Temperature sensing system and method for electronic devices

Abstract

The present disclosure relates to temperature sensing systems and methods for electronic devices. A portable electronic device can include a housing at least partially defining an internal volume, a set of temperature sensors disposed in the internal volume, a display assembly, and a processor or main logic board. The set of temperature sensors can be positioned proximate to or attached to components of the portable electronic device. The processor can determine an ambient temperature based on an adjustment factor and weighted temperature measurements taken by a subset of the set of temperature sensors.

Description

[0001] Cross-references to related applications

[0002] This patent application claims the benefit of U.S. Provisional Patent Application No. 63 / 176,007, filed April 16, 2021, entitled “TEMPERATURE SENSING SYSTEMS AND METHODS FOR AN ELECTRONIC DEVICE,” the entire disclosure of which is incorporated herein by reference. Technical Field

[0003] The embodiments described herein generally relate to temperature sensing. More specifically, embodiments of the present invention relate to temperature sensing using one or more portable electronic devices. Background Technology

[0004] In designing electronic devices, portability is increasingly being considered, for example, to allow users to use these devices in a variety of situations and environments. In fact, power sources such as lithium batteries can power electronic devices for extended periods and can be used in a variety of indoor and outdoor environments. Components within electronic devices, such as processors, memory, antennas, and other parts, can be sealed within a casing to protect them from damage or malfunction caused by the external environment. Improvements and enhancements may be needed to portable electronic devices to provide additional functionality in various situations and environments. Summary of the Invention

[0005] According to some aspects of this disclosure, a portable electronic device may include a housing defining an internal volume, a display assembly, a set of temperature sensors disposed within the internal volume, and a processor disposed within the internal volume. The processor may be connected to the set of temperature sensors and may be configured to determine the environment outside the housing. The processor may also determine an adjustment factor related to the environment. The processor may also select a first sensor and a second sensor from the set of temperature sensors. The processor may also assign a first weight to a first signal provided by the first sensor to generate a first weighted signal. The processor may also assign a second weight to a second signal provided by the second sensor to generate a second weighted signal. The processor may also determine the temperature of the environment based on the adjustment factor and the first and second weighted signals.

[0006] In some examples, a first sensor and a second sensor are selected from a set of temperature sensors based on a determined environment. The first and second signals may include temperature measurements of corresponding areas surrounding the first and second sensors. The first sensor may be positioned near a first internal component, and the second sensor may be positioned near a second internal component. The magnitude of a first weight may be based on at least one of the environment, the first signal, or the location of the first sensor within the internal volume. The magnitude of a second weight may be based on at least one of the environment, the second signal, or the location of the second sensor within the internal volume. At least one of the first or second sensors may be a thermistor. At least one of the first or second sensors may be attached to a display assembly. The environment may be either ambient air at least partially surrounding the portable electronic device or water at least partially surrounding the portable electronic device. The portable electronic device may be a smartwatch, a smartphone, or a tablet computing device.

[0007] According to some examples, an electronic device may include a housing defining an internal volume, a set of temperature sensors disposed within the internal volume, and a processor disposed within the internal volume. The processor may be connected to the set of temperature sensors and may be configured to select a first subset of sensors from the set of temperature sensors. The processor may also apply appropriate weights to each temperature detected by the first subset of sensors to generate a weighted temperature. The processor may also select a first adjustment factor relating to a predicted environment outside the housing. The processor may also determine a first predicted temperature of the predicted environment based on the first adjustment factor and the weighted temperature.

[0008] In some examples, the processor may also be configured to evaluate a first predicted temperature and, based on the evaluation, select a second subset of sensors from the set of temperature sensors; apply appropriate weights to each temperature detected by the second subset of sensors to generate alternating weighted temperatures; and determine a second predicted temperature of the environment based on a first adjustment factor and the alternating weighted temperatures. Alternatively, based on the evaluation, the processor may confirm the predicted temperature based on the first subset of sensors.

[0009] In some examples, the processor may also be configured to evaluate a first predicted temperature, and based on that evaluation, select a second adjustment factor relating to the predicted environment outside the enclosure, and determine a second predicted temperature of the environment based on the second adjustment factor and a weighted temperature. Alternatively, based on the evaluation, the processor may confirm the predicted temperature based on a first subset of sensors.

[0010] In some examples, the processor may also be configured to evaluate a first predicted temperature and, based on the evaluation, select a second subset of sensors from the set of temperature sensors; apply appropriate weights to each temperature detected by the second subset of sensors to generate alternating weighted temperatures; select a second adjustment factor relating to the predicted environment outside the enclosure; and determine a second predicted temperature of the environment based on the second adjustment factor and the alternating weighted temperatures. Alternatively, the processor may confirm the predicted temperature based on the first subset of sensors, based on the evaluation.

[0011] In some examples, the magnitude of the corresponding weight is based on at least one of the following: the predicted environment, the temperature detected by each corresponding sensor of the first subset of sensors, or the corresponding location within the internal volume of each sensor of the first subset of sensors. The predicted environment may be either ambient air at least partially surrounding the electronic device or water at least partially surrounding the electronic device. At least one of the corresponding weight or the first adjustment factor may be based at least partially on calibration data transmitted to the processor by an auxiliary electronic device. The auxiliary electronic device may be a smart thermostat, a smartphone, a smartwatch, or a tablet computing device. The auxiliary electronic device may be a first auxiliary electronic device, and the processor may be configured to receive calibration data from both the first and second auxiliary electronic devices.

[0012] According to some aspects, an electronic device may include a housing that at least partially defines an internal volume, a first electrical component disposed within the internal volume, a first temperature sensor, a second electrical component disposed within the internal volume, a second temperature sensor, and a process for connecting the first temperature sensor and the second temperature sensor. The first temperature sensor may be located close to the first electrical component and may be configured to measure the temperature at the first electrical component. The second temperature sensor may be located close to the second electrical component and may be configured to measure the temperature at the second electrical component. A processor may be configured to determine the temperature of the environment outside the housing based on an adjustment factor and the temperatures measured by the first and second temperature sensors.

[0013] In some examples, each of the first and second temperature sensors may be a negative temperature coefficient thermistor, a positive temperature coefficient thermistor, a resistance temperature detector, or a thermocouple. The electronic device may also include a power supply. When the first electrical component is powered by the power supply, the temperature near the first electrical component may rise. The first electrical component may be a display component, a battery, a speaker, or an antenna. Attached Figure Description

[0014] This disclosure will be readily understood from the following detailed description taken in conjunction with the accompanying drawings, wherein similar reference numerals denote similar structural elements, and wherein:

[0015] Figure 1A A perspective view of the electronic device is shown.

[0016] Figure 1B A top perspective view of the electronic device is shown.

[0017] Figure 1C A bottom perspective view of the electronic device is shown.

[0018] Figure 1D An exploded view of the electronic device is shown.

[0019] Figure 2A A perspective view of electronic devices in the environment is shown.

[0020] Figure 2B A perspective view of electronic devices in the environment is shown.

[0021] Figure 3A A process flow diagram for detecting ambient temperature is shown.

[0022] Figure 3B A process flow diagram for detecting ambient temperature is shown.

[0023] Figure 3C A process flow diagram for detecting ambient temperature is shown.

[0024] Figure 4 A block diagram of a system capable of detecting ambient temperature is shown.

[0025] Figure 5 A process flow diagram for detecting ambient temperature is shown. Detailed Implementation

[0026] Reference will now be made specifically to the representative embodiments shown in the accompanying drawings. It should be understood that the following description is not intended to limit the embodiments to a single preferred embodiment. Rather, it is intended to cover alternative forms, modifications, and equivalents that may be included within the substance and scope of the embodiments defined by the appended claims.

[0027] The following disclosure relates to an electronic device that can utilize one or more temperature sensors disposed within the electronic device to estimate or predict the temperature of the environment surrounding the electronic device. Each of the one or more temperature sensors can be disposed on or near a component within the electronic device and can be used to monitor the operating temperature of that component. For example, the temperature sensor can be disposed on the main logic board or processor of the electronic device to monitor the operating temperature of the processor. In one aspect of this disclosure, temperature measurements taken by the temperature sensors to monitor the operating temperature of a component (e.g., the processor) can also be used to estimate the temperature of the external environment (i.e., outside the electronic device housing). Therefore, a single measurement taken by the sensor can be used to determine the temperature of the adjacent sensor as well as the temperature outside the electronic device.

[0028] In some examples, determining the temperature of the external environment can be beneficial to the wearer or user of electronic devices. For instance, swimmers might want to know the water temperature when they are exercising, as water temperature significantly affects the number of calories burned during exercise. The same applies to hiking, cycling, and many other sports.

[0029] In some examples, a dataset or database containing data representing numerous attributes of an electronic device can be compiled. For example, the dataset might contain a number of sensors and the corresponding location of each sensor within the electronic device. The dataset might also include the corresponding temperature measurement for each sensor and the ambient temperature at the time the measurement was taken. Machine learning techniques can be used to correlate or compare the dataset with environmental types (drought, humid, watery, etc.) and the temperatures of these environments to generate models that best or most accurately predict the temperature of the external environment based on the temperature measured within the electronic device. In other words, adjustment and scaling factors can be applied to the temperatures measured by one or more temperature sensors inside the electronic device to determine the temperature of the external environment. For example, machine learning can determine which combinations of sensor measurements, weights, and adjustment factors best or most accurately estimate the temperature of the external environment of the electronic device.

[0030] In some examples, the electronic device may be portable and may include a housing defining an internal volume. The electronic device may include a set of temperature sensors disposed within the internal volume. The electronic device may include a processor or main logic board disposed within the internal volume and connected to the set of temperature sensors. In some examples, the processor may determine the type of environment outside the housing and determine an adjustment factor associated with that environment type. The processor may select a first sensor and a second sensor from the available set of temperature sensors. The processor may assign a first weight to a first signal provided by the first temperature sensor to generate a first weighted signal. The processor may assign a second weight to a second signal provided by the second temperature sensor to generate a second weighted signal. As described herein, the processor may determine the ambient temperature based on the adjustment factor and the first and second weighted signals.

[0031] The following text is for reference only. Figures 1A to 5 These and other embodiments are discussed. However, those skilled in the art will readily understand that the detailed descriptions given herein with respect to the accompanying drawings are for illustrative purposes only and should not be construed as limiting. Furthermore, as used herein, a system, method, article, component, feature, or sub-feature including at least one of the first, second, or third options should be understood to mean a system, method, article, component, feature, or sub-feature that may include one of each listed option (e.g., only one first option, only one second option, only one third option), multiple of a single listed option (e.g., two or more first options), two options simultaneously (e.g., one first option and one second option), or combinations thereof (e.g., two first options and one second option).

[0032] Figure 1A An example of electronic device 100 is shown. Figure 1A The electronic device 100 shown is a watch, such as a smartwatch. Figure 1A The smartwatch is merely a representative example of a device that can be used in conjunction with the systems and methods disclosed herein. Electronic device 100 may correspond to any form of wearable electronic device, portable media player, media storage device, portable digital assistant (“PDA”), tablet computer, computer, mobile communication device, GPS unit, remote control device, smartwatch, smartphone, or other electronic device. Electronic device 100 may be referred to as an electronic device or consumer device. In some examples, electronic device 100 may include a housing 102 that may, for example, carry operating components within an internal volume at least partially defined by housing 102. Electronic device 100 may also include a strap 104 or other retaining components that may, if desired, secure device 100 to a user's body. References below. Figure 1BFurther details about the electronic device are provided.

[0033] Figure 1B A device 200, such as a smartwatch, is shown. The smartwatch may be substantially similar to a device such as electronic device 100 described herein and may include some or all of the features of the device described herein. Device 200 may include a housing 202 and a display assembly 204 attached to the housing 202. The housing 202 may substantially define at least a portion of the outer surface of device 200.

[0034] Display assembly 204 may include glass, plastic, or any other substantially transparent outer layer, material, part, or component. Display assembly 204 may include multiple layers, each providing a unique function as described herein. Therefore, display assembly 204 may be an interface component or may be part of an interface component. Display assembly 204 may define a front outer surface of device 200, and as described herein, this outer surface may be considered an interface surface. In some examples, the interface surface defined by display assembly 204 may receive input from a user, such as touch input.

[0035] In some examples, housing 202 may be a substantially continuous or integral component and may define one or more openings to receive components of electronic device 200. In some examples, device 200 may include input components, such as one or more buttons 206 and / or crowns 208 disposed in the openings. In some examples, material may be disposed between buttons 206 and / or crowns 208 and housing 202 to provide an airtight and / or waterproof seal at the location of the openings. Housing 202 may also define one or more openings or apertures, such as aperture 210, which may allow sound to enter or exit the internal volume defined by housing 202. For example, aperture 210 may communicate with a microphone component disposed within the internal volume.

[0036] Figure 1C A bottom perspective view of an electronic device 200 is shown. Device 200 may include a rear cover 212, which may be attached to a housing 202, for example, opposite a display assembly 204. The rear cover 212 may include ceramic, plastic, metal, or a combination thereof. In some examples, the rear cover 212 may include at least partially electromagnetically transparent components 214. Electromagnetically transparent components 214 may be transparent to electromagnetic radiation of any desired wavelength, such as visible light, infrared light, radio waves, or combinations thereof. In some examples, electromagnetically transparent components 214 may allow sensors and / or transmitters disposed within the housing 202 to communicate with the external environment. The housing 202, display assembly 204, and rear cover 212 together substantially define the internal volume and external surface of device 200.

[0037] Figure 1D An exploded view of an electronic device 300, such as a smartwatch, is shown. The smartwatch may be substantially similar to devices described herein, such as electronic devices 100 and 200, and may include some or all of the features of the devices described herein. The electronic device 300 may include a housing 302, a display assembly 304, and a back cover 312. The housing 302, display assembly 304, and back cover 312 together define the external surface and internal volume of the electronic device 300.

[0038] The housing 302 may be a substantially continuous or integral component and may define one or more openings 316, 318, 320 to receive components of the electronic device 300 and / or provide access to internal portions of the electronic device 300. In some examples, the electronic device 300 may include input components, such as one or more buttons 306 and / or crowns 308 that may be disposed in openings 320, 318. A microphone (not shown) may be disposed in an internal volume communicating with the outside or surrounding environment through opening 316.

[0039] Display assembly 304 may be received by and attached to housing 302. Display assembly 304 may include a cover 322 comprising a transparent material such as plastic, glass, and / or ceramic. Display assembly 304 may also include display assembly 324, which may include multiple layers and components, each performing one or more desired functions. For example, display assembly 324 may include layers that may include touch detection layers or components, force-sensitive layers or components, and one or more display layers or components that may include one or more pixels and / or light-emitting portions for displaying visual content and / or information to a user. In some examples, display layers or components may include liquid crystal displays (LCDs), light-emitting diode (LED) displays, organic light-emitting diode (OLED) displays, and / or any other form of display. Display layers may also include one or more electrical connectors for providing signals and / or power from other components of electronic device 300 to the display layer.

[0040] In some examples, electronic device 300 may include a gasket or seal 326 disposed between display assembly 304 and housing 302 to generally define a barrier against liquid or moisture from the external environment entering the internal volume at the location of seal 326. As described herein, seal 326 may include polymer, metallic, and / or ceramic materials. Electronic device 300 may also include a similar seal (not shown) disposed between housing 302 and rear cover 312 to generally define a barrier against liquid or moisture from the external environment entering the internal volume at the location of seal. As described herein, seal may include polymer, metallic, and / or ceramic materials. A seal may be substantially similar to this seal and may include some or all of the features of this seal.

[0041] Electronic device 300 may also include internal components such as a haptic engine 328, a power supply 330 (e.g., a battery), a speaker module 336, and a logic board 332, also referred to as a main logic board 332. The main logic board may include a system-in-package (SiP) 334 disposed thereon, including one or more integrated circuits, such as processors, sensors, and memory. The SiP 334 may also include a package.

[0042] In some examples, internal components may be located below the main logic board 332 and may be at least partially located within a portion of the internal volume defined by the rear cover 312. In some examples, the electronic device 300 may include one or more wireless antennas (not shown) that are electrically connected to one or more other components of the electronic device 300. In some examples, the antennas may receive and / or transmit wireless signals at one or more frequencies and may be one or more of, for example, cellular antennas such as LTE antennas, Wi-Fi antennas, Bluetooth antennas, GPS antennas, multi-frequency antennas, etc. The antennas may be communicatively coupled to one or more additional components of the electronic device 300.

[0043] The main logic board 332 can determine the environment outside the housing 302 of the electronic device 300. This environment (i.e., a type of environment) can be determined as atmospheric or arid, such as when the user of the electronic device 300 is in a seat on a beach. Alternatively, the determined environment can be aquatic, for example, when the user enters a body of water such as the ocean, lake, or pool and the electronic device 300 is temporarily submerged. The main logic board 332 can determine the type of environment using any technology currently available or developed in the future. For example, the electronic device 300 may include one or more components that measure or detect environmental characteristics based on location information (i.e., GPS data), pressure detection, spectroscopy, moisture detection, or a combination thereof.

[0044] In some examples, electronic device 300 may include a speaker assembly 336 disposed within housing 302. Speaker assembly 336 may include one or more speakers that convert electrical signals into sound waves audible in an environment outside housing 302. For example, one or more openings 338 may be formed within housing 302 to allow the speaker assembly to fluidly communicate with the environment outside housing 302. The internal component may be disposed within an internal volume at least partially defined by housing 302 and may be attached to housing 302 via adhesives, inner surfaces, attachment features, threaded connectors, studs, columnar members, or other features formed into, defined by, or otherwise part of housing 302 and / or rear cover 312.

[0045] Electronic device 300 may include additional components, such as one or more sensors 340A-E, which detect the temperature of the space immediately surrounding the respective sensor. The one or more sensors 340A-E may be negative temperature coefficient thermistors (NTC), positive temperature coefficient thermistors (PTC), resistance temperature detectors, thermocouples, combinations thereof, or any other sensor capable of detecting the temperature of the space surrounding the sensor. In some examples, such as... Figure 1D As shown, one or more sensors 340A-E may be located on or near one or more components of electronic device 300. In other words, each of the one or more sensors 340A-E may be attached, affixed, secured, or otherwise coupled to a component of electronic device 300. Therefore, the corresponding temperature detected by each sensor 340 may be affected by or otherwise altered by the operation of the component to which the sensor 340 is attached. For example, sensor 340D may be attached to logic board 332 and detect a relatively higher temperature than that detected by sensor 340A attached to display assembly 324, because logic board 332 can generate heat during operation. Although Figure 1D The sensors 340A-E shown are illustrated as components coupled to an internal volume within housing 302 (e.g., display assembly 304, back cover 312, main logic board 332, etc.), but those skilled in the art will readily understand that one or more sensors may additionally or alternatively be attached to the exterior of housing 302 (e.g., on the outer surface of housing 302). Sensors 340A-E may be communicatively coupled to the main logic board 332 or another component within electronics 300 that has a processor. For example, one or more sensors in sensor 340A-E may be coupled to the main logic board 332 via a wired or wireless communication path.

[0046] While the corresponding temperatures measured by sensors 340A-E may not be equal to the temperature of the external environment of housing 302, the main logic board 332 can rely on one or more corresponding temperature measurements from sensors 340A-E to determine or approximate the temperature of the external environment of housing 302. In other words, the main logic board 332 can determine or approximate the temperature of the external environment of housing 302 based at least in part on temperature measurements obtained from one or more locations within housing 302. Therefore, the corresponding temperature measurements collected by sensors 340A-E can be used for a variety of purposes (e.g., determining the temperature around sensor 340 and determining the temperature outside housing 302). Determining the temperature of the external environment of electronic device 300 can be beneficial, for example, when a user is exercising and wants to know the ambient temperature (e.g., swimming, scuba diving, snorkeling, etc.).

[0047] In some examples, the main logic board 332 may rely on an equation to determine or approximate the temperature of the environment outside the enclosure. This equation may include two or more weighted temperature measurements from the respective sensors 340, and an adjustment factor or offset based on the external environment of the enclosure 302. In other words, the temperature of the environment outside the enclosure can be determined or approximated by an equation having n terms, where n represents two or more weighted temperature measurements from the respective sensors 340. Each of the n terms may be associated with at least one weighted temperature measurement collected by one or more sensors among the sensors 340. In some examples, Equation 1 shown below may be used to determine the temperature of the environment outside the enclosure 302.

[0048] Equation 1: T E =-A±(B*(value:S) n ))+(C*(value:S m ))

[0049] T in Equation 1 E The term can represent the temperature of the external environment of the enclosure 302. Term A in Equation 1 can represent an adjustment factor or offset related to the environment determined by the main logic board 332. Value: S n The term can represent a temperature measurement performed by a specific sensor in sensor 340A-E. Term B in Equation 1 can represent the temperature measurement applied to a specific sensor (i.e., by S). n The term represents the weighting or scaling factor for temperature measurements performed by the sensor. Value: S m The term can represent a temperature measurement performed by another specific sensor in sensor 340A-E. The term C in Equation 1 can represent the temperature measurement applied to another specific sensor (i.e., by S). mThe term represents the weight or scaling factor for the temperature measurement performed by the sensor (represented by the term). It is readily understood that Equation 1 may include additional weights or scaling factors to modify additional temperature measurements performed by additional sensors.

[0050] Adjustment factor A can be applied to the sum of weighted temperature values ​​to predict or estimate the temperature of the external environment. Adjustment factor A can be based, at least in part, on the specific environment surrounding electronic device 300. For example, adjustment factor A may have a corresponding magnitude or value when electronic device 300 is surrounded by a dry or arid environment, and may have a different magnitude or value when portable electronic device is surrounded by a humid or water-containing environment (e.g., submerged in water). The magnitude or value of adjustment factor A can be selected based on the thermal transfer characteristics of electronic device 300 and / or the environment, or any other characteristics.

[0051] In some examples, the adjustment factor A may be related to, or at least partially related to, the environment (or type of environment) outside the electronic device 300. Differences in the magnitude of the adjustment factor A between different environments may be based on how the environment interacts with one or more components of the electronic device 300. For example, when the electronic device is submerged underwater, the magnitude of the adjustment factor A may be relatively small because heat within the housing 302 can be transferred to the water at a higher rate. Conversely, when the electronic device is placed in an arid environment, the magnitude of the adjustment factor A may be relatively large because less heat within the housing 302 is transferred to the air surrounding the device. In some examples, the adjustment factor may be negative or otherwise subtracted from the weighted sum of temperatures to, for example, offset heat generated by electrical components located within the electronic device.

[0052] Applied to sensor S n S m The corresponding weights B and C of the measurement or signal can be scaling factors that modify the value or magnitude of the measurement or signal to achieve the best or most accurate determination of the predicted or estimated temperature of the environment surrounding the electronic device 300. In some examples, weight B can be different from weight C; for example, weight B can be greater than or higher than weight C.

[0053] In some examples, only a subset of sensors 340A-E can be relied upon to collect the temperature values ​​in Equation 1 (e.g., value: S). n Sum: S mFor example, the corresponding temperature measurements or signals from sensors 340A and 340E can be used in Equation 1. In some examples, temperature measurements or signals from two or more sensors, or fewer than two sensors, can be used to predict or estimate the temperature of the external environment of the electronic device 300. In other words, any number of sensors 340A-E, or a subset of sensors 340A-E, can be used to predict or estimate the temperature of the external environment of the electronic device 300. Furthermore, each temperature measurement or signal from two, more, or fewer sensors can be individually weighted to predict or estimate the temperature of the external environment of the electronic device 300.

[0054] In some examples, a dataset or database can be formed containing data representing numerous adjustment factors, weights, sensor locations within the electronic device, and temperature measurements taken by the sensors. Machine learning techniques can be used to correlate or compare the dataset with environmental types (arid, humid, watery, etc.) and the temperatures of these environments to generate models that can best or most accurately predict ambient temperatures. These machine learning techniques can be combined during the assembly and / or manufacturing of the electronic device and can be adjusted or tuned for each device. The dataset or database can be stored on a local or remote computer or server, and each generated model can be uploaded from the assembled device or test structure to the processor and memory of each electronic device. For example, machine learning can determine sensor 340A-E measurements (e.g., values: S). n Sum: S m The combination of sensor values, weights (e.g., weights B and C), and adjustment factors (e.g., adjustment factor A) optimally or most accurately estimates the temperature of the environment outside the electronic device 300. Therefore, after determining the environmental type (e.g., arid, humid, watery), the optimal or most accurate model associated with that type of environment (e.g., a combination of sensor values, weights, and adjustment factors) can be used to anticipate, estimate, or predict the temperature of a particular external environment.

[0055] The type of environment can be determined by any method or mechanism now known or to be 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 332 to determine the type of environment surrounding the electronic device 300. See below for reference. Figure 2A and Figure 2B Two unrestricted examples describing environment types.

[0056] Figure 2A and Figure 2BSide perspective views of an electronic device 400 positioned in a first-type environment and a second-type environment are shown, respectively. The electronic device 400 is shown as a smartwatch coupled to a wearer's wrist 402. However, the electronic device 400 can be any electronic device, including a smartphone, tablet, or other portable electronic device in other examples. The first-type environment can be a dry or relatively dry environment where the electronic device 400 is exposed to relatively little moisture (e.g., a beach). The second-type environment can be an aquatic environment where the electronic device 400 is immersed in liquid 404 (e.g., swimming in the ocean). One or more sensors of the electronic device 400 can determine the type of environment surrounding the electronic device 400. While only the first and second environments are described herein, alternative and / or additional types of environments are also within the scope of this disclosure. For example, a sauna, steam room, shower room, or other types of environments can also be identified or identified by the electronic device 400.

[0057] The following is for reference. Figures 3A to 3C and Figure 4 More detailed disclosures are provided regarding the operation and functionality of some examples of electronic devices. Figure 3A An example process flow diagram implementing a process on an electronic device is shown, such as any of the previously described electronic devices. The electronic device may be substantially similar to the devices described herein, such as electronic devices 100, 200, 300, 400, and may include some or all of the features and / or components of such devices. For example, the electronic device may include a housing defining an internal volume, a display assembly, a processor, a haptic engine, a power supply, a temperature sensor assembly, and / or any other components of other electronic devices disclosed herein.

[0058] The processor can be housed within an internal volume and can be communicatively coupled to the temperature sensor set. Process 500 includes the action 502 of determining the external environment of the housing. Process 500 includes the action 504 of determining an environment-related adjustment factor. Process 500 includes the action 506 of selecting a first sensor and a second sensor from the temperature sensor set. Process 500 includes the action 508 of assigning a first weight to a first signal provided by the first sensor to generate a first weighted signal. Process 500 includes the action 510 of assigning a second weight to a second signal provided by the second sensor to generate a second weighted signal. Process 500 includes the action 512 of determining the ambient temperature based on the adjustment factor and the first and second weighted signals.

[0059] Therefore, process 500 can be used to generate a predicted or estimated temperature of the external environment of the electronic device. Process 500 may include more or fewer actions than actions 502 to 512. For example, process 500 may optionally include actions related 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 the ambient temperature based on an adjustment factor and the first, second, and third weighted signals. In other words, some actions are optional and therefore do not need to be implemented to generate a predicted or estimated temperature of the external environment of the electronic device.

[0060] Process 500 includes action 502 of determining the external environment of the enclosure. Determining the external environment may include using one or more infrared sensors, humidity sensors, or other sensors communicatively coupled to the processor (e.g., the main logic board) to determine the type of environment surrounding the electronic device 300. For example, it may be determined that the environment or environment type is wet, such as when the electronic device is submerged in water.

[0061] Process 500 includes the action 504 of determining an environmentally relevant adjustment factor. The adjustment factor can be applied to a weighted sum of temperature values ​​to predict or estimate the temperature of the external environment. The adjustment factor can be at least partially related to the environment surrounding the electronic device. For example, the adjustment factor may have a corresponding magnitude or value when the electronic device is surrounded by relatively dry air, and the adjustment factor may have a different magnitude or value when the portable electronic device is surrounded by liquid (e.g., immersed in water). In some examples, the magnitude or value of the adjustment factor can be determined based on heat transfer characteristics or other relationships between the electronic device and the determined environment.

[0062] Process 500 includes the action 506 of selecting a first sensor and a second sensor from the set of temperature sensors. In some examples, the first and second sensors may be selected based on their location within the housing. As described herein, machine learning techniques can be applied to the dataset 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 sensors located near non-thermoelectric components of an electronic device can be used to more consistently approximate the accurate external temperature. Alternatively or additionally, the first and second sensors may be selected based on temperature values ​​measured by specific sensors in the set of temperature sensors. For example, machine learning techniques can generate models that correlate a specific measured temperature at a sensor with a specific temperature of the external environment (i.e., a combination of sensors, weights, and adjustment factors).

[0063] Process 500 includes an action 508 of assigning a first weight to a first signal provided by a first sensor to generate a first weighted signal. Process 500 includes an action 510 of assigning a second weight to a second signal provided by a second sensor to generate a second weighted signal. The first and second weights can be scaling factors that are unique for a specific model applied to estimate the external ambient temperature. For example, machine learning techniques can be used to generate the first and second weights after analyzing the previously described dataset. The first weight can be greater than, equal to, or less than the second weight. In some embodiments, the first and second weights can represent confidence levels 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 that the signal from the first sensor (i.e., a temperature sensor at a specific location within the electronic device) has a more consistent correlation with a particular type of environment. The magnitude or value of the first and second weights can be based on at least one of the environment outside the housing, the temperature detected by each of the first and second sensors, or a corresponding location within the internal volume of each of the first and second sensors.

[0064] Process 500 includes an action 512 to determine the ambient temperature based on an adjustment factor and a first weighted signal and a second weighted signal. For example, the ambient temperature can be determined by adding the first weighted signal and the second weighted signal and then subtracting the adjustment factor (or adding a negative adjustment factor), as shown in Equation 1. Although process 500 utilizes two sensors (a first sensor and a second sensor), those skilled in the art will readily understand that process 500 may include fewer than two sensors or more sensors. For example, process 500 may 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 the ambient temperature based on the adjustment factor and the first weighted signal, the second weighted signal, and the third weighted signal.

[0065] Figure 3B An example process flow diagram implementing a process on an electronic device is shown, such as any of the previously described electronic devices. The electronic device may be substantially similar to the devices described herein, such as electronic devices 100, 200, 300, 400, and may include some or all of the features and / or components of such devices. For example, the electronic device may include a housing defining an internal volume, a display assembly, a processor, a haptic engine, a power supply, a temperature sensor assembly, and / or any other components of other electronic devices disclosed herein.

[0066] The processor can be housed within an internal volume and communicatively coupled to the temperature sensor set. Process 600 includes the action 602 of selecting a first subset of sensors from the temperature sensor set. Process 600 includes the action 604 of applying a corresponding weight to each temperature detected by the first subset of sensors to generate a weighted temperature. Process 600 includes the action 606 of selecting a first adjustment factor related to the predicted environment outside the housing. Process 600 includes the action 608 of determining a first predicted temperature of the predicted environment based on the first adjustment factor and the weighted temperature.

[0067] Therefore, process 600 can be used to generate a predicted or estimated temperature of the external environment of an electronic device. Process 600 may include more or fewer actions than actions 602 to 608. For example, process 600 may optionally include an action of evaluating a first predicted temperature. In other words, some actions are optional and therefore do not need to be implemented to generate a predicted or estimated temperature of the external environment of the electronic device.

[0068] Process 600 includes the action 602 of selecting a first subset of sensors from the temperature sensor set. The first subset of sensors may be at least one sensor from the temperature sensor set; for example, the subset may be a single sensor, two sensors, three sensors, or more than three sensors. The temperature sensor set may be disposed within and located on or near various components of the electronic device, such as the main logic board or processor, power supply, air vents, speaker module, haptic engine, display assembly, wireless communication module, or any other component of the electronic device.

[0069] Process 600 includes the action 604 of applying a corresponding weight to each temperature detected by the first subset of sensors to generate a weighted temperature. Each of the corresponding weights can be a scaling factor, which is unique for the specific model applied to estimate the ambient temperature. For example, machine learning techniques can be used to generate the corresponding weights after analyzing the previously described dataset. Each corresponding weight assigned to a sensor can be greater than, equal to, or less than another corresponding weight assigned to another sensor. The magnitude or value of the corresponding weight can be based on at least one of the predicted environment, the temperature detected by each corresponding sensor of the first subset of sensors, or the corresponding location within the internal volume of each sensor of the first subset of sensors.

[0070] Process 600 includes the action 606 of selecting a first adjustment factor relating to a predicted environment outside the enclosure. The first adjustment factor may be at least partially related to the environment surrounding the electronic device. For example, the adjustment factor may have a corresponding magnitude or value when the electronic device is surrounded by relatively dry air, and the first adjustment factor may have a different magnitude or value when the portable electronic device is surrounded by liquid (e.g., immersed in water). In some examples, the magnitude or value of the first adjustment factor may be determined based on thermal transfer characteristics or other relationships between the electronic device and the determined environment.

[0071] Process 600 includes action 608 to determine a first predicted temperature of the predicted environment based on a first adjustment factor and a weighted temperature. For example, the first predicted temperature of the predicted environment can be determined by adding the weighted temperature of action 604 and then subtracting the first adjustment factor (or adding a negative adjustment factor), as shown in Equation 1.

[0072] In some examples, process 600 may include evaluating a first predicted temperature. For example, the first predicted temperature may 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 may be confirmed or verified. However, if the first predicted temperature exceeds the range of expected temperature values ​​(e.g., above or below the range), process 600 may include modifying at least one of a first subset of sensors, corresponding weights, or a first adjustment factor, and then repeating the determination or calculation of the predicted temperature. For example, if the first predicted temperature falls outside the range of expected temperature values, process 600 may include selecting a second subset of temperature sensors from the set of temperature sensors; applying corresponding weights to each temperature detected by the second subset of sensors to generate alternating weighted temperatures; and determining a second predicted temperature of the environment based on the first adjustment factor and the alternating weighted temperatures.

[0073] In some examples, if the first predicted temperature falls outside the expected temperature range, process 600 may include selecting a second adjustment factor related to the predicted environment outside the enclosure, and determining a second predicted temperature of the environment based on the second adjustment factor and a weighted temperature. In some examples, if the first predicted temperature falls outside the expected temperature range, process 600 may include selecting a second subset of temperature sensors from the set of temperature sensors; applying appropriate weights to each temperature detected by the second subset of sensors to generate alternating weighted temperatures; selecting a second adjustment factor related to the predicted environment outside the enclosure; and determining a second predicted temperature of the environment based on the second adjustment factor and the alternating weighted temperature.

[0074] In some examples, at least one of the appropriate weights or adjustment factors can be determined or selected based on calibration data transmitted from auxiliary electronic devices to the electronic device. For example, the electronic device may be communicatively coupled to one or more auxiliary electronic devices and receive calibration data, such as temperature data, location data, sensor data, or any other data related to the geographic location of the electronic device or the electronic device itself. In other words, the calibration data may be crowdsourced from one or more auxiliary electronic devices communicating with the electronic device. The calibration data may be received by the electronic device before the start of a temperature estimation process (e.g., process 500, 600) or while that process is currently in progress. The electronic device may be communicatively coupled to one or more auxiliary electronic devices via a wired or wireless connection (e.g., a cable or wireless protocol interconnecting two devices, such as IEEE 802 (i.e., Bluetooth and Wi-Fi wireless networking technologies)). Other methods for communicatively coupling the electronic device to one or more auxiliary electronic devices, such as USB-based connections and other wired connections, are also contemplated in this disclosure.

[0075] Figure 3C An example of a process flowchart that can be implemented on an electronic device is shown, for example, after or concurrently with process 600 described above. Therefore, processes 600 and 700 can be executed sequentially or in parallel. The additional process 700 includes an action 702 of selecting a second, different subset of sensors from the temperature sensor set. The second subset of sensors can be at least one sensor from the temperature sensor set; for example, the subset can be a single sensor, two sensors, three sensors, or more than three sensors. The temperature sensor set can be disposed within the electronic device and located on or near various components of the electronic device, such as the main logic board or processor, power supply, air vents, speaker module, haptic engine, display assembly, wireless communication module, or any other component of the electronic device.

[0076] Process 700 includes the action 704 of applying a corresponding weight to each temperature detected by the second subset of sensors to generate a weighted temperature. Each of the corresponding weights can be a scaling factor, which is unique for the specific model applied to estimate the ambient temperature. For example, machine learning techniques can be used to generate the corresponding weights after analyzing the previously described dataset. Each corresponding weight assigned to a sensor can be greater than, equal to, or less than another corresponding weight assigned to another sensor. The magnitude or value of the corresponding weight can be based on at least one of the predicted environment, the temperature detected by each corresponding sensor of the second subset of sensors, or the corresponding location within the internal volume of each sensor of the second subset of sensors.

[0077] Process 700 includes action 706 to determine a second predicted temperature of the prediction environment based on an adjustment factor and a weighted temperature. The adjustment factor may be the same as the first adjustment factor in process 600, or it may be another selected adjustment factor. The second adjustment factor can be selected to correlate with different prediction environments outside the enclosure. For example, the second predicted temperature of the prediction environment can be determined by adding the weighted temperature of action 704 and then subtracting either the first or second adjustment factor (or adding a negative adjustment factor), as shown in Equation 1.

[0078] like Figure 4 As shown in the block diagram, the electronic device can be a smartwatch 800 or another portable electronic device communicatively coupled to one or more auxiliary electronic devices. The auxiliary electronic device can be any fixed or portable electronic device, such as a home automation device 802, a smart thermostat 804, a tablet computing device 806, a smartphone 808, or any other electronic device. In some examples, the smartwatch 800 can receive calibration data, including a weather forecast for the location of the smartwatch 800. This weather forecast may include current and future temperature and humidity data for that location, the annual average temperature for a specific date or time period, and / or other weather-related information.

[0079] Any number or type of components in any configuration described herein may be included in an electronic device, as described herein. Components may include any combination of the features described herein and may be arranged in any of the various configurations described herein. The structure and arrangement of device components, and the concepts relating to their use, can be applied not only to the specific examples discussed herein, but also to any combination of any number of embodiments. References below... Figure 5 Various examples describing the operational aspects and functions of electronic devices and electronic device components.

[0080] Figure 5 An example process flow diagram implementing a process on an electronic device is shown, such as any of the previously described electronic devices. The electronic device may be substantially similar to the devices described herein, such as electronic devices 100, 200, 300, 400, and may include some or all of the features and / or components of such devices. For example, the electronic device may include a housing that at least partially defines an internal volume, a display assembly, a processor, a haptic engine, a power supply (e.g., a battery), one or more temperature sensors, and / or any other components of other electronic devices disclosed herein.

[0081] Process 900 includes an action 902 of measuring the temperature at a first electrical component of the electronic device using a first temperature sensor. Process 900 includes an action 904 of measuring the temperature at a second electrical component of the electronic device using a second temperature sensor. Process 900 includes an action 906 of determining the temperature of the external environment of the housing using a processor based on an adjustment factor and the temperatures measured by the first and second temperature sensors.

[0082] Therefore, process 900 can be used to generate a predicted or estimated temperature of the external environment of the electronic device enclosure. Process 900 may include more or fewer actions than actions 902 to 906. For example, process 900 may optionally include an action of weighting the corresponding temperatures measured by the first temperature sensor and the second temperature sensor. In other words, some actions are optional and therefore do not need to be implemented to generate a predicted or estimated temperature of the external environment of the electronic device enclosure.

[0083] Process 900 includes an action 902 of measuring the temperature at a first electrical component of the electronic device using a first temperature sensor. Process 900 includes an action 904 of measuring the temperature at a second electrical component of the electronic device using a second temperature sensor. 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 other types of thermistors. The first and second electrical components can be any combination of components of the electronic device, such as a haptic engine, power supply, speaker module and logic board or processor, display component, wireless communication module, user interface, backlight, or any other component disposed within the portable electronic device.

[0084] Process 900 includes an action 906 in which the processor determines the temperature of the external environment of the housing based on an adjustment factor and the temperatures measured by the first and second temperature sensors. For example, the ambient temperature can be determined by weighting the temperatures measured by the first and second temperature sensors, adding the weighted temperature, and then subtracting the adjustment factor (or adding a negative adjustment factor), as shown in Equation 1. The adjustment factor can be based at least in part on the type of environment surrounding the electronic device. For example, the adjustment factor may have a corresponding 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 water-containing environment (e.g., submerged in water).

[0085] In some examples, the first and / or second electrical components may receive power from a power source (e.g., a battery). When powered by the power source, the first and / or second electrical components may generate heat during operation. For example, a logic board or processor may generate heat during operation. This additional heat may affect or alter the temperature of adjacent electrical components as measured by the first and / or second temperature sensors. In some examples, the temperatures measured at the first and / or second temperature sensors may be weighted to reduce inaccuracies in determining the external temperature of the electronic device. Additionally or alternatively, the magnitude or value of an adjustment factor may be selected to compensate for heat generated within the housing of the electronic device.

[0086] Within the limits applicable to this technology, the collection and use of data from various sources can be used to improve the delivery of inspirational or other content that users may be interested in. This disclosure contemplates that, in some instances, such collected data may include personal information that uniquely identifies or can be used to contact or locate specific individuals. Such personal information may include demographic data, location-based data, telephone numbers, email addresses, etc. ID, home address, data or records related to the user's health or health level (e.g., vital sign measurements, medication information, exercise information), date of birth, or any other identifying or personal information.

[0087] This disclosure recognizes that the use of such personal information data in the techniques of this invention can benefit users. For example, the personal information data can be used to deliver targeted content that is of interest to the user. Therefore, the use of such personal information data enables users to have planned control over the delivered content. Furthermore, this disclosure also anticipates other uses of personal information data that are beneficial to users. For example, health and fitness data can be used to provide insights into a user's overall health status or as positive feedback for individuals using technology to pursue health goals.

[0088] This disclosure assumes that entities responsible for collecting, analyzing, disclosing, transmitting, storing, or otherwise using such personal information data will comply with established privacy policies and / or privacy practices. Specifically, such entities should implement and adhere to privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy and security of personal information data. Such policies should be easily accessible to users and should be updated as data collection and / or use change. Personal information from users should be collected for the entity's lawful and reasonable purposes and not shared or sold outside of these lawful uses. Furthermore, such collection / sharing should be conducted only after obtaining informed consent from users. In addition, such entities should consider taking any necessary steps to protect and safeguard access to such personal information data and ensure that others with access to such personal information data comply with their privacy policies and processes. Additionally, such entities may be subject to third-party evaluations to demonstrate their compliance with widely accepted privacy policies and practices. Furthermore, policies and practices should be adapted to the specific types of personal information data collected and / or accessed, and to applicable laws and standards, including specific considerations regarding jurisdiction. For example, in the United States, the collection or acquisition of certain health data may be governed by federal and / or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); while in other countries, health data may be subject to other regulations and policies and should be handled accordingly. Therefore, different privacy practices should be maintained for different types of personal data in each country.

[0089] Regardless of the foregoing, this disclosure also contemplates implementation schemes for users to selectively prevent the use or access to personal information data. That is, this disclosure contemplates providing hardware and / or software components to prevent or block access to such personal information data. For example, with regard to advertising delivery services, the inventive technology can be configured to allow users to opt-in or opt-out at any time during or after service registration to participate in the collection of personal information data. In another example, users can choose not to provide emotion-related data for a targeted content delivery service. In yet another example, users can choose to limit the duration for which emotion-related data is retained, or completely prohibit the development of underlying emotional states. In addition to providing "opt-in" and "opt-out" options, this disclosure envisions providing notifications related to access to or use of personal information. For example, users can be notified when downloading an application that their personal information data will be accessed, and then reminded again just before the application accesses the personal information data.

[0090] Furthermore, the purpose of this disclosure is to manage and process personal information data to minimize the risk of unintentional or unauthorized access or use. Once data is no longer needed, this risk can be minimized by limiting data collection and deleting data. Additionally, and where applicable, including in certain health-related applications, data deidentification can be used to protect user privacy. Deidentification can be facilitated, where appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of stored data (e.g., collecting location data at the city level rather than the address level), controlling how data is stored (e.g., aggregating data on users), and / or other methods.

[0091] Therefore, while this disclosure broadly covers the use of personal information data to implement one or more of the various disclosed embodiments, it is also contemplated that various embodiments can be implemented without access to such personal information data. That is, various embodiments of the present invention will not be rendered inoperable due to the absence of all or part of such personal information data. For example, preferences can be inferred based on non-personal information data or a minimal amount of personal information, such as content requested by a device associated with a user, other non-personal information available to the content delivery service, or publicly available information, thereby selecting content and delivering it to the user.

[0092] For illustrative purposes, the foregoing description uses specific names to provide a thorough understanding of the described embodiments. However, it will be apparent to those skilled in the art that specific details are not required to practice the described embodiments. Therefore, the foregoing description of specific embodiments described herein is presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the embodiments to the precise forms disclosed. It will be apparent to those skilled in the art that many modifications and variations are possible in light of the teachings above.

Claims

1. A portable electronic device, comprising: An outer casing that defines an internal volume; A display assembly, which is attached to the housing; A temperature sensor assembly, wherein the temperature sensor assembly is disposed within the internal volume; as well as A processor, disposed within the internal volume, connected to the temperature sensor assembly, is configured to: Determine the type of environment outside the enclosure; Select a first temperature sensor and a second temperature sensor from the set of temperature sensors; Based on the type of the environment, a first weight is assigned to the first signal provided by the first temperature sensor to generate a first weighted signal; Based on the type of environment, a second weight is assigned to the second signal provided by the second temperature sensor to generate a second weighted signal; as well as The temperature of the environment is determined at least in part based on the first weighted signal and the second weighted signal.

2. The portable electronic device of claim 1, wherein the first temperature sensor and the second temperature sensor are selected from the set of temperature sensors based at least in part on the type of the environment.

3. The portable electronic device according to claim 1, wherein: The processor is further configured to determine an adjustment factor corresponding to the type of the environment; and The temperature of the environment is determined at least in part based on the adjustment factor.

4. The portable electronic device according to claim 1, wherein: The first temperature sensor is positioned near the first internal component; and The second temperature sensor is located near a second, different internal component.

5. The portable electronic device according to claim 1, wherein: The value of the first weight is also based on at least one of the positions of the first signal or the first temperature sensor within the internal volume; and The value of the second weight is also based on at least one of the second signal or the location of the second temperature sensor within the internal volume.

6. The portable electronic device of claim 1, wherein the type of environment includes at least one of a dry environment, a humid environment, or an aqueous environment.

7. The portable electronic device of claim 1, wherein at least one of the first temperature sensor or the second temperature sensor is attached to the display assembly.

8. The portable electronic device of claim 1, wherein at least one of the first weight, the second weight, or the adjustment factor is determined by a machine learning algorithm.

9. A portable electronic device, comprising: An outer casing that defines an internal volume; A temperature sensor assembly, wherein the temperature sensor assembly is disposed within the internal volume; as well as A processor, disposed within the internal volume and connected to the temperature sensor, is configured to: Determine the environmental type of the environment outside the casing; Select a subset of temperature sensors from the set of temperature sensors; The corresponding weights are applied to each temperature detected by the subset of temperature sensors to generate a weighted temperature set; Select the adjustment factor corresponding to the environment type; as well as The estimated temperature of the environment is determined at least in part based on the adjustment factor and the weighted temperature set.

10. The portable electronic device of claim 9, wherein each corresponding weight is based on the environment type, the temperature detected by the temperature sensors in the subset of temperature sensors, or at least one of the corresponding locations of each temperature sensor in the subset of temperature sensors within the internal volume.

11. The portable electronic device of claim 9, wherein the environment type includes at least one of a dry environment or a water-containing environment, wherein in the dry environment ambient air at least partially surrounds the housing, and in the water-containing environment water at least partially surrounds the housing.

12. The portable electronic device according to claim 9, wherein: The environment type of the environment includes the first environment type of the first environment; The subset of temperature sensors includes a first subset of temperature sensors; The weighted temperature set includes a first weighted temperature set; The estimated temperature includes a first estimated temperature; and The processor is further configured to: Determine the second environment type of the second environment outside the casing; The second subset of temperature sensors in the set of temperature sensors is selected based on the second environment type. The corresponding weights are applied to each temperature detected by the second subset of temperature sensors to generate a second weighted temperature set; as well as The second estimated temperature of the second environment is determined at least in part based on the second weighted temperature set.

13. The portable electronic device according to claim 12, wherein: The adjustment factor includes a first adjustment factor; and The processor is further configured to: Select the second adjustment factor corresponding to the second environment type; The second estimated temperature for determining the second environment is further based, at least in part, on the second adjustment factor.

14. The portable electronic device of claim 9, wherein at least one of the corresponding weights or the adjustment factors is determined at least in part based on calibration data transmitted from the auxiliary electronic device to the processor.

15. The portable electronic device of claim 14, wherein the auxiliary electronic device includes a smart thermostat, a smartphone, a smartwatch, or a tablet computing device.

16. The portable electronic device of claim 14, wherein the auxiliary electronic device includes an assembly device.

17. An electronic device comprising: An outer casing that at least partially defines an internal volume; A first electrical component is disposed within the internal volume; A first temperature sensor is positioned close to the first electrical component and configured to measure a first temperature at the first electrical component. A second electrical component is disposed within the internal volume; A second temperature sensor is located close to the second electrical component and is configured to measure a second temperature at the second electrical component. as well as A processor, connected to the first temperature sensor and the second temperature sensor, is configured to: Determine the type of environment outside the enclosure; The estimated temperature of the environment outside the enclosure is determined based at least in part on the following: The weighted set of temperatures including the first temperature and the second temperature; as well as An adjustment factor corresponding to the type of environment outside the casing.

18. The electronic device of claim 17, wherein at least one of the first temperature sensor or the second temperature sensor comprises a negative temperature coefficient thermistor, a positive temperature coefficient thermistor, a resistance temperature detector, or a thermocouple.

19. The electronic device according to claim 17, wherein: The electronic device also includes a power supply; and The first temperature rises when the first electrical component is being powered by the power source.

20. The electronic device of claim 17, wherein the first electrical component comprises a display assembly, a battery, a speaker, or an antenna.

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