application; and 
 in accordance with a determination that the machine-learned model satisfies the one or more predetermined conditions, send, to the second electronic device, a request to establish the communication connection. 
 
     
     
       23. The non-transitory computer-readable storage medium of  claim 22 , wherein the one or more predetermined conditions is based on a location of the first electronic device. 
     
     
       24. The non-transitory computer-readable storage medium of  claim 19 , wherein the one or more programs include further instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 prior to establishing the communication connection with the second electronic device, connect to a network of the second electronic device in accordance with an obtained user permission, wherein the communication connection with the second electronic device is established based on the obtained user permission. 
 
     
     
       25. The non-transitory computer-readable storage medium of  claim 19 , wherein the communication connection is a full duplex point-to-point connection between the first electronic device and the second electronic device. 
     
     
       26. The non-transitory computer-readable storage medium of  claim 19 , wherein establishing the communication connection causes the second electronic device to load the machine-learned model. 
     
     
       27. The non-transitory computer-readable storage medium of  claim 19 , wherein a property of the communication connection is uniquely mapped to the machine-learned model. 
     
     
       28. The non-transitory computer-readable storage medium of  claim 19 , wherein the proxy maps one or more resources of the second electronic device to the application. 
     
     
       29. The non-transitory computer-readable storage medium of  claim 19 , wherein the proxy is configured to appear, to the application running on the first electronic device, as a local machine-learned model operating on the first electronic device. 
     
     
       30. The non-transitory computer-readable storage medium of  claim 19 , wherein the first electronic device does not store a local machine-learned model for the application. 
     
     
       31. The non-transitory computer-readable storage medium of  claim 19 , wherein the first electronic device stores a local machine-learned model, and wherein the one or more programs include further instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 in accordance with establishing the communication connection with the second electronic device, forgo processing the representation of the input data through the local machine-learned model to generate a local result. 
 
     
     
       32. The non-transitory computer-readable storage medium of  claim 19 , wherein the one or more programs include further instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 generate the representation of the input data by adjusting a format or size of the input data. 
 
     
     
       33. The non-transitory computer-readable storage medium of  claim 32 , wherein the representation of the input data is generated such that a dimension of the representation of the input data corresponds to an input dimension of the machine-learned model. 
     
     
       34. The non-transitory computer-readable storage medium of  claim 19 , wherein the result is generated by mapping the output from the machine-learned model to the result based on one or more predetermined relationships. 
     
     
       35. The non-transitory computer-readable storage medium of  claim 34 , wherein the one or more programs include further instructions, which when executed by the one or more processors of the first electronic device, cause the first electronic device to:
 send, with the representation of the input data via the communication connection, processing instructions for the representation of the input data, wherein the result is generated from the output based on the processing instructions. 
 
     
     
       36. The non-transitory computer-readable storage medium of  claim 19 , wherein the first electronic device implements a first version of the application and the second electronic device implements a second version of the application, wherein an integrated development environment compiles the application into the first version in accordance with a setting of the integrated development environment being set at a first state, and wherein the integrated development environment compiles the application into the second version in accordance with the setting being set at a second state. 
     
     
       37. The electronic device of  claim 19 , wherein the one or more programs include further instructions for:
 at the electronic device:
 prior to establishing the communication connection with the second electronic device, connecting to a network of the second electronic device in accordance with an obtained user permission, wherein the communication connection with the second electronic device is established based on the obtained user permission. 
 
 
     
     
       38. An electronic device, comprising:
 a sensor; 
 one or more processors; and 
 memory storing one or more programs configured to be executed by the one or more processors, the one or more programs including instructions for:
 detecting one or more wireless signals from a second electronic device positioned within a threshold proximity of the electronic device, wherein the second electronic device includes a machine-learned model associated with an application implemented on the electronic device; 
 based on the one or more wireless signals, establishing a communication connection with the second electronic device; 
 upon establishing the communication connection, generating, for the application, a proxy to the machine-learned model, wherein the proxy enables the first electronic device to access the machine-learned model of the second electronic device; 
 obtaining input data via the sensor; 
 sending, via the proxy and the communication connection, a representation of the input data to the second electronic device, wherein sending the representation of the input data initiates the second electronic device to generate an output from processing the representation of the input data through the machine-learned model; 
 receiving a result from the second electronic device via the communication connection, wherein the result is derived from the output; and 
 outputting a representation of the result via a user interface of the application. 
 
 
     
     
       39. The electronic device of  claim 38 , wherein the one or more wireless signals includes identification information for the machine-learned model stored on the second electronic device. 
     
     
       40. The electronic device of  claim 39 , wherein the identification information includes an input dimension of the machine-learned model. 
     
     
       41. The electronic device of  claim 38 , wherein the one or more programs include further instructions for:
 at the electronic device:
 based on the one or more wireless signals, determining whether the machine-learned model satisfies one or more predetermined conditions associated with the application; and 
 in accordance with a determination that the machine-learned model satisfies the one or more predetermined conditions, sending, to the second electronic device, a request to establish the communication connection. 
 
 
     
     
       42. The electronic device of  claim 41 , wherein the one or more predetermined conditions is based on a location of the electronic device. 
     
     
       43. The electronic device of  claim 38 , wherein the communication connection is a full duplex point-to-point connection between the electronic device and the second electronic device. 
     
     
       44. The electronic device of  claim 38 , wherein establishing the communication connection causes the second electronic device to load the machine-learned model. 
     
     
       45. The electronic device of  claim 38 , wherein a property of the communication connection is uniquely mapped to the machine-learned model. 
     
     
       46. The electronic device of  claim 38 , wherein the proxy maps one or more resources of the second electronic device to the application. 
     
     
       47. The electronic device of  claim 38 , wherein the proxy is configured to appear, to the application running on the electronic device, as a local machine-learned model operating on the electronic device. 
     
     
       48. The method of  claim 38 , wherein the electronic device does not store a local machine-learned model for the application. 
     
     
       49. The electronic device of  claim 38 , wherein the electronic device stores a local machine-learned model, and wherein the one or more programs include further instructions for:
 in accordance with establishing the communication connection with the second electronic device, forgo processing the representation of the input data through the local machine-learned model to generate a local result. 
 
     
     
       50. The electronic device of  claim 38 , wherein the one or more programs include further instructions for:
 at the electronic device:
 generating the representation of the input data by adjusting a format or size of the input data. 
 
 
     
     
       51. The electronic device of  claim 50 , wherein the representation of the input data is generated such that a dimension of the representation of the input data corresponds to an input dimension of the machine-learned model. 
     
     
       52. The electronic device of  claim 38 , wherein the result is generated by mapping the output from the machine-learned model to the result based on one or more predetermined relationships. 
     
     
       53. The electronic device of  claim 52 , wherein the one or more programs include further instructions for:
 at the electronic device:
 sending, with the representation of the input data via the communication connection, processing instructions for the representation of the input data, wherein the result is generated from the output based on the processing instructions. 
 
 
     
     
       54. The electronic device of  claim 38 , wherein the electronic device implements a first version of the application and the second electronic device implements a second version of the application, wherein an integrated development environment compiles the application into the first version in accordance with a setting of the integrated development environment being set at a first state, and wherein the integrated development environment compiles the application into the second version in accordance with the setting being set at a second state. 
     
     
       55. An electronic device, comprising:
 a sensor; 
 means for detecting one or more wireless signals from a second electronic device positioned within a threshold proximity of the electronic device, wherein the second electronic device includes a machine-learned model associated with an application implemented on the electronic device; 
 means for, based on the one or more wireless signals, establishing a communication connection with the second electronic device; 
 means for, upon establishing the communication connection, generating, for the application, a proxy to the machine-learned model, wherein the proxy enables the first electronic device to access the machine-learned model of the second electronic device; 
 means for obtaining input data via the sensor; 
 means for sending, via the proxy and the communication connection, a representation of the input data to the second electronic device, wherein sending the representation of the input data initiates the second electronic device to generate an output from processing the representation of the input data through the machine-learned model; 
 means for receiving a result from the second electronic device via the communication connection, wherein the result is derived from the output; and 
 means for outputting a representation of the result via a user interface of the application.

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Application No. 62/855,154, entitled “REMOTE EXECUTION OF MACHINE-LEARNED MODELS,” filed May 31, 2019, the content of which is hereby incorporated by reference in its entirety for all purposes. 
    
    
     FIELD 
     The present disclosure relates generally to using machine-learned models, and more specifically to techniques for remote execution of machine-learned models. 
     BACKGROUND 
     Machine-learned models are statistical models that have been trained using a predetermined set of training data. Machine-learned models can be configured to perform specific tasks, such as making inferences (e.g., classification, clustering, or prediction) based on the predetermined set of training data. For example, machine-learned models can be used to infer objects represented in a given image or predict a subsequent word given a sequence of words. Examples of machine-learned models include recurrent neural network models, convolution neural network models, and the like. 
     BRIEF SUMMARY 
     The present disclosure relates to remote execution of machine-learned models. In one exemplary process, one or more wireless signals are detected by a first electronic device. The one or more wireless signals originate from a second electronic device positioned within a threshold proximity of the first electronic device. The second electronic device includes a machine-learned model associated with an application implemented on the first electronic device. Based on the one or more wireless signals, a communication connection is established with the second electronic device. Upon establishing the communication connection, a proxy to the machine-learned model is generated for the application. Input data is obtained via a sensor of the first electronic device. A representation of the input data is sent to the second electronic device via the proxy and the established communication connection. Sending the representation of the input data initiates the second electronic device to generate an output from processing the representation of the input data through the machine-learned model. A result from the second electronic device is received via the communication connection. The result is derived from the generated output. A representation of the result is outputted via the user interface of the application. 
     Generating the proxy to the machine-learned model can enable the first electronic device to access and utilize the machine-learned model of the second electronic device more seamlessly and efficiently. For example, the proxy makes the machine-learned model appear to the application as though it is loaded and available for use on the first electronic device, which improve the efficiency at which the representation of the input data is processed through the machine-learned model. This results in an improvement in the functioning of the first electronic device. 
    
    
     
       DESCRIPTION OF THE FIGURES 
       For a better understanding of the various described embodiments, reference should be made to the Description of Embodiments below, in conjunction with the following drawings in which like reference numerals refer to corresponding parts throughout the figures. 
         FIG. 1A  is a block diagram illustrating a portable multifunction device with a touch-sensitive display in accordance with some embodiments. 
         FIG. 1B  is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. 
         FIG. 2  illustrates a portable multifunction device having a touch screen in accordance with some embodiments. 
         FIG. 3  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. 
         FIG. 4A  illustrates an exemplary user interface for a menu of applications on a portable multifunction device in accordance with some embodiments. 
         FIG. 4B  illustrates an exemplary user interface for a multifunction device with a touch-sensitive surface that is separate from the display in accordance with some embodiments. 
         FIG. 5A  illustrates a personal electronic device in accordance with some embodiments. 
         FIG. 5B  is a block diagram illustrating a personal electronic device in accordance with some embodiments. 
         FIG. 6  illustrates a system and environment for performing remote execution of machine-learned models in accordance with some embodiments. 
         FIGS. 7A-7F  illustrate a process for remote execution of machine-learned models in accordance with some embodiments. 
         FIG. 8  is a flow diagram illustrating a process for remote execution of machine-learned models in accordance with some embodiments. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     The following description sets forth exemplary methods, parameters, and the like. It should be recognized, however, that such description is not intended as a limitation on the scope of the present disclosure but is instead provided as a description of exemplary embodiments. 
     Remote execution of machine-learned models can be desirable. For example, an entity or enterprise may develop a proprietary machine-learned model (e.g., with proprietary training data and/or using proprietary training techniques) for its clients or customers to use. The entity or enterprise may, for strategic reasons, choose to maintain control over the model and not distribute copies of the model onto the devices of its clients or customers. For example, maintaining control over the model can enable the entity or enterprise to more effectively introduce updates to the model. In such cases, the entity or enterprise may host its machine-learned model on one or more host devices and allow the user devices of its clients or customers to remotely access the machine-learned model to perform inferencing tasks. In implementing remote execution of machine-learned models, it can be desirable for the remote execution process to be robust and efficient. For example, it can be desirable for the communication connections between the applications running on the user devices and the machine-learned models loaded on the host devices to facilitate quick and reliable data processing. In addition, it can be desirable for the machine-learned models to be compatible with various types of user devices. For example, remote execution of machine-learned models can be implemented such that user devices with limited computing resources can access and utilize the models without overburdening local resources and causing high response latency. It can further be desirable for the remote machine-learned models to be compatible with different versions of applications running on user devices. For example, the machine-learned models on host devices can support both older as well as newer versions of applications. 
     Techniques for remote execution of machine-learned models described herein can address the various considerations described above. In accordance with some embodiments described herein, one or more signals are detected by a first electronic device (e.g., a user device). The one or more signals originate from a second electronic device (e.g., a host device) positioned within a threshold proximity of the first electronic device. The one or more signals contain, for example, information regarding a machine-learned model stored and available for use on the second electronic device. The information can facilitate support for different versions of applications running on user devices. For example, the information can enable user devices to discover host devices advertising models that are compatible with the respective versions of applications running on the user devices. Based on the one or more signals, a communication connection is established between the first electronic device and the second electronic device. The communication connection establishes, for example, a direct virtual data pipeline between the application running on the first electronic device and the machine-learned model on the second electronic device (e.g., via machine learning layers, such as machine learning layers  612  and  614  shown in  FIG. 6  and discussed below). The established communication connection can enable quick and reliable communication between the application and the machine-learned model. Upon establishing the communication connection, a proxy to the machine-learned model is generated for the application. The proxy can serve as an interface to the application in order to make the machine-learned model appear to the application as though it is locally available for the application to use. The proxy can thus facilitate efficient processing of data through the machine-learned model. Input data is obtained via a sensor of the first electronic device. A representation of the input data is sent to the second electronic device via the proxy and the established communication connection. Sending the representation of the input data initiates the second electronic device to generate an output from processing the representation of the input data through the machine-learned model. A result from the second electronic device is received via the communication connection. The result is derived from the generated output. In some examples, the result is generated on the second electronic device to avoid overburdening the local resources on the first electronic device. In some examples, the result is a higher level result that facilitates compatibility of the machine-learned model with various versions of the application on different user devices. A representation of the result is outputted on the first electronic device via a user interface of the application. 
     Below,  FIGS. 1A-1B, 2, 3, 4A-4B, and 5A-5B  provide a description of exemplary devices for performing the techniques for remote execution of machine-learned models.  FIG. 6  illustrates an exemplary system and environment for performing remote execution of machine-learned models.  FIGS. 7A-7F  illustrate an exemplary process for remote execution of machine-learned models.  FIG. 8  is a flow diagram illustrating an exemplary process for remote execution of machine-learned models. 
     Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first touch could be termed a second touch, and, similarly, a second touch could be termed a first touch, without departing from the scope of the various described embodiments. The first touch and the second touch are both touches, but they are not the same touch. 
     The terminology used in the description of the various described embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various described embodiments and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. 
     The term “if” is, optionally, construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” is, optionally, construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context. 
     Embodiments of electronic devices, user interfaces for such devices, and associated processes for using such devices are described. In some embodiments, the device is a portable communications device, such as a mobile telephone, that also contains other functions, such as PDA and/or music player functions. Exemplary embodiments of portable multifunction devices include, without limitation, the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other portable electronic devices, such as laptops or tablet computers with touch-sensitive surfaces (e.g., touch screen displays and/or touchpads), are, optionally, used. It should also be understood that, in some embodiments, the device is not a portable communications device, but is a desktop computer with a touch-sensitive surface (e.g., a touch screen display and/or a touchpad). 
     In the discussion that follows, an electronic device that includes a display and a touch-sensitive surface is described. It should be understood, however, that the electronic device optionally includes one or more other physical user-interface devices, such as a physical keyboard, a mouse, and/or a joystick. 
     The device typically supports a variety of applications, such as one or more of the following: a drawing application, a presentation application, a word processing application, a website creation application, a disk authoring application, a spreadsheet application, a gaming application, a telephone application, a video conferencing application, an e-mail application, an instant messaging application, a workout support application, a photo management application, a digital camera application, a digital video camera application, a web browsing application, a digital music player application, and/or a digital video player application. 
     The various applications that are executed on the device optionally use at least one common physical user-interface device, such as the touch-sensitive surface. One or more functions of the touch-sensitive surface as well as corresponding information displayed on the device are, optionally, adjusted and/or varied from one application to the next and/or within a respective application. In this way, a common physical architecture (such as the touch-sensitive surface) of the device optionally supports the variety of applications with user interfaces that are intuitive and transparent to the user. 
     Attention is now directed toward embodiments of portable devices with touch-sensitive displays.  FIG. 1A  is a block diagram illustrating portable multifunction device  100  with touch-sensitive display system  112  in accordance with some embodiments. Touch-sensitive display  112  is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device  100  includes memory  102  (which optionally includes one or more computer-readable storage mediums), memory controller  122 , one or more processing units (CPUs)  120 , peripherals interface  118 , RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , input/output (I/O) subsystem  106 , other input control devices  116 , and external port  124 . Device  100  optionally includes one or more optical sensors  164 . Device  100  optionally includes one or more contact intensity sensors  165  for detecting intensity of contacts on device  100  (e.g., a touch-sensitive surface such as touch-sensitive display system  112  of device  100 ). Device  100  optionally includes one or more tactile output generators  167  for generating tactile outputs on device  100  (e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system  112  of device  100  or touchpad  355  of device  300 ). These components optionally communicate over one or more communication buses or signal lines  103 . 
     As used in the specification and claims, the term “intensity” of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a substitute (proxy) for the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values that includes at least four distinct values and more typically includes hundreds of distinct values (e.g., at least 256). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical/mechanical control such as a knob or a button). 
     As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user&#39;s sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a user&#39;s hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in physical characteristics of the device or the component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is, optionally, interpreted by the user as a “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user&#39;s movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensory perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user. 
     It should be appreciated that device  100  is only one example of a portable multifunction device, and that device  100  optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown in  FIG. 1A  are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application-specific integrated circuits. 
     Memory  102  optionally includes high-speed random access memory and optionally also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller  122  optionally controls access to memory  102  by other components of device  100 . 
     Peripherals interface  118  can be used to couple input and output peripherals of the device to CPU  120  and memory  102 . The one or more processors  120  run or execute various software programs and/or sets of instructions stored in memory  102  to perform various functions for device  100  and to process data. In some embodiments, peripherals interface  118 , CPU  120 , and memory controller  122  are, optionally, implemented on a single chip, such as chip  104 . In some other embodiments, they are, optionally, implemented on separate chips. 
     RF (radio frequency) circuitry  108  receives and sends RF signals, also called electromagnetic signals. RF circuitry  108  converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry  108  optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry  108  optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry  108  optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e-mail (e.g., Internet message access protocol (IMAP) and/or post office protocol (POP)), instant messaging (e.g., extensible messaging and presence protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document. 
     Audio circuitry  110 , speaker  111 , and microphone  113  provide an audio interface between a user and device  100 . Audio circuitry  110  receives audio data from peripherals interface  118 , converts the audio data to an electrical signal, and transmits the electrical signal to speaker  111 . Speaker  111  converts the electrical signal to human-audible sound waves. Audio circuitry  110  also receives electrical signals converted by microphone  113  from sound waves. Audio circuitry  110  converts the electrical signal to audio data and transmits the audio data to peripherals interface  118  for processing. Audio data is, optionally, retrieved from and/or transmitted to memory  102  and/or RF circuitry  108  by peripherals interface  118 . In some embodiments, audio circuitry  110  also includes a headset jack (e.g.,  212 ,  FIG. 2 ). The headset jack provides an interface between audio circuitry  110  and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone). 
     I/O subsystem  106  couples input/output peripherals on device  100 , such as touch screen  112  and other input control devices  116 , to peripherals interface  118 . I/O subsystem  106  optionally includes display controller  156 , optical sensor controller  158 , depth camera controller  169 , intensity sensor controller  159 , haptic feedback controller  161 , and one or more input controllers  160  for other input or control devices. The one or more input controllers  160  receive/send electrical signals from/to other input control devices  116 . The other input control devices  116  optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s)  160  are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g.,  208 ,  FIG. 2 ) optionally include an up/down button for volume control of speaker  111  and/or microphone  113 . The one or more buttons optionally include a push button (e.g.,  206 ,  FIG. 2 ). 
     A quick press of the push button optionally disengages a lock of touch screen  112  or optionally begins a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g.,  206 ) optionally turns power to device  100  on or off. The functionality of one or more of the buttons are, optionally, user-customizable. Touch screen  112  is used to implement virtual or soft buttons and one or more soft keyboards. 
     Touch-sensitive display  112  provides an input interface and an output interface between the device and a user. Display controller  156  receives and/or sends electrical signals from/to touch screen  112 . Touch screen  112  displays visual output to the user. The visual output optionally includes graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output optionally corresponds to user-interface objects. 
     Touch screen  112  has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen  112  and display controller  156  (along with any associated modules and/or sets of instructions in memory  102 ) detect contact (and any movement or breaking of the contact) on touch screen  112  and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen  112 . In an exemplary embodiment, a point of contact between touch screen  112  and the user corresponds to a finger of the user. 
     Touch screen  112  optionally uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies are used in other embodiments. Touch screen  112  and display controller  156  optionally detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen  112 . In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif. 
     A touch-sensitive display in some embodiments of touch screen  112  is, optionally, analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen  112  displays visual output from device  100 , whereas touch-sensitive touchpads do not provide visual output. 
     A touch-sensitive display in some embodiments of touch screen  112  is described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety. 
     Touch screen  112  optionally has a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user optionally makes contact with touch screen  112  using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user. 
     In some embodiments, in addition to the touch screen, device  100  optionally includes a touchpad for activating or deactivating particular functions. In some embodiments, the touchpad is a touch-sensitive area of the device that, unlike the touch screen, does not display visual output. The touchpad is, optionally, a touch-sensitive surface that is separate from touch screen  112  or an extension of the touch-sensitive surface formed by the touch screen. 
     Device  100  also includes power system  162  for powering the various components. Power system  162  optionally includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices. 
     Device  100  optionally also includes one or more optical sensors  164 .  FIG. 1A  shows an optical sensor coupled to optical sensor controller  158  in I/O subsystem  106 . Optical sensor  164  optionally includes charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor  164  receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module  143  (also called a camera module), optical sensor  164  optionally captures still images or video. In some embodiments, an optical sensor is located on the back of device  100 , opposite touch screen display  112  on the front of the device so that the touch screen display is enabled for use as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user&#39;s image is, optionally, obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor  164  can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor  164  is used along with the touch screen display for both video conferencing and still and/or video image acquisition. 
     Device  100  optionally also includes one or more depth camera sensors  175 .  FIG. 1A  shows a depth camera sensor coupled to depth camera controller  169  in I/O subsystem  106 . Depth camera sensor  175  receives data from the environment to create a three dimensional model of an object (e.g., a face) within a scene from a viewpoint (e.g., a depth camera sensor). In some embodiments, in conjunction with imaging module  143  (also called a camera module), depth camera sensor  175  is optionally used to determine a depth map of different portions of an image captured by the imaging module  143 . In some embodiments, a depth camera sensor is located on the front of device  100  so that the user&#39;s image with depth information is, optionally, obtained for video conferencing while the user views the other video conference participants on the touch screen display and to capture selfies with depth map data. In some embodiments, the depth camera sensor  175  is located on the back of device, or on the back and the front of the device  100 . In some embodiments, the position of depth camera sensor  175  can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a depth camera sensor  175  is used along with the touch screen display for both video conferencing and still and/or video image acquisition. 
     Device  100  optionally also includes one or more contact intensity sensors  165 .  FIG. 1A  shows a contact intensity sensor coupled to intensity sensor controller  159  in I/O subsystem  106 . Contact intensity sensor  165  optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor  165  receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system  112 ). In some embodiments, at least one contact intensity sensor is located on the back of device  100 , opposite touch screen display  112 , which is located on the front of device  100 . 
     Device  100  optionally also includes one or more proximity sensors  166 .  FIG. 1A  shows proximity sensor  166  coupled to peripherals interface  118 . Alternately, proximity sensor  166  is, optionally, coupled to input controller  160  in I/O subsystem  106 . Proximity sensor  166  optionally performs as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen  112  when the multifunction device is placed near the user&#39;s ear (e.g., when the user is making a phone call). 
     Device  100  optionally also includes one or more tactile output generators  167 .  FIG. 1A  shows a tactile output generator coupled to haptic feedback controller  161  in I/O subsystem  106 . Tactile output generator  167  optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor  165  receives tactile feedback generation instructions from haptic feedback module  133  and generates tactile outputs on device  100  that are capable of being sensed by a user of device  100 . In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system  112 ) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device  100 ) or laterally (e.g., back and forth in the same plane as a surface of device  100 ). In some embodiments, at least one tactile output generator sensor is located on the back of device  100 , opposite touch screen display  112 , which is located on the front of device  100 . 
     Device  100  optionally also includes one or more accelerometers  168 .  FIG. 1A  shows accelerometer  168  coupled to peripherals interface  118 . Alternately, accelerometer  168  is, optionally, coupled to an input controller  160  in I/O subsystem  106 . Accelerometer  168  optionally performs as described in U.S. Patent Publication No. 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices,” and U.S. Patent Publication No. 20060017692, “Methods And Apparatuses For Operating A Portable Device Based On An Accelerometer,” both of which are incorporated by reference herein in their entirety. In some embodiments, information is displayed on the touch screen display in a portrait view or a landscape view based on an analysis of data received from the one or more accelerometers. Device  100  optionally includes, in addition to accelerometer(s)  168 , a magnetometer and a GPS (or GLONASS or other global navigation system) receiver for obtaining information concerning the location and orientation (e.g., portrait or landscape) of device  100 . 
     In some embodiments, the software components stored in memory  102  include operating system  126 , communication module (or set of instructions)  128 , contact/motion module (or set of instructions)  130 , graphics module (or set of instructions)  132 , text input module (or set of instructions)  134 , Global Positioning System (GPS) module (or set of instructions)  135 , and applications (or sets of instructions)  136 . Furthermore, in some embodiments, memory  102  ( FIG. 1A ) or  370  ( FIG. 3 ) stores device/global internal state  157 , as shown in  FIGS. 1A and 3 . Device/global internal state  157  includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display  112 ; sensor state, including information obtained from the device&#39;s various sensors and input control devices  116 ; and location information concerning the device&#39;s location and/or attitude. 
     Operating system  126  (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components. 
     Communication module  128  facilitates communication with other devices over one or more external ports  124  and also includes various software components for handling data received by RF circuitry  108  and/or external port  124 . External port  124  (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices. 
     Contact/motion module  130  optionally detects contact with touch screen  112  (in conjunction with display controller  156 ) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module  130  includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module  130  receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module  130  and display controller  156  detect contact on a touchpad. 
     In some embodiments, contact/motion module  130  uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has “clicked” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device  100 ). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter). 
     Contact/motion module  130  optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event. 
     Graphics module  132  includes various known software components for rendering and displaying graphics on touch screen  112  or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. As used herein, the term “graphics” includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user-interface objects including soft keys), digital images, videos, animations, and the like. 
     In some embodiments, graphics module  132  stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module  132  receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller  156 . 
     Haptic feedback module  133  includes various software components for generating instructions used by tactile output generator(s)  167  to produce tactile outputs at one or more locations on device  100  in response to user interactions with device  100 . 
     Text input module  134 , which is, optionally, a component of graphics module  132 , provides soft keyboards for entering text in various applications (e.g., contacts  137 , e-mail  140 , IM  141 , browser  147 , and any other application that needs text input). 
     GPS module  135  determines the location of the device and provides this information for use in various applications (e.g., to telephone  138  for use in location-based dialing; to camera  143  as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets). 
     Applications  136  optionally include the following modules (or sets of instructions), or a subset or superset thereof:
         Contacts module  137  (sometimes called an address book or contact list);   Telephone module  138 ;   Video conference module  139 ;   E-mail client module  140 ;   Instant messaging (IM) module  141 ;   Workout support module  142 ;   Camera module  143  for still and/or video images;   Image management module  144 ;   Video player module;   Music player module;   Browser module  147 ;   Calendar module  148 ;   Widget modules  149 , which optionally include one or more of: weather widget  149 - 1 , stocks widget  149 - 2 , calculator widget  149 - 3 , alarm clock widget  149 - 4 , dictionary widget  149 - 5 , and other widgets obtained by the user, as well as user-created widgets  149 - 6 ;   Widget creator module  150  for making user-created widgets  149 - 6 ;   Search module  151 ;   Video and music player module  152 , which merges video player module and music player module;   Notes module  153 ;   Map module  154 ; and/or   Online video module  155 .       

     Examples of other applications  136  that are, optionally, stored in memory  102  include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , contacts module  137  are, optionally, used to manage an address book or contact list (e.g., stored in application internal state  192  of contacts module  137  in memory  102  or memory  370 ), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone  138 , video conference module  139 , e-mail  140 , or IM  141 ; and so forth. 
     In conjunction with RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , telephone module  138  are optionally, used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module  137 , modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies. 
     In conjunction with RF circuitry  108 , audio circuitry  110 , speaker  111 , microphone  113 , touch screen  112 , display controller  156 , optical sensor  164 , optical sensor controller  158 , contact/motion module  130 , graphics module  132 , text input module  134 , contacts module  137 , and telephone module  138 , video conference module  139  includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , e-mail client module  140  includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module  144 , e-mail client module  140  makes it very easy to create and send e-mails with still or video images taken with camera module  143 . 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , the instant messaging module  141  includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages optionally include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , GPS module  135 , map module  154 , and music player module, workout support module  142  includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data. 
     In conjunction with touch screen  112 , display controller  156 , optical sensor(s)  164 , optical sensor controller  158 , contact/motion module  130 , graphics module  132 , and image management module  144 , camera module  143  includes executable instructions to capture still images or video (including a video stream) and store them into memory  102 , modify characteristics of a still image or video, or delete a still image or video from memory  102 . 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and camera module  143 , image management module  144  includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , browser module  147  includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , e-mail client module  140 , and browser module  147 , calendar module  148  includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and browser module  147 , widget modules  149  are mini-applications that are, optionally, downloaded and used by a user (e.g., weather widget  149 - 1 , stocks widget  149 - 2 , calculator widget  149 - 3 , alarm clock widget  149 - 4 , and dictionary widget  149 - 5 ) or created by the user (e.g., user-created widget  149 - 6 ). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets). 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , and browser module  147 , the widget creator module  150  are, optionally, used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget). 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , search module  151  includes executable instructions to search for text, music, sound, image, video, and/or other files in memory  102  that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , and browser module  147 , video and music player module  152  includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen  112  or on an external, connected display via external port  124 ). In some embodiments, device  100  optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.). 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , and text input module  134 , notes module  153  includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions. 
     In conjunction with RF circuitry  108 , touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , text input module  134 , GPS module  135 , and browser module  147 , map module  154  are, optionally, used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions. 
     In conjunction with touch screen  112 , display controller  156 , contact/motion module  130 , graphics module  132 , audio circuitry  110 , speaker  111 , RF circuitry  108 , text input module  134 , e-mail client module  140 , and browser module  147 , online video module  155  includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port  124 ), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module  141 , rather than e-mail client module  140 , is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety. 
     Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules are, optionally, combined or otherwise rearranged in various embodiments. For example, video player module is, optionally, combined with music player module into a single module (e.g., video and music player module  152 ,  FIG. 1A ). In some embodiments, memory  102  optionally stores a subset of the modules and data structures identified above. Furthermore, memory  102  optionally stores additional modules and data structures not described above. 
     In some embodiments, device  100  is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device  100 , the number of physical input control devices (such as push buttons, dials, and the like) on device  100  is, optionally, reduced. 
     The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device  100  to a main, home, or root menu from any user interface that is displayed on device  100 . In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad. 
       FIG. 1B  is a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory  102  ( FIG. 1A ) or  370  ( FIG. 3 ) includes event sorter  170  (e.g., in operating system  126 ) and a respective application  136 - 1  (e.g., any of the aforementioned applications  137 - 151 ,  155 ,  380 - 390 ). 
     Event sorter  170  receives event information and determines the application  136 - 1  and application view  191  of application  136 - 1  to which to deliver the event information. Event sorter  170  includes event monitor  171  and event dispatcher module  174 . In some embodiments, application  136 - 1  includes application internal state  192 , which indicates the current application view(s) displayed on touch-sensitive display  112  when the application is active or executing. In some embodiments, device/global internal state  157  is used by event sorter  170  to determine which application(s) is (are) currently active, and application internal state  192  is used by event sorter  170  to determine application views  191  to which to deliver event information. 
     In some embodiments, application internal state  192  includes additional information, such as one or more of: resume information to be used when application  136 - 1  resumes execution, user interface state information that indicates information being displayed or that is ready for display by application  136 - 1 , a state queue for enabling the user to go back to a prior state or view of application  136 - 1 , and a redo/undo queue of previous actions taken by the user. 
     Event monitor  171  receives event information from peripherals interface  118 . Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display  112 , as part of a multi-touch gesture). Peripherals interface  118  transmits information it receives from I/O subsystem  106  or a sensor, such as proximity sensor  166 , accelerometer(s)  168 , and/or microphone  113  (through audio circuitry  110 ). Information that peripherals interface  118  receives from I/O subsystem  106  includes information from touch-sensitive display  112  or a touch-sensitive surface. 
     In some embodiments, event monitor  171  sends requests to the peripherals interface  118  at predetermined intervals. In response, peripherals interface  118  transmits event information. In other embodiments, peripherals interface  118  transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration). 
     In some embodiments, event sorter  170  also includes a hit view determination module  172  and/or an active event recognizer determination module  173 . 
     Hit view determination module  172  provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display  112  displays more than one view. Views are made up of controls and other elements that a user can see on the display. 
     Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected optionally correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected is, optionally, called the hit view, and the set of events that are recognized as proper inputs are, optionally, determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture. 
     Hit view determination module  172  receives information related to sub-events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module  172  identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module  172 , the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view. 
     Active event recognizer determination module  173  determines which view or views within a view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module  173  determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module  173  determines that all views that include the physical location of a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views. 
     Event dispatcher module  174  dispatches the event information to an event recognizer (e.g., event recognizer  180 ). In embodiments including active event recognizer determination module  173 , event dispatcher module  174  delivers the event information to an event recognizer determined by active event recognizer determination module  173 . In some embodiments, event dispatcher module  174  stores in an event queue the event information, which is retrieved by a respective event receiver  182 . 
     In some embodiments, operating system  126  includes event sorter  170 . Alternatively, application  136 - 1  includes event sorter  170 . In yet other embodiments, event sorter  170  is a stand-alone module, or a part of another module stored in memory  102 , such as contact/motion module  130 . 
     In some embodiments, application  136 - 1  includes a plurality of event handlers  190  and one or more application views  191 , each of which includes instructions for handling touch events that occur within a respective view of the application&#39;s user interface. Each application view  191  of the application  136 - 1  includes one or more event recognizers  180 . Typically, a respective application view  191  includes a plurality of event recognizers  180 . In other embodiments, one or more of event recognizers  180  are part of a separate module, such as a user interface kit or a higher level object from which application  136 - 1  inherits methods and other properties. In some embodiments, a respective event handler  190  includes one or more of: data updater  176 , object updater  177 , GUI updater  178 , and/or event data  179  received from event sorter  170 . Event handler  190  optionally utilizes or calls data updater  176 , object updater  177 , or GUI updater  178  to update the application internal state  192 . Alternatively, one or more of the application views  191  include one or more respective event handlers  190 . Also, in some embodiments, one or more of data updater  176 , object updater  177 , and GUI updater  178  are included in a respective application view  191 . 
     A respective event recognizer  180  receives event information (e.g., event data  179 ) from event sorter  170  and identifies an event from the event information. Event recognizer  180  includes event receiver  182  and event comparator  184 . In some embodiments, event recognizer  180  also includes at least a subset of: metadata  183 , and event delivery instructions  188  (which optionally include sub-event delivery instructions). 
     Event receiver  182  receives event information from event sorter  170 . The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information optionally also includes speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device. 
     Event comparator  184  compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub-event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator  184  includes event definitions  186 . Event definitions  186  contain definitions of events (e.g., predefined sequences of sub-events), for example, event  1  ( 187 - 1 ), event  2  ( 187 - 2 ), and others. In some embodiments, sub-events in an event ( 187 ) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event  1  ( 187 - 1 ) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event  2  ( 187 - 2 ) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display  112 , and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers  190 . 
     In some embodiments, event definition  187  includes a definition of an event for a respective user-interface object. In some embodiments, event comparator  184  performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display  112 , when a touch is detected on touch-sensitive display  112 , event comparator  184  performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler  190 , the event comparator uses the result of the hit test to determine which event handler  190  should be activated. For example, event comparator  184  selects an event handler associated with the sub-event and the object triggering the hit test. 
     In some embodiments, the definition for a respective event ( 187 ) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer&#39;s event type. 
     When a respective event recognizer  180  determines that the series of sub-events do not match any of the events in event definitions  186 , the respective event recognizer  180  enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture. 
     In some embodiments, a respective event recognizer  180  includes metadata  183  with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata  183  includes configurable properties, flags, and/or lists that indicate how event recognizers interact, or are enabled to interact, with one another. In some embodiments, metadata  183  includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy. 
     In some embodiments, a respective event recognizer  180  activates event handler  190  associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer  180  delivers event information associated with the event to event handler  190 . Activating an event handler  190  is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer  180  throws a flag associated with the recognized event, and event handler  190  associated with the flag catches the flag and performs a predefined process. 
     In some embodiments, event delivery instructions  188  include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process. 
     In some embodiments, data updater  176  creates and updates data used in application  136 - 1 . For example, data updater  176  updates the telephone number used in contacts module  137 , or stores a video file used in video player module. In some embodiments, object updater  177  creates and updates objects used in application  136 - 1 . For example, object updater  177  creates a new user-interface object or updates the position of a user-interface object. GUI updater  178  updates the GUI. For example, GUI updater  178  prepares display information and sends it to graphics module  132  for display on a touch-sensitive display. 
     In some embodiments, event handler(s)  190  includes or has access to data updater  176 , object updater  177 , and GUI updater  178 . In some embodiments, data updater  176 , object updater  177 , and GUI updater  178  are included in a single module of a respective application  136 - 1  or application view  191 . In other embodiments, they are included in two or more software modules. 
     It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices  100  with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized. 
       FIG. 2  illustrates a portable multifunction device  100  having a touch screen  112  in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI)  200 . In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers  202  (not drawn to scale in the figure) or one or more styluses  203  (not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device  100 . In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap. 
     Device  100  optionally also include one or more physical buttons, such as “home” or menu button  204 . As described previously, menu button  204  is, optionally, used to navigate to any application  136  in a set of applications that are, optionally, executed on device  100 . Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen  112 . 
     In some embodiments, device  100  includes touch screen  112 , menu button  204 , push button  206  for powering the device on/off and locking the device, volume adjustment button(s)  208 , subscriber identity module (SIM) card slot  210 , headset jack  212 , and docking/charging external port  124 . Push button  206  is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device  100  also accepts verbal input for activation or deactivation of some functions through microphone  113 . Device  100  also, optionally, includes one or more contact intensity sensors  165  for detecting intensity of contacts on touch screen  112  and/or one or more tactile output generators  167  for generating tactile outputs for a user of device  100 . 
       FIG. 3  is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device  300  need not be portable. In some embodiments, device  300  is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child&#39;s learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device  300  typically includes one or more processing units (CPUs)  310 , one or more network or other communications interfaces  360 , memory  370 , and one or more communication buses  320  for interconnecting these components. Communication buses  320  optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device  300  includes input/output (I/O) interface  330  comprising display  340 , which is typically a touch screen display. I/O interface  330  also optionally includes a keyboard and/or mouse (or other pointing device)  350  and touchpad  355 , tactile output generator  357  for generating tactile outputs on device  300  (e.g., similar to tactile output generator(s)  167  described above with reference to  FIG. 1A ), sensors  359  (e.g., optical, acceleration, proximity, touch-sensitive, and/or contact intensity sensors similar to contact intensity sensor(s)  165  described above with reference to  FIG. 1A ). Memory  370  includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory  370  optionally includes one or more storage devices remotely located from CPU(s)  310 . In some embodiments, memory  370  stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory  102  of portable multifunction device  100  ( FIG. 1A ), or a subset thereof. Furthermore, memory  370  optionally stores additional programs, modules, and data structures not present in memory  102  of portable multifunction device  100 . For example, memory  370  of device  300  optionally stores drawing module  380 , presentation module  382 , word processing module  384 , website creation module  386 , disk authoring module  388 , and/or spreadsheet module  390 , while memory  102  of portable multifunction device  100  ( FIG. 1A ) optionally does not store these modules. 
     Each of the above-identified elements in  FIG. 3  is, optionally, stored in one or more of the previously mentioned memory devices. Each of the above-identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules are, optionally, combined or otherwise rearranged in various embodiments. In some embodiments, memory  370  optionally stores a subset of the modules and data structures identified above. Furthermore, memory  370  optionally stores additional modules and data structures not described above. 
     Attention is now directed towards embodiments of user interfaces that are, optionally, implemented on, for example, portable multifunction device  100 . 
       FIG. 4A  illustrates an exemplary user interface for a menu of applications on portable multifunction device  100  in accordance with some embodiments. Similar user interfaces are, optionally, implemented on device  300 . In some embodiments, user interface  400  includes the following elements, or a subset or superset thereof:
         Signal strength indicator(s)  402  for wireless communication(s), such as cellular and Wi-Fi signals;   Time  404 ;   Bluetooth indicator  405 ;   Battery status indicator  406 ;   Tray  408  with icons for frequently used applications, such as:
           Icon  416  for telephone module  138 , labeled “Phone,” which optionally includes an indicator  414  of the number of missed calls or voicemail messages;   Icon  418  for e-mail client module  140 , labeled “Mail,” which optionally includes an indicator  410  of the number of unread e-mails;   Icon  420  for browser module  147 , labeled “Browser;” and   Icon  422  for video and music player module  152 , also referred to as iPod (trademark of Apple Inc.) module  152 , labeled “iPod;” and   
           Icons for other applications, such as:
           Icon  424  for IM module  141 , labeled “Messages;”   Icon  426  for calendar module  148 , labeled “Calendar;”   Icon  428  for image management module  144 , labeled “Photos;”   Icon  430  for camera module  143 , labeled “Camera;”   Icon  432  for online video module  155 , labeled “Online Video;”   Icon  434  for stocks widget  149 - 2 , labeled “Stocks;”   Icon  436  for map module  154 , labeled “Maps;”   Icon  438  for weather widget  149 - 1 , labeled “Weather;”   Icon  440  for alarm clock widget  149 - 4 , labeled “Clock;”   Icon  442  for workout support module  142 , labeled “Workout Support;”   Icon  444  for notes module  153 , labeled “Notes;” and   Icon  446  for a settings application or module, labeled “Settings,” which provides access to settings for device  100  and its various applications  136 .   
               

     It should be noted that the icon labels illustrated in  FIG. 4A  are merely exemplary. For example, icon  422  for video and music player module  152  is labeled “Music” or “Music Player.” Other labels are, optionally, used for various application icons. In some embodiments, a label for a respective application icon includes a name of an application corresponding to the respective application icon. In some embodiments, a label for a particular application icon is distinct from a name of an application corresponding to the particular application icon. 
       FIG. 4B  illustrates an exemplary user interface on a device (e.g., device  300 ,  FIG. 3 ) with a touch-sensitive surface  451  (e.g., a tablet or touchpad  355 ,  FIG. 3 ) that is separate from the display  450  (e.g., touch screen display  112 ). Device  300  also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors  359 ) for detecting intensity of contacts on touch-sensitive surface  451  and/or one or more tactile output generators  357  for generating tactile outputs for a user of device  300 . 
     Although some of the examples that follow will be given with reference to inputs on touch screen display  112  (where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in  FIG. 4B . In some embodiments, the touch-sensitive surface (e.g.,  451  in  FIG. 4B ) has a primary axis (e.g.,  452  in  FIG. 4B ) that corresponds to a primary axis (e.g.,  453  in  FIG. 4B ) on the display (e.g.,  450 ). In accordance with these embodiments, the device detects contacts (e.g.,  460  and  462  in  FIG. 4B ) with the touch-sensitive surface  451  at locations that correspond to respective locations on the display (e.g., in  FIG. 4B, 460  corresponds to  468  and  462  corresponds to  470 ). In this way, user inputs (e.g., contacts  460  and  462 , and movements thereof) detected by the device on the touch-sensitive surface (e.g.,  451  in  FIG. 4B ) are used by the device to manipulate the user interface on the display (e.g.,  450  in  FIG. 4B ) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein. 
     Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a contact) followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is, optionally, replaced with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously. 
       FIG. 5A  illustrates exemplary personal electronic device  500 . Device  500  includes body  502 . In some embodiments, device  500  can include some or all of the features described with respect to devices  100  and  300  (e.g.,  FIGS. 1A-4B ). In some embodiments, device  500  has touch-sensitive display screen  504 , hereafter touch screen  504 . Alternatively, or in addition to touch screen  504 , device  500  has a display and a touch-sensitive surface. As with devices  100  and  300 , in some embodiments, touch screen  504  (or the touch-sensitive surface) optionally includes one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen  504  (or the touch-sensitive surface) can provide output data that represents the intensity of touches. The user interface of device  500  can respond to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device  500 . 
     Exemplary techniques for detecting and processing touch intensity are found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, published as WIPO Publication No. WO/2013/169849, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, published as WIPO Publication No. WO/2014/105276, each of which is hereby incorporated by reference in their entirety. 
     In some embodiments, device  500  has one or more input mechanisms  506  and  508 . Input mechanisms  506  and  508 , if included, can be physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device  500  has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device  500  with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms permit device  500  to be worn by a user. 
       FIG. 5B  depicts exemplary personal electronic device  500 . In some embodiments, device  500  can include some or all of the components described with respect to  FIGS. 1A, 1B , and  3 . Device  500  has bus  512  that operatively couples I/O section  514  with one or more computer processors  516  and memory  518 . I/O section  514  can be connected to display  504 , which can have touch-sensitive component  522  and, optionally, intensity sensor  524  (e.g., contact intensity sensor). In addition, I/O section  514  can be connected with communication unit  530  for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device  500  can include input mechanisms  506  and/or  508 . Input mechanism  506  is, optionally, a rotatable input device, for example. Input mechanism  508  is, optionally, a button, in some examples. 
     Input mechanism  508  is, optionally, a microphone, in some examples. Personal electronic device  500  optionally includes various sensors, such as GPS sensor  532 , accelerometer  534 , directional sensor  540  (e.g., compass), gyroscope  536 , motion sensor  538 , and/or a combination thereof, all of which can be operatively connected to I/O section  514 . 
     Memory  518  of personal electronic device  500  can include one or more non-transitory computer-readable storage mediums, for storing computer-executable instructions, which, when executed by one or more computer processors  516 , for example, can cause the computer processors to perform the techniques described below, including process  800  ( FIG. 8 ). A computer-readable storage medium can be any medium that can tangibly contain or store computer-executable instructions for use by or in connection with the instruction execution system, apparatus, or device. In some examples, the storage medium is a transitory computer-readable storage medium. In some examples, the storage medium is a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and/or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well as persistent solid-state memory such as flash, solid-state drives, and the like. Personal electronic device  500  is not limited to the components and configuration of  FIG. 5B , but can include other or additional components in multiple configurations. 
     As used here, the term “affordance” refers to a user-interactive graphical user interface object that is, optionally, displayed on the display screen of devices  100 ,  300 , and/or  500  ( FIGS. 1A, 3, and 5A-5B ). For example, an image (e.g., icon), a button, and text (e.g., hyperlink) each optionally constitute an affordance. 
     As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “focus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad  355  in  FIG. 3  or touch-sensitive surface  451  in  FIG. 4B ) while the cursor is over a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations that include a touch screen display (e.g., touch-sensitive display system  112  in  FIG. 1A  or touch screen  112  in  FIG. 4A ) that enables direct interaction with user interface elements on the touch screen display, a detected contact on the touch screen acts as a “focus selector” so that when an input (e.g., a press input by the contact) is detected on the touch screen display at a location of a particular user interface element (e.g., a button, window, slider, or other user interface element), the particular user interface element is adjusted in accordance with the detected input. In some implementations, focus is moved from one region of a user interface to another region of the user interface without corresponding movement of a cursor or movement of a contact on a touch screen display (e.g., by using a tab key or arrow keys to move focus from one button to another button); in these implementations, the focus selector moves in accordance with movement of focus between different regions of the user interface. Without regard to the specific form taken by the focus selector, the focus selector is generally the user interface element (or contact on a touch screen display) that is controlled by the user so as to communicate the user&#39;s intended interaction with the user interface (e.g., by indicating, to the device, the element of the user interface with which the user is intending to interact). For example, the location of a focus selector (e.g., a cursor, a contact, or a selection box) over a respective button while a press input is detected on the touch-sensitive surface (e.g., a touchpad or touch screen) will indicate that the user is intending to activate the respective button (as opposed to other user interface elements shown on a display of the device). 
     As used in the specification and claims, the term “characteristic intensity” of a contact refers to a characteristic of the contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is, optionally, based on a predefined number of intensity samples, or a set of intensity samples collected during a predetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10 seconds) relative to a predefined event (e.g., after detecting the contact, prior to detecting liftoff of the contact, before or after detecting a start of movement of the contact, prior to detecting an end of the contact, before or after detecting an increase in intensity of the contact, and/or before or after detecting a decrease in intensity of the contact). A characteristic intensity of a contact is, optionally, based on one or more of: a maximum value of the intensities of the contact, a mean value of the intensities of the contact, an average value of the intensities of the contact, a top 10 percentile value of the intensities of the contact, a value at the half maximum of the intensities of the contact, a value at the 90 percent maximum of the intensities of the contact, or the like. In some embodiments, the duration of the contact is used in determining the characteristic intensity (e.g., when the characteristic intensity is an average of the intensity of the contact over time). In some embodiments, the characteristic intensity is compared to a set of one or more intensity thresholds to determine whether an operation has been performed by a user. For example, the set of one or more intensity thresholds optionally includes a first intensity threshold and a second intensity threshold. In this example, a contact with a characteristic intensity that does not exceed the first threshold results in a first operation, a contact with a characteristic intensity that exceeds the first intensity threshold and does not exceed the second intensity threshold results in a second operation, and a contact with a characteristic intensity that exceeds the second threshold results in a third operation. In some embodiments, a comparison between the characteristic intensity and one or more thresholds is used to determine whether or not to perform one or more operations (e.g., whether to perform a respective operation or forgo performing the respective operation), rather than being used to determine whether to perform a first operation or a second operation. 
     In some embodiments, a portion of a gesture is identified for purposes of determining a characteristic intensity. For example, a touch-sensitive surface optionally receives a continuous swipe contact transitioning from a start location and reaching an end location, at which point the intensity of the contact increases. In this example, the characteristic intensity of the contact at the end location is, optionally, based on only a portion of the continuous swipe contact, and not the entire swipe contact (e.g., only the portion of the swipe contact at the end location). In some embodiments, a smoothing algorithm is, optionally, applied to the intensities of the swipe contact prior to determining the characteristic intensity of the contact. For example, the smoothing algorithm optionally includes one or more of: an unweighted sliding-average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and/or an exponential smoothing algorithm. In some circumstances, these smoothing algorithms eliminate narrow spikes or dips in the intensities of the swipe contact for purposes of determining a characteristic intensity. 
     The intensity of a contact on the touch-sensitive surface is, optionally, characterized relative to one or more intensity thresholds, such as a contact-detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and/or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform operations that are different from operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, when a contact is detected with a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact-detection intensity threshold below which the contact is no longer detected), the device will move a focus selector in accordance with movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally, unless otherwise stated, these intensity thresholds are consistent between different sets of user interface figures. 
     An increase of characteristic intensity of the contact from an intensity below the light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a “light press” input. An increase of characteristic intensity of the contact from an intensity below the deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a “deep press” input. An increase of characteristic intensity of the contact from an intensity below the contact-detection intensity threshold to an intensity between the contact-detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting the contact on the touch-surface. A decrease of characteristic intensity of the contact from an intensity above the contact-detection intensity threshold to an intensity below the contact-detection intensity threshold is sometimes referred to as detecting liftoff of the contact from the touch-surface. In some embodiments, the contact-detection intensity threshold is zero. In some embodiments, the contact-detection intensity threshold is greater than zero. 
     In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a respective press input or in response to detecting the respective press input performed with a respective contact (or a plurality of contacts), where the respective press input is detected based at least in part on detecting an increase in intensity of the contact (or plurality of contacts) above a press-input intensity threshold. In some embodiments, the respective operation is performed in response to detecting the increase in intensity of the respective contact above the press-input intensity threshold (e.g., a “down stroke” of the respective press input). In some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the press-input threshold (e.g., an “up stroke” of the respective press input). 
     In some embodiments, the device employs intensity hysteresis to avoid accidental inputs sometimes termed “jitter,” where the device defines or selects a hysteresis intensity threshold with a predefined relationship to the press-input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press-input intensity threshold or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press-input intensity threshold). Thus, in some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the hysteresis intensity threshold that corresponds to the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the hysteresis intensity threshold (e.g., an “up stroke” of the respective press input). Similarly, in some embodiments, the press input is detected only when the device detects an increase in intensity of the contact from an intensity at or below the hysteresis intensity threshold to an intensity at or above the press-input intensity threshold and, optionally, a subsequent decrease in intensity of the contact to an intensity at or below the hysteresis intensity, and the respective operation is performed in response to detecting the press input (e.g., the increase in intensity of the contact or the decrease in intensity of the contact, depending on the circumstances). 
     For ease of explanation, the descriptions of operations performed in response to a press input associated with a press-input intensity threshold or in response to a gesture including the press input are, optionally, triggered in response to detecting either: an increase in intensity of a contact above the press-input intensity threshold, an increase in intensity of a contact from an intensity below the hysteresis intensity threshold to an intensity above the press-input intensity threshold, a decrease in intensity of the contact below the press-input intensity threshold, and/or a decrease in intensity of the contact below the hysteresis intensity threshold corresponding to the press-input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting a decrease in intensity of a contact below the press-input intensity threshold, the operation is, optionally, performed in response to detecting a decrease in intensity of the contact below a hysteresis intensity threshold corresponding to, and lower than, the press-input intensity threshold. 
     As used herein, an “installed application” refers to a software application that has been downloaded onto an electronic device (e.g., devices  100 ,  300 , and/or  500 ) and is ready to be launched (e.g., become opened) on the device. In some embodiments, a downloaded application becomes an installed application by way of an installation program that extracts program portions from a downloaded package and integrates the extracted portions with the operating system of the computer system. 
     As used herein, the terms “open application,” “loaded application,” or “executing application” refer to a software application with retained state information (e.g., as part of device/global internal state  157  and/or application internal state  192 ). An open or executing application is, optionally, any one of the following types of applications:
         an active application, which is currently displayed on a display screen of the device that the application is being used on;   a background application (or background processes), which is not currently displayed, but one or more processes for the application are being processed by one or more processors; and   a suspended or hibernated application, which is not running, but has state information that is stored in memory (volatile and non-volatile, respectively) and that can be used to resume execution of the application.       

     As used herein, the term “closed application” refers to software applications without retained state information (e.g., state information for closed applications is not stored in a memory of the device). Accordingly, closing an application includes stopping and/or removing application processes for the application and removing state information for the application from the memory of the device. Generally, opening a second application while in a first application does not close the first application. When the second application is displayed and the first application ceases to be displayed, the first application becomes a background application. 
     Attention is now directed towards embodiments for remote execution of machine-learned models.  FIG. 6  illustrates a system and environment for performing remote execution of machine-learned models, in accordance with some embodiments. As shown, system  600  includes first electronic device  602  and second electronic device  604 . First electronic device  602  and second electronic device  604  are configured to communicate with each other (e.g., via network  616 ). In some embodiments, first electronic device  602  is a user device. For example, first electronic device  602  is a portable multifunction device (e.g., device  100 ,  300 , or  500 ), such as a smartphone or a tablet computer. In some embodiments, first electronic device  602  is a wearable smart device, such as a smartwatch or a head-mounted display. In some embodiments, second electronic device  604  is a computing device owned and operated by an entity or enterprise (e.g., a department store or company). For example, second electronic device  604  is one of a plurality of computing device on a network (e.g., network  616 ) owned and operated by the entity or enterprise. Second electronic device  604  is, for example, a portable multifunction device, a desktop computer, or a server computer. 
     In some embodiments, first electronic device  602  and second electronic device  604  are both user devices. For example, first electronic device  604  is a user&#39;s smartwatch or smartphone and second electronic device  604  is the user&#39;s tablet computer, laptop computer, or (second) smartphone. In one example, first electronic device  602  has less computing resources (e.g., processing power and memory) than second electronic device  604 . For example, second electronic device  602  is a smartphone and first electronic device  604  is an accessory device, such as a smartwatch, that is tethered to second electronic device  604 . In another example, first electronic device  602  is a head-mounted display and second electronic device  604  is a base device supporting first electronic device  602 . As will be apparent in the techniques described below, first electronic device  602  can leverage the computing resources of second electronic device  604  to perform remote execution of machine-learned models. 
     In some embodiments, first electronic device  602  functions as a client device and second electronic device  604  functions as a host device. For example, second electronic device  604  is configured to provide services (e.g., machine learning services) to first electronic device  602 . In some embodiments, first electronic device  602  and second electronic device  604  each store and operate an application (e.g., similar to one of applications  136 ). For example, first electronic device  602  includes application  606  and second electronic device  608  includes application  608 . In some embodiments, applications  606  and  608  are different versions of the same application. For example, applications  606  and  608  are respectively a client version and a host version of the same application. In particular, application  606  provides user-facing frontend functions (e.g., user interfaces) that benefit the user of first electronic device  602  whereas application  608  provides backend functions that support one or more features (e.g., machine learning features) of application  606 . In some embodiments, application  608  is a background extension that serves a specific function for application  606 . The background extension is, for example, an on-demand background extension that launches upon receiving a request (e.g., from first electronic device  602 ) for a related service. 
     In some embodiments, application  608  includes one or more features or functions that are not included in application  606 . For example, application  608  includes machine-learned model  610  that is not included in application  606 . In some embodiments, machine-learned model  610  is, for example, a statistical model that is trained and generated by the entity or enterprise. For example, machine-learned model  610  is trained on one or more devices of the entity or enterprise using a set of data obtained by the entity or enterprise. In some embodiments, the machine-learned model  610  is deployed from the one or more devices to second electronic device  604  via network  616  of the entity or enterprise. In some embodiments, machine-learned model  610  is configured to perform inferencing functions, such as prediction or classification. For instance, in one example, when given input representing an image, machine-learned model  610  is configured to determine the likelihood that the image corresponds to any one of a plurality of objects. In another example, when given input representing one or more words, machine-learned model  610  is configured to determine the likelihood that one of a plurality of words follows the one or more words. Although for simplicity, application  608  is depicted to include machine-learned model  610 , it should be appreciate that application  608  can include any number of machine-learned models that support associated applications on any number of user devices. 
     As shown in  FIG. 6 , first electronic device  602  and second electronic device  604  each include a machine learning layer (e.g., machine learning layers  612  and  614 ) that is configured to support machine learning functions for applications  606  and  608 . In some embodiments, machine learning layers  612  and  614  are each a machine learning framework (e.g., Core ML® of Apple Inc.®) that supports machine learning functions for applications  606  and  608 , respectively. For example, machine learning layer  614  enables application  608  to perform inferencing using machine-learned model  610 . In some embodiments, machine learning layers  612  and  614  are the same machine learning framework operating on separate devices. In some embodiments, machine learning layer  614  enable second electronic device  604  to seamlessly offer machine-learned model  610  as a service to application  606  of first electronic device  602 . Similarly, machine learning layer  612  enables first electronic device to access machine-learned model  610  and provide suitable input to machine-learned model  610  for remote execution. As described in greater detail below, machine learning layers  612  and  614  support the generation of a proxy (e.g., proxy  722 , described below) to machine-learned model  610  to enable first electronic device  602  to access machine-learned model as though it is a local model running on first electronic device  602 . 
     In some embodiments, applications  606  and  608  are generated using an integrated development environment, such as Xcode® of Apple Inc.®. In some embodiments, applications  606  and  608  are generated from the same source code of an application. For example, the integrated development environment includes a version setting controlled using a user interface element (e.g., a toggle switch). If a developer sets the version setting to a first state and compiles the application, then the integrated development environment compiles the application into a first version (e.g., application  606 ) of the application. If a developer sets the version setting to a second state and compiles the application, then the integrated development environment compiles the application into a second version (e.g., application  608 ) of the application. In some embodiments, the second version of the application includes a machine-learned model (e.g., machine-learned model  610 ) that is not included in the first version of the application. In some embodiments, first version of the application includes a machine-learned model (e.g., a local machine-learned model that is not included in the second version of the application. The first version of the application is, for example, a client program that is configured to execute on a client device (e.g., first electronic device  602 ). The second version of the application is, for example, a host program that is configured to execute on a host device (e.g., second electronic device  604 ). The second version of the application is configured to provide one or more services (e.g., machine learning services) to the first version of the application and the first version of the application is configured to utilize the one or more services provided by the second version of the application. 
     A non-limiting illustrative example of performing remote execution of machine-learned models is now described with reference to  FIGS. 7A-7F . First electronic device  702  and second electronic device  704  are similar or identical to first electronic device  602  and second electronic device  604 , respectively, described above with reference to  FIG. 6 . In this example, application  706  is an application for a department store (e.g., an application developed and distributed by the department store). In particular, application  706  enables a user of first electronic device  702  to take a picture of a product while browsing in the department store and obtain, based on the picture, additional information (e.g., price, styles, sizes, colors, availability, related products or promotions, etc.) regarding the product. Second electronic device  704  is a device that is owned and operated by the department store. For example, second electronic device  704  is registered with and connected to the store&#39;s network. Machine-learned model  710  on second electronic device  704  is configured to perform image classification for products offered by the department stores. For example, machine-learned model  710  is configured to receive input representing an image (e.g., image of a product) and output likelihood scores that the image corresponds to any of a plurality of products offered by the department store. In some embodiments, second electronic device  704  is located at the department store. For example, second electronic device  704  is positioned at a particular department (e.g., shoe department) of the store and machine-learned model  710  is configured to perform image classification for the products (e.g., various shoes) in that particular department. 
     During operation, as shown in  FIG. 7A , second electronic device  704  broadcasts (e.g., continuously or periodically) one or more signals  716  to advertise one or more machine learning services offered by second electronic device  704 . Signals  716  are broadcasted, for example, to various devices (e.g., first electronic device  702 ) connected to the department store network. In some embodiments, signals  716  are wireless signals broadcasted by second electronic device  704 . For example, second electronic device  704  broadcasts short range wireless signals (e.g., Bluetooth® signals) within a threshold proximity (e.g., 10 m, 20 m, 50 m, or 70 m) of first electronic device  702 . In some embodiments, application  708  of second electronic device  704  is launched upon second electronic device  704  detecting a triggering event. The triggering event is, for example, detecting that a client device (e.g., first electronic device  702 ) is within a threshold proximity (e.g., 10 m, 20 m, 50 m, or 70 m) of second electronic device  704  (e.g., based on beacon technology). In response to launching application  708 , second electronic device  704  begins broadcasting signals  716  to advertise its machine learning services to various devices connected to the store&#39;s network. 
     The machine learning services advertised by signals  716  include the use (e.g., remote execution) of one or more machine-learned models (e.g., machine-learned model  710 ) stored on second electronic device  704  in connection with one or more applications (e.g., application  706 ) of client devices. In some embodiments, signals  716  include information regarding the one or more machine-learned models. For example, signals  716  include identification information (e.g., model name/ID) and revision information (e.g., revision or version number) for each machine-learned model on second electronic device  704 . In some examples, the information includes the input and output capabilities of each machine-learned model. For example, signals  716  include the input dimension (e.g., the dimension of input accepted by the input layer of each model), the output dimension (e.g., the dimension of the output generated by the output layer of each model), and/or the format of the results returned by each machine-learned model. 
     After entering the department store, the user of first electronic device  702  may provide user input to launch (e.g., open) application  706  on first electronic device  702 . In some embodiments, upon launching application  706 , application  706  initiates a discovery mode on first electronic device  702  to discover services advertised by store devices on the store&#39;s network. For example, upon launching application  706 , application  706  determines that first electronic device  702  is in the department store (e.g., based on the location of first electronic device  702  or based on detecting a WiFi signal associated with the department store). In accordance with determining that first electronic device  702  is in the department store, application  706  prompts the user for permission to connect to the store&#39;s network (e.g., via a WiFi access point) and/or to access data from one or more sensors (e.g., optical sensor  164 ) of first electronic device  702 . Upon receiving such user permission, application  706  initiates the discovery mode. 
     While operating in the discovery mode, first electronic device  702  detects signals  716  broadcasted by second electronic device  704 . Based on signals  716 , first electronic device  702  determines whether the machine learning services advertised by signals  716  include the particular service that application  706  is looking for in the discovery mode. For example, application  706  includes one or more predetermined conditions that define a particular machine-learned model that application  706  is looking for in the discovery mode. The one or more predetermined conditions include, for example, information (e.g., model name, model revision, input/output dimensions, etc.) regarding the particular machine-learned model. In some embodiments, the one or more predetermined conditions change depending on the location of the first electronic device. For example, based on the location of the first electronic device (e.g., using GPS or beacon data), application  706  determines that first electronic device  702  has moved from the shoe department to the toy department of the department store. In this example, the one or more predetermined conditions would change from including information specifying a first machine-learned model for the shoe department (e.g., a model trained to identify products found in the shoe department) to including information specifying a second machine-learned model for the toy department (e.g., a model trained to identify products found in the toy department). 
     As described above, in some embodiments, signals  716  include information regarding the one or more machine-learned models available for use on second electronic device  704 . Based on this information in the detected signals  716 , first electronic device  702  determines whether the one or more machine-learned models on second electronic device  704  includes the particular machine-learned model required by application  706 . The determination is performed, for example, by comparing the model information contained in signals  716  with the model information of the model required by application  706 . In the present example, based on the information included in signals  716 , first electronic device  702  determines that second electronic device  704  includes the machine-learned model (e.g., machine-learned model  710 ) that application  706  is looking for in the discovery mode. In accordance with this determination, first electronic device  702  sends request  718  ( FIG. 7B ) to second electronic device  704  (e.g., via the store network) to establish a communication connection (e.g., TCP/IP connection). In some embodiments, request  718  specifies information (e.g., model name/ID, version, etc.) regarding the machine-learned model (e.g., machine-learned model  710 ) that application  706  would like to utilize. Alternatively, if first electronic device  702  determines, based on the information included in signals  716 , that second electronic device  704  does not include the machine-learned model that application  706  is looking for, then electronic device  702  forgoes sending request  718  to second electronic device to establish a communication connection. Instead, first electronic device  702  continues to discover and evaluate signals advertising machine learning services. 
     In some embodiments, first electronic device  702  determines whether or not second electronic device  704  offers the particular machine-learned model that application  706  is looking for based on obtained location information of first electronic device  702 . For example, first electronic device  702  obtains location information (e.g., GPS data, cellular tower triangulation data, beacon data, etc.) from its GPS module (e.g., GPS module  135 ) or RF circuitry (e.g., RF circuitry  108 ). Based on the obtained location information, first electronic device  702  determines that second electronic device  704  is the closest host device offering a machine-learned model that is compatible with application  706  (e.g., having compatible model type, model version, or model input/output capabilities). In accordance with this determination, first electronic device  702  sends request  718  to second electronic device  704  to establish a communication connection for that machine-learned model. 
     In another illustrative example, first electronic device  702  determines, based on the obtained location information, that first electronic device  702  is currently located in the shoe department of the store. First electronic device  702  further determines (e.g., based on information in the detected signals  716 ) that second electronic device  704  has a machine-learned model specific to the shoe department (e.g., a machine-learned model trained using data associated with products only from the shoe department). In accordance with these determinations, first electronic device  702  determines that second electronic device  704  has the particular machine-learned model that application  706  is looking and, in response, sends request  718  to second electronic device  704  to establish a communication connection for that machine-learned model. 
     It should be appreciated that using location information to access the machine-learning services of a host device can be advantageous for remote execution of machine-learned models. In particular, smaller specialized models can be deployed across different host devices rather than a single larger omnibus model deployed across every host device. For example, host devices in different departments of the store can implement smaller models that are specific to the products in the respective departments rather than every host device in the store implementing a larger omnibus model that covers all the products in the store. The smaller specialized models can, for example, require fewer computing resources to execute than the larger omnibus model, which results in quicker execution times and greater capacity to execute multiple service requests from multiple client devices. 
     With reference now to  FIG. 7C , request  718  triggers first electronic device  702  and second electronic device  704  to perform a handshake process to establish communication connection  720 . In some embodiments, the authentication information used to perform the handshake process to establish communication connection  720  is based on the authentication information used to connect to the store&#39;s network (e.g., based on the obtained user permission to connect to the store&#39;s network). In some embodiments, communication connection  720  is a full-duplexed persistent communication connection. In some embodiments, communication connection  720  is a peer-to-peer connection between first electronic device  702  and second electronic device  704   a . In some embodiments, communication connection  720  is a direct point-to-point communication connection. In some embodiments, communication connection  720  is a wireless communication connection (e.g., Bluetooth® connection or Apple Wireless Direct Link). In a specific embodiment, communication connection  720  is a TCP/IP connection. 
     In some embodiments, establishing communication connection  720  causes second electronic device  704  to load machine-learned model  710 . For example, in response to receiving and successfully authenticating request  718  to establish a communication connection, second electronic device  704  loads machine-learned model  710  from its file system into its working memory and prepares (e.g., by reserving processor and memory resources) to receive input data to process through machine-learned model  710 . In some embodiments, a property of communication connection  720  is uniquely mapped to machine-learned model  710 . For example, the specific socket or port assigned to communication connection  720  is uniquely mapped to machine-learned model  710 . It should be appreciated that second electronic device  704  can establish multiple communication connections  720  with multiple client devices, where each communication connection  720  is uniquely assigned to a respective machine-learned model loaded on second electronic device  704 . 
     In some embodiments, first electronic device  702  generates proxy  722  ( FIG. 7D ) for application  706  in response to or in the process of establishing communication connection  720 . Proxy  722  is a proxy to machine-learned model  710  loaded on second electronic device  704 . In some embodiments, proxy  722  is configured to appear, to application  706  and/or machine learning layer  712  running on first electronic device  702 , as a local machine-learned model loaded and operating on first electronic device  702 . For example, proxy  722  is a virtual interface or environment on first electronic device  702  that provides state information to application  706  and/or machine learning layer  712  in order to appear as though machine-learned model  710  is available to application  706  and/or machine learning layer  712  as a local resource on first electronic device  702 . In some embodiments, proxy  722  maps one or more resources (e.g., processor and/or memory) of second electronic device  704  to application  706 , thereby causing the one or more resources to appear locally available for application  706  (and/or machine learning layer  712 ) to use. In some embodiments, the state information provided to electronic device  702  by proxy  722  indicates that proxy  722  is ready to receive input for machine-learned model  710  to process. Proxy  722  is advantageous in enabling application  706  to seamlessly and efficiently utilize machine-learned model  710  of second electronic device  704 . In particular, proxy  722  directly connects application  706  to machine-learned model  710  via machine learning layer  712 , communication connection  720 , and machine learning layer  714 . 
     After establishing communication connection  720  and generating proxy  722 , application  706  receives input data  724  via a sensor (e.g., optical sensor  164 , microphone  113 , or touchscreen  112 ) of first electronic device  702 . For example, using a user interface of application  706 , the user controls a camera (e.g., optical sensor  164 ) of first electronic device  702  to take a picture of a product in the department store. In this example, the received input data  724  is image data representing the product. It should be recognized that, in other examples, other input data (e.g., text input, audio input, speech input, touch input, etc.) from other sensors of first electronic device  702  can be received. 
     Machine learning layer  712  pre-processes input data  724  to generate representation  726  of the input data ( FIG. 7E ). The pre-processing of input data  724  is performed in accordance with information received from second electronic device  704  regarding the input capabilities of machine-learned model  710 . For example, as described above, signals  716  include the input capabilities of machine-learned model  710 . In some embodiments, first electronic device  702  receives information regarding the input capabilities of machine-learned model  710  via the established communication connection  720 . The input capabilities include the input data format that machine-learned model  710  is configured to receive. For example, the input capabilities define the size or dimension of the input that machine-learned model  710  is configured to receive and/or the input format that machine-learned model  710  is configured to process. Machine learning layer  712  processes input data  724  according to the defined input capabilities of machine-learned model  710  such that representation  726  of the input data corresponds to the defined input capabilities. For example, if the input capabilities specify that the input dimensions of machine-learned model  710  is the positive integer n and that the required input format is a de-interleaved image format having a particular lossy compression, then machine learning layer  712  reformats input data  724  such that the format of representation  726  of the input data satisfies these input capabilities. In some embodiments, generating representation  726  of the input data includes one or more of rotating, cropping, scaling, padding, aligning, compressing (lossy or lossless), and de-interleaving input data  724 . In some embodiments, generating representation  726  of the input data includes performing image normalization techniques on input data  724 . In some embodiments, generating representation  726  of the input data includes extracting a feature set from input data  724 . In some embodiments, generating representation  726  of the input includes generating an embedding (e.g., embedded vector) for input data  724 . 
     As shown in  FIG. 7E , representation  726  of the input data generated from input data  724  by machine learning layer  712  is sent to machine-learned model  710  for processing. In particular, machine learning layer  712  provides representation of input data  726  to proxy  722 , which directly transfers representation of input data  726  to the loaded machine-learned model  710  via communication connection  720 . In some embodiments, machine learning layer  714  receives representation  726  of the input data via communication connection  720  and provides representation  726  of the input data to machine-learned model  710  for processing. In some embodiments, prior to providing representation  726  of the input data to machine-learned model  710 , machine learning layer  714  verifies that representation  726  of the input data is in the correct format for machine-learned model  710  to process. 
     Upon receiving representation  726  of the input data, machine-learned model  710  automatically processes representation  726  of the input data to generate an output. In some embodiments, machine-learned model  710  is a trained neural network model having an input layer, an output layer, and one or more hidden layers between the input layer and the output layer. In these embodiments, machine-learned model  710  receives representation  726  of the input data at the input layer and provides the output at the output layer. In the present example, machine-learned model  710  infers whether representative  726  of the input data corresponds to any of a plurality of products sold by the department store. In some embodiments, the output generated by machine-learned model  710  is a probability distribution across the plurality of products. The probability distribution represents, for example, the likelihoods that the image represented by input data  724  corresponds to each of the plurality of products. 
     In some embodiments, application  708  further generates a result from the output of machine-learned model  710 . In some embodiments, the result is generated based on instructions received from application  706  via communication connection  720 . For example, processing instructions are sent to application  708  together with representation  726  of the input data. The processing instructions indicate, for example, how to process the output from machine-learned model  710  to generate the result in a format desired by application  706 . The processing instructions indicate, for example, whether or not to rank the products in the output (e.g., based on the probability distribution), the number of products to return (e.g., top 3 ranked products), how to represent the products in the result (e.g., text name, inventory ID, picture, etc.), what related information to include (e.g., price, options, related deals, etc.), or the like. In some embodiments, generating the result includes mapping the output from machine-learned model  710  to the result based on one or more predetermined relationships. For example, each product is represented in a particular manner in the output layer of machine-learned model  710 . Using a look-up table with predetermined mappings, the representations in the output are converted into a higher-level meaningful result. For example, the representations of the products in the output are converted into corresponding product text names (e.g., “office desk” or “Nike® men&#39;s running shoes”). 
     Generating the result from the output at second electronic device  704  can be desirable for enabling compatibility and improved user experience across various client devices having different computing capabilities and operating different versions of applications. For example, if application  706  is an older or outdated version that does not contain information for new store products, application  706  would be unable to map the output of machine-learned model  710  to a meaningful result. Generating the result on second electronic device  704  thus enable compatibility with such older versions of client applications. Moreover, generating the higher level result on second electronic device  704  can be desirable if first electronic device  702  is a device having limited computing resources (e.g., limited processor and memory capacity). For example, the output data would be generated more quickly on second electronic device  704 , which improves the overall processing time for generating the result. 
     As shown in  FIG. 7F , result  728  generated from the output of machine-learned model  710  is sent from application  708  of second electronic device  704  to application  706  of first electronic device  702  via communication connection  720 . Upon receiving result  728 , application  706  outputs a representation of result  728  on first electronic device  702 . For example, application  706  presents a user interface displaying the name of the product corresponding to the image represented by input data  724 . In addition, information related to the product (e.g., price, sizes, options, styles, availability, etc.) are displayed in the user interface. In some embodiments, the information related to the product are included in results  728  from application  708 . In other embodiments, application  706  retrieves the information based on result  728 . For example, result  728  includes the store product ID of the product and application  706  performs a search using the store product ID to obtain the information related to the product. 
     Although  FIGS. 7A-7F  depict application  706  of first electronic device  702  as not having a machine-learned model, it should be appreciated that, in some embodiments, application  706  of first electronic device  702  includes a local machine-learned model that is different from machine-learned model  710 . The local machine-learned model is, for example, a user-specific machine-learned model that was trained using data associated with the user of first electronic device  702  (e.g., data generated or obtained by first electronic device  702  in connection with input from the user). In some embodiments, application  706  determines whether to utilize the local machine-learned model or a remote machine-learned model (e.g., machine-learned model  710 ). In some embodiments, the determination is based on the version or data associated with the local machine-learned model. For example, if application  706  determines that the local machine-learned model an old or outdated version (e.g., based on a comparison with a reference model date or version), then application  706  initiates request  718  to establish communication connection  720  and proceeds to utilize machine-learned model  710  as described above. If, however, application  706  determines that the local machine-learned model is sufficiently recent or up-to-date (e.g., based on a comparison with a reference model date or version), then application  706  utilizes the local machine-learned model without establishing communication connection  720  to utilize machine-learned model  710 . 
     In some embodiments, the determination of whether to utilize the local machine-learned model or a remote machine-learned model (e.g., machine-learned model  710 ) is based on whether or not first electronic device  702  is able to establish a communication connection with a remote electronic device having a suitable machine-learned model to use. For example, if first electronic device  702  is unable to detect any signals (e.g., signals  716 ) advertising a desired machine-learned model (e.g., having the desired model name, version, input/output capabilities, etc.) or if first electronic device  702  is unable to establish a communication connection with a remote electronic device that has the desired machine-learned model (e.g., due to poor signal, unsuccessful authentication, connection time-out, or other errors), then application  706  utilizes the local machine-learned model without establishing a communication connection to utilize a remote machine-learned model (e.g., machine-learned model  710 ) on the remote electronic device (e.g., second electronic device  704 ). If, however, first electronic device  702  detects signals (e.g., signals  716 ) advertising a desired machine-learned model or if first electronic device  702  successfully establishes a communication connection with a remote electronic device that has the desired machine-learned model, then first electronic device  702  proceeds to utilize the desired machine-learned model on the remote electronic device via the established communication connection (e.g., as described above with reference to machine-learned model  710  of second electronic device  704 ). In some embodiments, first electronic device  702  forgoes utilizing its local machine-learned model if first electronic device  702  successfully establishes a communication connection with a remote electronic device that has the desired machine-learned model. 
     Moreover, it should be appreciated that, in some embodiments, application  706  utilizes machine-learned model  710  in combination with its local machine-learned model. For example, application  706  can process representation of input data  726  through both its local machine-learned model and through machine-learned model  710  (via communication connection  720 ) and obtain two results (e.g., local result and result  728 ). Application  706  can combine the two results and present the combination of the results to the user. 
       FIG. 8  is a flow diagram illustrating process  800  for remote execution of machine-learned models in accordance with some embodiments. Process  800  is performed at a first electronic device, such as first electronic device  602  or  702 , which can be similar or identical to device  100 ,  300 , or  500 ). Some operations in process  800  are, optionally, combined, the orders of some operations are, optionally, changed, and some operations are, optionally, omitted. 
     As described in greater detail below, process  800  involves communication between the first device (e.g., device  602  or  702 ) and a second device (e.g., device  604  or  704 ) and is implemented by a system (e.g., system  600  or  700 ) described above with reference to  FIGS. 6 and 7A-7F . The first device include an application (e.g., application  606  or  706 ). In some embodiments, the first device implements a first version of the application and the second device implements a second version of the application (e.g., application  608  or  708 ). In some embodiments, an integrated development environment (e.g., Xcode® of Apple Inc.®) compiles the application into the first version in accordance with a setting of the integrated development environment being set at a first state. The integrated development environment compiles the application into the second version in accordance with the setting being set at a second state. 
     At block  802 , one or more signals (e.g., signal  716 ) are detected (e.g., using application  706  and RF circuitry  108 ). The one or more signals are, for example, wireless signals. In some embodiments, the one or more signals originate from the second device (e.g., device  604  or  704 ) that is positioned within a threshold proximity of the first device (e.g., device  602  or  702 ). In some embodiments, the second device includes a machine-learned model (e.g., machine-learned model  710 ) associated with the application (e.g., application  706 ) implemented on the first device. For example, the machine-learned model is stored on the second electronic device and is configured to support one or more features of the application implemented on the first device. In some embodiments, the first device does not store a local machine-learned model for the application. 
     In some embodiments, the one or more signals serve to advertise machine learning services offered by the second electronic device. For example, the second electronic device periodically broadcasts the one or more signals to advertise the availability of one or more machine-learned models for use in connection with one or more applications. In some embodiments, the one or more signals include information regarding the one or more machine-learned models available for use on the second device. The information includes, for example, identification information for the machine-learned model stored on the second electronic device. In some embodiments, the identification information includes an input dimension (e.g., the dimension of input that the machine-learned model is configured to accept) of the machine-learned model. 
     At block  804 , based on the one or more signals of block  802 , a communication connection (e.g., communication connection  720 ) is established with the second device (e.g., using application  706 , machine-learning layer  712 , and/or RF circuitry  108 ). In some embodiments, the communication connection is a full duplex point-to-point connection between the first device and the second device. In some embodiments, establishing the communication connection causes the second device to load the machine-learned model. In some embodiments, a property of the communication connection is uniquely mapped to the machine-learned model. 
     In some embodiments, block  804  includes determining, based on the one or more signals, whether the machine-learned model satisfies one or more predetermined conditions associated with the application. In some embodiments, the one or more predetermined conditions is based on a location of the first electronic device. In some embodiments, in accordance with a determination that the machine-learned model satisfies the one or more predetermined conditions, a request (e.g., request  718 ) to establish the communication connection is sent to the second device. Conversely, in accordance with a determination that the machine-learned model does not satisfy the one or more predetermined conditions, process  800  forgoes sending the request to establish the communication connection and continues to discover and evaluate signals from remote devices. 
     In some embodiments, prior to establishing the communication connection with the second device, process  800  includes connecting a network of the second device in accordance with an obtained user permission. In some embodiments, the communication connection with the second device is established based on the obtained user permission. 
     At block  806 , a proxy (e.g., proxy  722 ) to the machine-learned model on the second device is generated (e.g., using application  706  and machine learning layer  712 ) for the application. The proxy is generated, for example, upon establishing the communication connection. In some embodiments, the proxy maps one or more resources (e.g., memory, processor, etc.) of the second device to the application. In some embodiments, the proxy is configured to appear, to the application running on the first device, as a local machine-learned model operating on the first device. 
     At block  808 , input data (e.g., input data  724 ) is obtained (e.g., using application  706 ). The input data is obtained, for example, via a sensor (e.g., microphone  113 , optical sensor  164 , touchscreen  112 , or location sensor, such as GPS module  135  and RF circuitry  108 ) of the first device. In some embodiments, the input data is obtained using a user interface of the application. For example, user input received via the user interface causes the application to obtain the input data via the sensor. In some embodiments, the input data is data that was generated and/or stored on the first device prior to performing block  802 ,  804 , or  806 . In these embodiments, block  808  includes obtaining the input data from the memory of the first device or from a remote device. 
     At block  810 , a representation of the input data (e.g., representation  726  of the input data) is generated (e.g., using application  706  and/or machine learning layer  712 ). The representation of the input data is generated, for example, by adjusting a format or size of the input data (e.g., rotating, cropping, scaling, padding, aligning, compressing, normalizing de-interleaving, etc.). In some embodiments, the representation of the input data is generated such that a dimension of the representation of the input data corresponds to an input dimension of the machine-learned model. 
     At block  812 , the representation of the input data is sent (e.g., using application  706  and/or machine learning layer  712 ) to the second electronic device via the proxy and the established communication connection. In some embodiments, sending the representation of the input data initiates the second device to generate an output from processing the representation of the input data through the machine-learned model (e.g., machine-learned model  710 ). 
     In some embodiments, sending the representation of the input data further causes second electronic device to generate a result (e.g., result  728 ) from the output of the machine-learned model. The result is generated, for example, by mapping the output from the machine-learned model to the result based on one or more predetermined relationships. In some embodiments, block  812  includes sending, with the representation of the input data and via the established communication connection, processing instructions for the representation of the input data. In these embodiments, the result is generated from the output based on the processing instructions. 
     In some embodiments, the first device stores a local machine-learned model. In some embodiments, in accordance with establishing the communication connection with the second device (block  804 ), process  800  forgoes processing the representation of the input data through the local machine-learned model to generate a local result. Instead, the representation of the input data is sent to the second device for the machine-learned model of the second device to process. 
     In some embodiments, block  810  and/or block  812  is performed in response to detecting a triggering event. The triggering event is, for example, based on data (e.g., location information) obtained from one or more sensors (e.g., GPS module  135  or RF circuitry  108 ) of the first device. For example, the input data of block  808  can be obtained prior to establishing the communication connection with the second device at block  804 . In this example, the representation of the input data is generated and stored on the first device pending detection of a communication connection to a suitable host device. In response to detecting that the communication device is established with the second device (block  804 ), the representation of the input data is automatically sent to the second device via the proxy to generate the output. In other examples, the detected triggering event includes detecting, based on location information from a location sensor of the first device, that the first device is at a predetermined location. 
     At block  814 , the result (e.g., result  728 ) is received (e.g., using proxy  722 , machine learning layer  712 , and/or application  706 ) from the second device via the communication connection. 
     At block  816 , a representation of the result is outputted (e.g., using application  706 ). For example, the result is outputted via a user interface of the application (e.g., using touchscreen  112  or speaker  111 ). 
     The operations described above with reference to  FIG. 8  are optionally implemented by components depicted in  FIGS. 1-4B, 6, and 7A-7F . For example, the operations of process  800  may be implemented by Optical sensor  164 , touchscreen  112 , RF circuitry  108 , application  606  or  706 , machine learning layer  612  or  712 , and proxy  722 . It would be clear to a person having ordinary skill in the art how other processes are implemented based on the components depicted in  FIGS. 1-4B, 6, and 7A-7F . 
     In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein. 
     In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein. 
     The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated. 
     Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims. 
     As described above, one aspect of the present technology is the gathering and use of data available from various sources to perform remote execution of machine-learned models. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, user speech data, or image data. 
     The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to access and perform inferencing at a remote machine-learned model. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, location data can be used to determine which remote machine-learned model to access and utilize. 
     The present disclosure contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. Hence different privacy practices should be maintained for different personal data types in each country. 
     Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of remote execution of machine-learned models, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In another example, users can select not to provide certain types of personal data. In yet another example, users can select to limit the length of time certain types of personal data is maintained or entirely prohibit sharing certain types of personal data with remote devices. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app. 
     Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user&#39;s privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data at a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods. 
     Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, remote machine-learned models can be utilized based on non-personal information data or a bare minimum amount of personal information, such as the data that does not specifically identify the user.

Metadata:
Filing Date: 20190828
Publication Date: 20221108
Grant Date: 20221108
Priority Date: 20190531
Inventors: VAISHAMPAYAN, UMESH S.
KAPOOR, GAURAV
WAN, Kit-Man
Assignee: APPLE INC
CPC Classifications: [{"code": "H04W4/021", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/34", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W4/80", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F1/1694", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W4/02", "inventive": false, "first": false, "tree": "[]"}, {"code": "G06F1/163", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W4/14", "inventive": false, "first": false, "tree": "[]"}, {"code": "H04L67/2895", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/59", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04L67/2895", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04883", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04W4/00", "inventive": true, "first": true, "tree": "[]"}, {"code": "G06N3/063", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/59", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0488", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/0346", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F1/1698", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N3/08", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/2895", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/59", "inventive": true, "first": true, "tree": "[]"}, {"code": "H04W4/021", "inventive": true, "first": false, "tree": "[]"}, {"code": "H04L67/34", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06F3/04883", "inventive": true, "first": false, "tree": "[]"}, {"code": "G06N20/00", "inventive": true, "first": false, "tree": "[]"}]
Family ID: 73549764