Patent Description:
The described embodiments relate generally to temperature sensing. More particularly, the present embodiments relate to temperature sensing using one or more portable electronic devices.

Electronic devices are increasingly being designed with device portability in mind, for example, to allow users to use these devices in a wide variety of situations and environments. Indeed, power sources, such as lithium batteries, can power an electronic device for a substantial duration of time and in a variety of indoor and outdoor environments. Components within an electronic device, such as, a processor, memory, antennas, and other components, can be sealed within a housing to protect the components from damage or failure induced by an environment external to the housing. Improvements and advances to portable electronic devices can be desirable to provide additional functionality in a variety of situations and environments.

<CIT> discloses techniques for estimating ambient temperature. Here, temperature data is received from a first sensor and a second sensor of a computing device, and the ambient temperature may be estimated based on first temperature data from the first sensor in relation to second temperature data from the second sensor.

According to the invention a portable electronic device as defined in claim <NUM> is provided.

In some examples, the first and second sensors are selected from the set of temperature sensors based on the determined environment. The first and second signals can include temperature measurements of respective regions surrounding the first and second sensors. The first sensors can be positioned adjacent a first internal component, and the second sensor can be positioned adjacent a second internal component. A magnitude of the first weight can be further based on the first signal, or the position of the first sensor within the internal volume. A magnitude of the second weight can be further based on the second signal, or the position of the second sensor within the internal volume. At least one of the first or second sensors can be a thermistor. At least one of the first or second sensors can be affixed to the display assembly. The environment can be one of ambient air at least partially surrounding the portable electronic device, or water at least partially surrounding the portable electronic device. The portable electronic device can be one of a smartwatch, a smartphone, or a tablet computing device.

The processor can be configured to select a first subset of the sensors of the set of temperature sensors. The processor can also apply a respective weight to each of temperature detected by the first subset of sensors to generate weighted temperatures. The processor can also select a first adjustment factor correlating to a predicted environment external to the housing. The processor can also determine a first predicted temperature of the predicted environment based on the first adjustment factor and the weighted temperatures.

In some examples, the processor can also be configured to evaluate the first predicted temperature and, based on the evaluation, select a second subset of sensors of the set of temperature sensors; apply a respective weight to each temperature detected by the second subset of sensors to generate alternate weighted temperatures; and determine a second predicted temperature of the environment based on the first adjustment factor and the alternate weighted temperatures. Alternatively, based on the evaluation, the processor can confirm the predicted temperature based on the first subset of sensors.

In some examples, the processor can also be configured to evaluate the first predicted temperature and, based on the evaluation, select a second adjustment factor correlating to the predicted environment external to the housing, and determine a second predicted temperature of the environment based on the second adjustment factor and the weighted temperatures. Alternatively, based on the evaluation, the processor can confirm the predicted temperature based on the first subset of sensors.

In some examples, the processor can also be configured to evaluate the first predicted temperature and, based on the evaluation, select a second subset of sensors of the set of temperature sensors; apply a respective weight to each temperature detected by the second subset of sensors to generate alternate weighted temperatures; select a second adjustment factor correlating to the predicted environment external to the housing; and determine a second predicted temperature of the environment based on the second adjustment factor and the alternate weighted temperatures. Alternatively, based on the evaluation, the processor can confirm the predicted temperature based on the first subset of sensors.

In some examples, a magnitude of the respective weight is based on at least one of the predicted environment, a temperature detected by each respective sensor of the first subset of sensors, or respective positions within the internal volume of each sensor of the first subset of sensors. The predicted environment can be one of ambient air at least partially surrounding the electronic device or water at least partially surrounding the electronic device. At least one of the respective weights or the first adjustment factor can be at least partially based on calibration data communicated to the processor by an ancillary electronic device. The ancillary electronic device can be a smart thermostat, a smartphone, a smartwatch, or a tablet computing device. The ancillary electronic device can be a first ancillary electronic device, and the processor can be configured to receive calibration data from the first ancillary electronic device and a second ancillary electronic device.

In some examples, each of the first and second temperature sensors can be one of negative temperature coefficient thermistor, a positive temperature coefficient thermistor, a resistance temperature detector, or a thermocouple. The electronic device can also include an electrical power supply. The temperature proximate to the first electrical component can increase while the first electrical component is being supplied electrical power by the electrical power supply. The first electrical component can be a display assembly, a battery, a speaker, or an antenna.

Reference will now be made in detail to representative embodiments illustrated in the accompanying drawings. It should be understood that the following descriptions are not intended to limit the embodiments to one preferred embodiment.

The following invention relates to a portable electronic device as defined in claim <NUM>.

In some examples, determining the temperature of the external environment can be beneficial to a wearer or user of the electronic device. For example, a swimmer can desire to know a water temperature in which the swimmer is exercising because the temperature of the water can significantly impact the number of calories the swimmer burns while exercising. The same is true for hiking, bicycling, and many other physical activities.

In some examples, a data set or a database can be compiled which contains data representative of numerous attributes of the electronic device. For example, the data set can contain a quantity of sensors and the respective position of each sensor within the electronic device. The data set can also include a respective temperature measurement for each of the sensors and an external environment temperature at the time when the respective temperature measurements were taken. Machine learning techniques can be used to correlate or compare the data set to types of environments (arid, humid, aqueous, etc.) and the temperature of those environments to generate models which best or most accurately predict a temperature of the external environment based on the temperatures measured within the electronic device. In other words, adjustment factors and scalers can be applied to temperatures measured by the one or more temperature sensors internal to the electronic device to determine a temperature of an environment external to the electronic device. For example, machine learning can determine which combinations of sensors measurements, weights, and adjustment factors best or most accurately estimate the temperature of the environment external to the electronic device.

These and other embodiments are discussed below with reference to FIGS. 1A - <NUM>. Furthermore, as used herein, a system, a method, an article, a component, a feature, or a sub-feature including at least one of a first option, a second option, or a third option should be understood as referring to a system, a method, an article, a component, a feature, or a sub-feature that can include one of each listed option (e.g., only one of the first option, only one of the second option, or only one of the third option), multiple of a single listed option (e.g., two or more of the first option), two options simultaneously (e.g., one of the first option and one of the second option), or combination thereof (e.g., two of the first option and one of the second option).

<FIG> shows an example of an electronic device <NUM>. The electronic device <NUM> shown in <FIG> is a watch, such as a smartwatch. The smartwatch of <FIG> is merely one representative example of a device that can be used in conjunction with the systems and methods disclosed herein. Electronic device <NUM> can correspond to any form of wearable electronic device, a portable media player, a media storage device, a portable digital assistant ("PDA"), a tablet computer, a computer, a mobile communication device, a GPS unit, a remote control device, a smart watch, a smart phone, or other electronic device. The electronic device <NUM> can be referred to as an electronic device, or a consumer device. In some examples, the electronic device <NUM> can include a housing <NUM> that can carry operational components, for example, in an internal volume at least partially defined by the housing <NUM>. The electronic device <NUM> can also include a strap <NUM>, or other retaining component that can secured the device <NUM> to a body of a user, as desired. Further details of the electronic device are provided below with reference to <FIG>.

<FIG> illustrates a device <NUM>, such as, a smartwatch that can be substantially similar to, and can include some or all of the features of the devices described herein, such as electronic device <NUM>. The device <NUM> can include a housing <NUM>, and a display assembly <NUM> attached to the housing <NUM>. The housing <NUM> can substantially define at least a portion of an exterior surface of the device <NUM>.

The display assembly <NUM> can include a glass, a plastic, or any other substantially transparent exterior layer, material, component, or assembly. The display assembly <NUM> can include multiple layers, with each layer providing a unique function, as described herein. Accordingly, the display assembly <NUM> can be, or can be a part of, an interface component. The display assembly <NUM> can define a front exterior surface of the device <NUM> and, as described herein, this exterior surface can be considered an interface surface. In some examples, the interface surface defined by display assembly <NUM> can receive inputs, such as touch inputs, from a user.

In some examples, the housing <NUM> can be a substantially continuous or unitary component and can define one or more openings to receive components of the electronic device <NUM>. In some examples, the device <NUM> can include input components such as one or more buttons <NUM> and/or a crown <NUM> that can be disposed in the openings. In some examples, a material can be disposed between the buttons <NUM> and/or crown <NUM> and the housing <NUM> to provide an airtight and/or watertight seal at the locations of the openings. The housing <NUM> can also define one or more openings or apertures, such as aperture <NUM> that can allow for sound to pass into or out of the internal volume defined by the housing <NUM>. For example, the aperture <NUM> can be in communication with a microphone component disposed in the internal volume.

<FIG> shows a bottom perspective view of the electronic device <NUM>. The device <NUM> can include a back cover <NUM> that can be attached to the housing <NUM>, for example, opposite the display assembly <NUM>. The back cover <NUM> can include ceramic, plastic, metal, or combinations thereof. In some examples, the back cover <NUM> can include an at least partially electromagnetically transparent component <NUM>. The electromagnetically transparent component <NUM> can be transparent to any desired wavelengths of electromagnetic radiation, such as visible light, infrared light, radio waves, or combinations thereof. In some examples, the electromagnetically transparent component <NUM> can allow sensors and/or emitters disposed in the housing <NUM> to communicate with the external environment. Together, the housing <NUM>, display assembly <NUM> and back cover <NUM> can substantially define an internal volume and an external surface of the device <NUM>.

<FIG> illustrates an exploded view of an electronic device <NUM>, such as, a smartwatch that can be substantially similar to, and can include some or all of the features of the devices described herein, such as electronic devices <NUM> and <NUM>. The electronic device <NUM> can include a housing <NUM>, a display assembly <NUM>, and a back cover <NUM>. Together, the housing <NUM>, display assembly <NUM>, and back cover <NUM> can define an exterior surface and an internal volume of the electronic device <NUM>.

The housing <NUM> can be a substantially continuous or unitary component, and can define one or more openings <NUM>, <NUM>, <NUM> to receive components of the electronic device <NUM> and/or to provide access to an internal portion of the electronic device <NUM>. In some examples, the electronic device <NUM> can include input components such as one or more buttons <NUM> and/or a crown <NUM> that can be disposed in the openings <NUM>, <NUM>. A microphone (not shown) can be disposed in the internal volume in communication with the external or ambient environment through the opening <NUM>.

The display assembly <NUM> can be received by, and can be attached to, the housing <NUM>. The display assembly <NUM> can include a cover <NUM> including a transparent material, such as plastic, glass, and/or ceramic. The display assembly <NUM> can also include a display assembly <NUM> that can include multiple layers and components, each of which can perform one or more desired functions. For example, the display assembly <NUM> can include a layer that can include a touch detection layer or component, a force sensitive layer or component, and one or more display layers or components that can include one or more pixels and/or light emitting portions to display visual content and/or information to a user. In some examples, the display layer or component can include a liquid crystal display (LCD), a light emitting diode (LED) display, an organic light emitting diode (OLED) display, and/or any other form of display. The display layer can also include one or more electrical connectors to provide signals and/or power to the display layer from other components of the electronic device <NUM>.

In some examples, the electronic device <NUM> can include a gasket or seal <NUM> that can be disposed between the display assembly <NUM> and the housing <NUM> to substantially define a barrier to the ingress of liquids or moisture into the internal volume from the external environment at the location of the seal <NUM>. As described herein, the seal <NUM> can include polymer, metal, and/or ceramic materials. The electronic device <NUM> can also include a similar seal (not shown) that can be disposed between the housing <NUM> and the back cover <NUM> to substantially define a barrier to the ingress of liquids or moisture into the internal volume from the external environment at the location of the seal. As described herein, the seal can include polymer, metal, and/or ceramic materials. The seal can be substantially similar to, and can include, some or all of the features of the seal.

The electronic device <NUM> can also include internal components, such as a haptic engine <NUM>, an electrical power supply <NUM> (e.g., a battery), a speaker module <NUM>, and a logic board <NUM>, also referred to as a main logic board <NUM> that can include a system in package (SiP) <NUM> disposed thereon, including one or more integrated circuits, such as processors, sensors, and memory. The SiP <NUM> can also include a package.

In some examples, internal components can be disposed below the main logic board <NUM> and can be disposed at least partially in a portion of the internal volume defined by the back cover <NUM>. In some examples, the electronic device <NUM> can include one or more wireless antennas (not shown) that can be in electrical communication with one or more other components of the electronic device <NUM>. In some examples, the antenna(s) can receive and/or transmit wireless signals at one or more frequencies and can be, for example, one or more of a cellular antenna such as an LTE antenna, a Wi-Fi antenna, a Bluetooth antenna, a GPS antenna, a multifrequency antenna, and the like. The antenna(s) can be communicatively coupled to one or more additional components of the electronic device <NUM>.

The main logic board <NUM> can determine an environment external to the housing <NUM> of the electronic device <NUM>. The environment (i.e., a type of environment) can be determined to be an atmospheric or arid environment, such as, while a user of the electronic device <NUM> is lounging in a chair on a beach. Alternatively, the determined environment can be aqueous, for example, when the user enters a body of water such as an ocean, lake, or pool and the electronic device <NUM> is temporarily submerged under water. The main logic board <NUM> can determine the type of environment by any technology currently available or otherwise developed in the future. For example, the electronic device <NUM> can include one or more components which measure or detect characteristics of the environment based on location information (i.e., GPS data), pressure detection, spectroscopy, moisture detection, or a combination thereof.

In some examples, the electronic device <NUM> can include a speaker assembly <NUM> disposed within the housing <NUM>. The speaker assembly <NUM> can include one or more speakers which convert electrical signals into acoustic waves audible at an environment external to the housing <NUM>. For example, one or more apertures <NUM> can formed within the housing <NUM> which place the speaker assembly in fluid communication with the environment external to the housing <NUM>. The internal components can be disposed within the internal volume defined at least partially by the housing <NUM>, and can be affixed to the housing <NUM> via adhesives, internal surfaces, attachment features, threaded connectors, studs, posts, or other features, that are formed into, defined by, or otherwise part of the housing <NUM> and/or the back cover <NUM>.

The electronic device <NUM> can include additional components such as one or more sensors 340A-E which detect a temperature of a space immediately surrounding the respective sensor. The one or more sensors 340A-E can be a negative temperature coefficient thermistor (NTC), a positive temperature coefficient thermistor (PTC), a resistance temperature detector, a thermocouple, a combination thereof, or any other sensor capable of detecting a temperature of a space immediately surrounding the sensor. In some examples, as shown in <FIG>, the one or more sensors 340A-E can be positioned on or adjacent one or more of the components of the electronic device <NUM>. In other words, each of the one or more sensors 340A-E can be affixed, adhered, fastened, or otherwise coupled to a component of the electronic device <NUM>. As such, the respective temperature detected by each sensor <NUM> can be influenced or otherwise altered by operation of the component to which the sensor <NUM> is affixed. For example, a sensor 340D can be affixed to the logic board <NUM> and detect a relatively higher temperature than a temperature detected by a sensor 340A affixed to the display assembly <NUM> because the logic board <NUM> can generate heat while operating. While the sensors 340A-E illustrated in <FIG> are shown as coupled to components within the internal volume of the housing <NUM> (e.g., display assembly <NUM>, back cover <NUM>, a main logic board <NUM>, etc.), those having skill in the art will readily appreciate that one or more sensors can additionally, or alternatively, be affixed external to the housing (e.g., on an external surface of the housing <NUM>). The sensors 340A-E can be communicatively coupled to the main logic board <NUM> or another component having a processor within the electronic device <NUM>. For example, one or more of the sensors 340A-E can be coupled to the main logic board <NUM> through a wired communication path or a wireless communication path.

While the respective temperatures measured by the sensors 340A-E may not be equivalent to a temperature of the environment external to the housing <NUM>, the main logic board <NUM> can nonetheless rely on one or more of the respective temperature measurements from the sensors 340A-E to determine or approximate the temperature of the environment external to the housing <NUM>. In other words, the main logic board <NUM> can determine or approximate the temperature of the environment external to the housing <NUM> based at least partially on temperature measurements taken from one or more locations within the housing <NUM>. Thus, the respective temperature measurements collected by the sensors 340A-E can be utilized for multiple purposes (e.g., to determine a temperature surrounding the sensor <NUM> and determine a temperature external to the housing <NUM>). Determining a temperature of the environment external to the electronic device <NUM> can be beneficial, for example, when a user is undertaking an exercise and desires to know the temperature of the environment (e.g., swimming, scuba-diving, snorkeling, etc.).

In some examples, the main logic board <NUM> can rely on an equation to determine or approximate the temperature of the environment external to the housing. The equation can include two or more weighted temperature measurements from respective sensors <NUM> and an adjustment factor or offset based on the environment external to the housing <NUM>. In other words, the temperature of the environment external to the housing can be determined or approximated by an equation having n-terms, where n represents two or more weighted temperature measurements from respective sensors <NUM>. Each of the n-terms can correlate to at least one weighted temperature measurement collected by one or more of the sensors <NUM>. In some examples, the Equation <NUM> shown below can be utilized to determine the temperature of the environment external to the housing <NUM>.

The term TE in Equation <NUM> can represent the temperature of the environment external to the housing <NUM>. The term A in Equation <NUM> can represent the adjustment factor or offset correlating to the environment determined by the main logic board <NUM>. The term VALUE:Sn can represent the temperature measurement taken by a particular sensor of the sensors 340A-E. The term B in Equation <NUM> can represent a weight or scaler applied to the temperature measurement taken by the particular sensor (i.e., the sensor represented by the term Sn). The term VALUE:Sm can represent the temperature measurement taken by another particular sensor of the sensors 340A-E. The term C in Equation <NUM> can represent a weight or scaler applied to the temperature measurement taken by the other particular sensor (i.e., the sensor represented by the term Sm). It will be readily understood, that Equation <NUM> can include additional weights or scalers modifying additional temperature measurements taken by additional sensors.

The adjustment factor A can be applied to a summation of the weighted temperature values to predict or estimate a temperature of the external environment. The adjustment factor A can be at least partially based on the particular environment surrounding the electronic device <NUM>. For example, the adjustment factor A can have a respective magnitude or value when the electronic device <NUM> is surrounded by a dry or arid environment and a different magnitude or value when the portable electronic device is surrounded by a humid or aqueous environment (e.g., submerged in water). The magnitude or size of the adjustment factor A can be selected based on heat transfer characteristics or any other properties of the electronic device <NUM> and/or the environment.

In some examples, the adjustment factor A can be correlated or at least partially relate to the environment (or type of environment) external to the electronic device <NUM>. The variance between the magnitudes of adjustment factors A in different environments can be based on the way the environment interacts with the one or more of the components of the electronic device <NUM>. For example, a magnitude of the adjustment factor A can be relatively smaller when the electronic device is submerged under water because the heat within the housing <NUM> can be transferred to water at a higher rate. Conversely, a magnitude of the adjustment factor A can be relatively larger when the electronic device is disposed within an arid environment because less of the heat within the housing <NUM> is transferred to the air surrounding the device. In some examples, the adjustment factor can be negative or otherwise subtracted from the summation of the weighted temperatures to, for example, offset heat generated by electrical components disposed within the electronic device.

The respective weights B and C applied to the measurements or signals of the sensors Sn, Sm can be scalers having values or magnitudes which modify the measurements or signals to enable a best or most accurate determination of the predicted or estimated temperature of the environment surrounding the electronic device <NUM>. In some examples, the weight B can be different from the weight C, for example, the weight B can be larger or greater than the weight C.

In some examples, only a subset of the sensors 340A-E can be relied on to collect the temperature values (e.g., VALUE:Sn and VALUE: Sm) within Equation <NUM>. For example, the respective temperature measurements or signals of the sensor 340A and the sensor 340E can be utilized within Equation <NUM>. In some examples, the temperature measurements or signals of more than two of the sensors, or fewer than two of the sensors, can be utilized to predict or estimate the temperature of the environment external to the electronic device <NUM>. In other words, any number of the sensors 340A-E or subsets of the sensors 340A-E can be utilized to predict or estimate the temperature of the environment external to the electronic device <NUM>. Furthermore, each of the temperature measurements or signals of the two, more, or fewer sensors can be individually weighted to predict or estimate the temperature of the environment external to the electronic device <NUM>.

In some examples, a data set or database can be formed which contains data representative of numerous adjustment factors, weights, sensor positions within the electronic device, and temperature measurements of the sensors. Machine learning techniques can be used to correlate or compare the data set to types of environments (arid, humid, aqueous, etc.) and the temperature of those environments to generate models which best or most accurately predict a temperature of the environment. These machine learning techniques can be incorporated during assembly and/or manufacture of the electronic device, and can be adjusted or tuned for each device. The data set or database can be stored on a local or remote computer, or server, and each generated model can be uploaded from the assembly devices or test structures to the processor and memory of each electronic device. For example, machine learning can determine which combinations of sensors 340A-E measurements (e.g., V ALUE:Sn and VALUE:Sm), weights (e.g., weights B and C), and adjustment factors (e.g., adjustment factor A) best or most accurately estimate the temperature of the environment external to the electronic device <NUM>. Thus, after the type of environment is determined (e.g., arid, humid, aqueous), a best or most accurate model (e.g., combination of sensors values, weights, and an adjustment factor) correlating to that type of environment can be used to project, estimate, or predict a temperature of the particular external environment.

The type of environment can be determined by any method or mechanism now known or developed in the future. For example, one or more infrared sensors, humidity sensors, or other sensors can be communicatively coupled to the main logic board <NUM> to determine the type of environment surrounding the electronic device <NUM>. Two non-limiting examples of types of environment are described below with reference to <FIG>.

<FIG> show a side perspective view of an electronic device <NUM> disposed within a first type of environment and a second type of environment, respectively. The electronic device <NUM> is illustrated as a smartwatch coupled to a wrist <NUM> of the wearer. However, the electronic device <NUM> can be any electronic device including a smartphone, a tablet computer, or another portable electronic device in other examples. The first type of environment can be an arid or a relatively dry environment wherein the electronic device <NUM> is exposed to relatively little moisture (e.g., a beach). The second type of environment can be an aqueous environment wherein the electronic device <NUM> is submerged within a liquid <NUM> (e.g., swimming in the ocean). One or more sensors of the electronic device <NUM> can determine the type of environment surrounding the electronic device <NUM>. While only the first and second environments are described herein, alternative and/or additional types of environments are also within the scope of the present disclosure. For example, a sauna, steam room, shower, or other types of environments can also be determined or identified by the electronic device <NUM>.

A more detailed disclosure relating to the operation and functionality of some examples of electronic devices are provided below with reference to <FIG> and <FIG>.

<FIG> shows an example of a process flow diagram implemented on an electronic device, such as, any one of the electronic devices previously described. The electronic device can be substantially similar to, and can include some or all of the features and/or components of the devices described herein, such as electronic devices <NUM>, <NUM>, <NUM>, <NUM>. For examples, the electronic device can include a housing defining an internal volume, a display assembly, a processor, a haptic engine, an electrical power supply, a set of temperature sensors, and/or any other component of the other electronic devices disclosed herein.

The processor can be disposed within the internal volume and can be communicatively coupled to the set of temperature sensors. The process <NUM> includes the act <NUM> of determining an environment external to the housing. The process <NUM> includes the act <NUM> of determining an adjustment factor correlating to the environment. The process <NUM> includes the act <NUM> of selecting a first sensor and a second sensor from the set of temperature sensors. The process <NUM> includes the act <NUM> of assigning a first weight to a first signal provided by the first sensor to generate a first weighted signal. The process <NUM> includes the act <NUM> of assigning a second weight to a second signal provided by the second sensor to generate a second weighted signal. The process <NUM> includes the act <NUM> of determining a temperature of the environment based on the adjustment factor and the first and second weighted signals.

Accordingly, the process <NUM> can be utilized to generate a predicted or estimated temperature of the environment external to the electronic device. The process <NUM> can include more or fewer acts than the acts <NUM>-<NUM>. For example, the process <NUM> can optionally include acts which relate to selecting a third sensor; assigning a third weight to a third signal provided by the third sensor to generate a third weighted signal; and determining a temperature of the environment based on the adjustment factor and the first, second, and third weighted signals. In other words, some of the acts are optional, and therefore, need not be implemented to generate the predicted or estimated temperature of the environment external to the electronic device.

The process <NUM> includes the act <NUM> of determining an environment external to the housing. Determining the environment external to the housing can include determining the type of environment using one or more infrared sensors, humidity sensors, or other sensors communicatively coupled to the processor (e.g., main logic board) to determine the type of environment surrounding the electronic device <NUM>. For example, the environment or type of environment can be determined to be aqueous, such as, when the electronic device is submerged under water.

The process <NUM> includes the act <NUM> of determining an adjustment factor correlating to the environment. The adjustment factor can be applied to a summation of the weighted temperature values to predict or estimate a temperature of the external environment. The adjustment factor can at least partially correlate to the environment surrounding the electronic device. For example, the adjustment factor can have a respective magnitude or value when the electronic device is surrounded by relatively dry air and a different magnitude or value when the portable electronic device is surrounded by a liquid (e.g., submerged in water). In some examples, the magnitude or size of the adjustment factor can be determined based on heat transfer characteristics or another relationship between the electronic device and the determined environment.

The process <NUM> includes the act <NUM> of selecting a first sensor and a second sensor from the set of temperature sensors. In some examples, the first and second sensors can be selected based on their positions within the housing. As described herein, machine learning techniques can be applied to a data set to determine which combinations of sensors can be relied upon to consistently generate the most accurate estimated temperature of the external environment. For example, signals from a sensor positioned adjacent a non-heat generating electrical component of the electronic device can be utilized to more consistently approximate an accurate external temperature. Alternatively, or additionally, the first and second sensors can be selected based on the value of the temperature measured by the particular sensor of the set of temperature sensors. For example, the machine learning techniques can generate models (i.e., combinations of sensors, weights, and an adjustment factor) which correlates a particular measured temperature at the sensor(s) with a particular temperature of the external environment.

The process <NUM> includes the act <NUM> of assigning a first weight to a first signal provided by the first sensor to generate a first weighted signal. The process <NUM> includes the act <NUM> of assigning a second weight to a second signal provided by the second sensor to generate a second weighted signal. The first and second weights can be scalers which are unique to a particular model being applied to estimate the temperature of the external environment. For example, machine learning techniques can be utilized to generate the first and second weights after analyzing the data set previously described. The first weight can be larger, equivalent, or smaller than the second weight. In some embodiments, the first and second weights can be representative of a confidence level associated with the first and second sensors, respectively. For example, the first weight applied to the first signal can be greater than the second weight applied to the second signal because machine learning techniques indicate the signals of the first sensor (i.e., a temperature sensor in a particular position within the electronic device) have a more consistent correlation with a particular type of environment. A magnitude or size of the first and second weights can be based on at least one of the environment external to the housing, a temperature detected by each of the first and second sensors, or respective positions within the internal volume of each of the first and second sensors.

The process <NUM> includes the act <NUM> of determining a temperature of the environment based on the adjustment factor and the first and second weighted signals. For example, the temperature of the environment can be determined by adding the first and second weighted signals and then subtracting the adjustment factor (or adding a negative adjustment factor) as shown in Equation <NUM>. While the process <NUM> utilizes two sensors (the first and second sensors), those having skill in the art will readily appreciate that the process <NUM> can include less than two sensors or more than two sensors. For example, the process <NUM> can optionally include selecting a third sensor; assigning a third weight to a third signal provided by the third sensor to generate a third weighted signal; and determining a temperature of the environment based on the adjustment factor and the first, second, and third weighted signals.

The processor can be disposed within the internal volume and communicatively coupled to the set of temperature sensors. The process <NUM> includes the act <NUM> of selecting a first subset of sensors of the set of temperature sensors. The process <NUM> includes the act <NUM> of applying a respective weight to each temperature detected by the first subset of sensors to generate weighted temperatures. The process <NUM> includes the act <NUM> of selecting a first adjustment factor correlating to a predicted environment external to the housing. The process <NUM> includes the act <NUM> of determining a first predicted temperature of the predicted environment based on the first adjustment factor and the weighted temperatures.

Accordingly, the process <NUM> can be utilized to generate a predicted or estimated temperature of the environment external to the electronic device. The process <NUM> can include more or fewer acts than the acts <NUM>-<NUM>. For example, the process <NUM> may optionally include an act of evaluating the first predicted temperature. In other words, some of the acts are optional and therefore need not be implemented to generate the predicted or estimated temperature of the environment external to the electronic device.

The process <NUM> includes the act <NUM> of selecting a first subset of sensors of the set of temperature sensors. The first subset of sensors can be at least one sensor from the set of temperature sensors, for example, the subset can be a single sensor, two sensors, three sensors, or more than three sensors. The set of temperature sensors can be disposed within the electronic device and positioned on or adjacent various components of the electronic device, such as, a main logic board or processor, an electrical power supply, a barometric vent, a speaker module, a haptic engine, a display assembly, a wireless communication module, or any other component of the electronic device.

The process <NUM> includes the act <NUM> of applying a respective weight to each temperature detected by the first subset of sensors to generate weighted temperatures. Each of the respective weights can be a scaler which is unique to a particular model being applied to estimate the temperature of the external environment. For example, machine learning techniques can be utilized to generate the respective weights after analyzing the data set previously described. Each respective weight assigned to a sensor can be larger, equivalent, or smaller than another respective weight assigned to another sensor. A magnitude or size of the respective weights can be based on at least one of the predicted environment, a temperature detected by each respective sensor of the first subset of sensors, or respective positions within the internal volume of each sensor of the first subset of sensors.

The process <NUM> includes the act <NUM> of selecting a first adjustment factor correlating to a predicted environment external to the housing. The first adjustment factor can at least partially correlate to the environment surrounding the electronic device. For example, the first adjustment factor can have a respective magnitude or value when the electronic device is surrounded by relatively dry air and a different magnitude or value when the portable electronic device is surrounded by a liquid (e.g., submerged in water). In some examples, the magnitude or size of the first adjustment factor can be determined based on heat transfer characteristics or another relationship between the electronic device and the determined environment.

The process <NUM> includes the act <NUM> of determining a first predicted temperature of the predicted environment based on the first adjustment factor and the weighted temperatures. For example, the first predicted temperature of the predicted environment can be determined by adding the weighted temperatures of act <NUM> and then subtracting the first adjustment factor (or adding a negative adjustment factor) as shown in Equation <NUM>.

In some examples, the process <NUM> can include evaluating the first predicted temperature. For example, the first predicted temperature can be compared to a range of expected temperature values. If the first predicted temperature falls within the range of expected temperature values, the first predicted temperature can be confirmed or validated. However, if the first predicted temperature falls outside of the range of expected temperature values (e.g., above or below the range), the process <NUM> can include modifying at least one of the first subset of sensors, the respective weights, or the first adjustment factor and then repeating the determination or calculation of the predicted temperature. For example, if the first predicted temperature falls outside of the range of expected temperature values, the process <NUM> can include selecting a second subset of sensors of the set of temperature sensors; applying a respective weight to each temperature detected by the second subset of sensors to generate alternate weighted temperatures; and determining a second predicted temperature of the environment based on the first adjustment factor and the alternate weighted temperatures.

In some examples, if the first predicted temperature falls outside of the range of expected temperature values, the process <NUM> can include selecting a second adjustment factor correlating to the predicted environment external to the housing and determining a second predicted temperature of the environment based on the second adjustment factor and the weighted temperatures. In some examples, if the first predicted temperature falls outside of the range of expected temperature values, the process <NUM> can include selecting a second subset of sensors of the set of temperature sensors; applying a respective weight to each temperature detected by the second subset of sensors to generate alternate weighted temperatures; selecting a second adjustment factor correlating to the predicted environment external to the housing; and determining a second predicted temperature of the environment based on the second adjustment factor and the alternate weighted temperatures.

In some examples, at least one of the respective weights or the adjustment factor can be determined or selected based on calibration data communicated to the electronic device from an ancillary electronic device. For example, the electronic device can be communicatively coupled to one or more ancillary electronic devices and receive calibration data, such as, temperature data, location data, sensor data, or any other data relating to the geographic location of the electronic device or the electronic device itself. In other words, the calibration data can be crowd-sourced from one or more ancillary electronic devices in communication with the electronic device. The calibration data can be received by the electronic device before the temperature estimation process (e.g., process <NUM>, <NUM>) begins or while the process is currently underway. The electronic device can be communicatively coupled to the one or more ancillary electronic devices via a wired or a wireless connection, for example, a cable interconnecting the two devices or a wireless protocol such as IEEE <NUM> (i.e., Bluetooth and Wi-Fi wireless networking technologies). Any other method for communicatively coupling the electronic device with the one or more ancillary electronic devices is also contemplated within this disclosure, such as, a USB based connection, and other wired connections.

<FIG> shows an example of a process flow diagram that can be implemented on an electronic device, for example, following the process <NUM> described above, or concurrently therewith. Thus, the processes <NUM> and <NUM> can be carried out in series, or in parallel. The additional process <NUM> includes the act <NUM> of selecting a second, different subset of sensors of the set of temperature sensors. The second subset of sensors can be at least one sensor from the set of temperature sensors, for example, the subset can be a single sensor, two sensors, three sensors, or more than three sensors. The set of temperature sensors can be disposed within the electronic device and positioned on or adjacent various components of the electronic device, such as, a main logic board or processor, an electrical power supply, a barometric vent, a speaker module, a haptic engine, a display assembly, a wireless communication module, or any other component of the electronic device.

The process <NUM> includes the act <NUM> of applying a respective weight to each temperature detected by the second subset of sensors to generate weighted temperatures. Each of the respective weights can be a scaler which is unique to a particular model being applied to estimate the temperature of the external environment. For example, machine learning techniques can be utilized to generate the respective weights after analyzing the data set previously described. Each respective weight assigned to a sensor can be larger, equivalent, or smaller than another respective weight assigned to another sensor. A magnitude or size of the respective weights can be based on at least one of the predicted environment, a temperature detected by each respective sensor of the second subset of sensors, or respective positions within the internal volume of each sensor of the second subset of sensors.

The process <NUM> includes the act <NUM> of determining a second predicted temperature of the predicted environment based on an adjustment factor and the weighted temperatures. The adjustment factor can be the same as the first adjustment factor of process <NUM>, or can be another selected adjustment factor. The second adjustment factor can be selected to correlate to a different predicted environment external to the housing. For example, the second predicted temperature of the predicted environment can be determined by adding the weighted temperatures of act <NUM> and then subtracting the first or second adjustment factor (or adding a negative adjustment factor) as shown in Equation <NUM>.

As shown in the block diagram illustrated in <FIG>, the electronic device can be a smartwatch <NUM> or another portable electronic device which is communicatively coupled to one or more ancillary electronic devices. The ancillary electronic devices can be any stationary or portable electronic devices, for example, a home automation device <NUM>, a smart thermostat <NUM>, a tablet computing device <NUM>, a smartphone <NUM>, or any other electronic device. In some examples, the smartwatch <NUM> can receive calibration data including a weather forecast for a geographic location at which the smartwatch <NUM> is located. The weather forecast can include current and future temperatures of the geographic location, humidity data, annual average temperatures for a particular day or period of time, and/or other weather related information.

Any number or variety of components in any of the configurations described herein can be included in an electronic device, as described herein. The components can include any combination of the features described herein, and can be arranged in any of the various configurations described herein. The structure and arrangement of components of a device, as well as the concepts regarding their use can apply not only to the specific examples discussed herein, but to any number of embodiments in any combination. Various examples of operational aspect and functionality of the electronic devices and electronic device components are described below, with reference to <FIG>.

<FIG> shows an example of a process flow diagram implemented on an electronic device, such as, any one of the electronic devices previously described. The electronic device can be substantially similar to, and can include some or all of the features and/or components of the devices described herein, such as electronic devices <NUM>, <NUM>, <NUM>, <NUM>. For example, the electronic device can include a housing at least partially defining an internal volume, a display assembly, a processor, a haptic engine, an electrical power supply (e.g., a battery), one or more temperature sensors, and/or any other component of the other electronic devices disclosed herein.

The process <NUM> includes the act <NUM> of measuring, with a first temperature sensor, a temperature at a first electrical component of the electronic device. The process <NUM> includes the act <NUM> of measuring, with a second temperature sensor, a temperature at a second electrical component of the electronic device. The process <NUM> includes the act <NUM> of determining, with the processor, a temperature of an environment external to the housing based on an adjustment factor and the temperatures measured by the first and second temperature sensors.

Accordingly, the process <NUM> can be utilized to generate a predicted or estimated temperature of the environment external to the housing of the electronic device. The process <NUM> can include more or fewer acts than the acts <NUM>-<NUM>. For example, the process <NUM> may optionally include an act of weighting the respective temperatures measured by the first and second temperature sensors. In other words, some of the acts are optional and therefore need not be implemented to generate the predicted or estimated temperature of the environment external to the housing of the electronic device.

The process <NUM> includes the act <NUM> of measuring, with a first temperature sensor, a temperature at a first electrical component of the electronic device. The process <NUM> includes the act <NUM> of measuring, with a second temperature sensor, a temperature at a second electrical component of the electronic device. The first and second temperature sensors can be thermistors, such as, negative temperature coefficient thermistors (NTC), positive temperature coefficient thermistors (PTC), resistance temperature detectors, thermocouples, or another type of thermistor. The first and second electrical components can be any combination of components of the electronic device, for example, a haptic engine, an electrical power supply, a speaker module, and a logic board or processor, a display component, a wireless communication module, a user interface, a backlight, or any other components disposed within portable electronic devices.

The process <NUM> includes the act <NUM> of determining, with the processor, a temperature of an environment external to the housing based on an adjustment factor and the temperatures measured by the first and second temperature sensors. For example, the temperature of the environment can be determined by weighting temperatures measured by the first and second temperature sensors, adding the weighted temperatures, and subtracting the adjustment factor (or adding a negative adjustment factor) as shown in Equation <NUM>. The adjustment factor can be at least partially based on the type of environment surrounding the electronic device. For example, the adjustment factor can have a respective magnitude or value when the electronic device is surrounded by a dry or arid environment and a different magnitude or value when the portable electronic device is surrounded by a humid or aqueous environment (e.g., submerged in water).

In some examples the first and/or second electrical component can receive electrical power from the electrical power supply (e.g., a battery). While powered by the electrical power supply, the first and/or second electrical component can generate heat during operation. For example, a logic board or processor can generate heat while operating. This additional heat can influence or vary the temperature proximate to the electrical component measured by the first and/or second temperature sensor. In some examples, the temperature measured at the first and/or second temperature sensor can be weighted to mitigate an inaccurate determination of the temperature external to the electronic device. Additionally, or alternatively, a magnitude or size of the adjustment factor can be selected which compensates for heat generated within the housing of the electronic device.

To the extent applicable to the present technology, gathering and use of data available from various sources can be used to improve the delivery to users of invitational content or any other content that may be of interest to them. 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, TWITTER® ID's, home addresses, data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal information.

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 deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables users to calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used to provide insights into a user's general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals.

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 advertisement delivery services, 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 mood-associated data for targeted content delivery services. In yet another example, users can select to limit the length of time mood-associated data is maintained or entirely prohibit the development of a baseline mood profile. 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.

Claim 1:
A portable electronic device, comprising:
a housing (<NUM>) defining an internal volume;
a set of temperature sensors (340A-E) disposed in the internal volume; and
a processor disposed in the internal volume, the processor connected to the set of temperature sensors, the processor configured to:
determine (<NUM>) a type of environment external to the housing;
select (<NUM>) a first temperature sensor and a second temperature sensor from the set of temperature sensors;
assign (<NUM>) a first weight to a first signal provided by the first sensor to generate a first weighted signal, wherein a magnitude of the first weight is based on at least the type of environment external to the housing;
assign (<NUM>) a second weight to a second signal provided by the second sensor to generate a second weighted signal, wherein a magnitude of the second weight is based on at least the type of environment external to the housing; and
determine (<NUM>) an estimated temperature of the environment based at least in part on the first and second weighted signals.