Wireless charging alignment

Abstract

The technology provides a system for determining wireless charging alignment. In this regard, one or more processors (112) may receive motion data indicating the motion of a computing device (Figure 4; 910) from one or more sensors (115) of the computing device. The one or more processors may also receive charging data (920) related to the energy storage state of the computing device or the energy transfer state between the wireless charger and the computing device. Based on the motion data and the charging data, a reference vector associated with at least two charging rates may be determined (930). Then, based on the reference vector and the associated charging rates, an alignment vector between the computing device and the wireless charger may be determined (940). Based on the alignment vector, an output may be generated to guide the movement of the computing device to align with the wireless charger (950).

Description

[0001] Cross - reference to related applications

[0002] This application claims the benefit of Application Serial No. 16 / 295,064, filed on March 7, 2019, the disclosure of which is incorporated herein by reference. Background Art

[0003] A wireless charger can supply electric charge to a wireless charging device without a conductive connection, such as a contact or a wire, between the wireless charger and the wireless charging device. To this end, the wireless charger may be provided with a transmitter coil for inductively transmitting energy, and the wireless charging device may be provided with a receiver coil for inductively receiving the transmitted energy.

[0004] Although the charging rate or efficiency may depend to a large extent on the alignment between the transmitter coil and the receiver coil, aligning these two coils may not be easy. For example, the position of the transmitter coil inside the wireless charger and / or the position of the receiver coil inside the wireless charging device may not be visible to the user. Therefore, some wireless chargers are designed for specific types of wireless charging devices, with brackets or other physical features to ensure correct alignment. However, such specially designed wireless chargers can only be used to charge the devices they are designed for. Alternatively, the wireless charger and / or the wireless charging device can be designed with multiple coils or complex coil geometries to ensure that a certain amount of energy can be transferred even with poor alignment. However, this design may be challenging in cases where space is limited. Summary of the Invention

[0005] The present disclosure provides a method, including: receiving, by one or more processors, motion data indicating motion of a computing device from one or more sensors of the computing device; receiving, by the one or more processors, charging data related to an energy storage state of the computing device or an energy transfer state between a wireless charger and the computing device; determining, by the one or more processors, a reference vector associated with at least two charging rates based on the motion data and the charging data, each charging rate corresponding to an amount of energy transferred per unit time between the wireless charger and the computing device; determining, by the one or more processors, an alignment vector between the computing device and the wireless charger based on the reference vector and the associated charging rates; and generating, by the one or more processors, an output guiding movement of the computing device to align with the wireless charger based on the alignment vector.

[0006] The motion data may include acceleration measurements of the motion of the computing device. The method may further include determining, by the one or more processors, a displacement of the computing device relative to a previous position of the computing device based on the acceleration measurements, wherein the reference vector is determined based on the displacement.

[0007] The motion data may include rotation measurements of the motion of the computing device. The method may further include determining, by the one or more processors, orientation information based on the rotation measurements, wherein the alignment vector is determined based on the orientation information.

[0008] The alignment vector may be a vector that connects the position of the charging system of the computing device to the position of the charging system of the wireless charger. The alignment vector may be a vector that connects the center of the receiver coil of the computing device to the center of the transmitter coil of the wireless charger.

[0009] The method may further include: receiving, by the one or more processors, past motion data that captures the motion of the computing device being placed on a surface; training, by the one or more processors, one or more models based on the past motion data for predicting a movement vector of the computing device when the computing device is placed on the surface. The method may further include predicting, by the one or more processors, a movement vector of the computing device when the computing device is being placed on the wireless charger using the one or more models, wherein the alignment vector is further determined based on the predicted movement vector.

[0010] The output may include a graphical representation of the relative positions of the computing device and the wireless charger and a graphical representation of the alignment vector. The output may include a haptic output in the direction of the alignment vector. The output may include audio instructions.

[0011] The method may further include: receiving, by the one or more processors, image data from the one or more sensors; identifying, by the one or more processors, the wireless charger based on the image data; determining, by the one or more processors, a relative position of the wireless charger and the computing device based on the image data, wherein the alignment vector is further determined based on the relative position of the wireless charger and the computing device.

[0012] The method may further include: receiving, by the one or more processors, a signal strength measurement of a wireless connection between the wireless charger and the computing device; determining, by the one or more processors, a relative position of the wireless charger and the computing device based on the signal strength measurement, wherein an alignment vector is further determined based on the relative position of the wireless charger and the computing device.

[0013] The method may further include: determining, by the one or more processors, that the wireless charger includes a plurality of charging systems; identifying, by the one or more processors, one of the plurality of charging systems that is closest to the computing device, wherein the alignment vector is determined for the identified charging system that is closest to the computing device.

[0014] The present disclosure also provides a system, including: one or more processors configured to: receive motion data from one or more sensors of a computing device, the motion data indicating motion of the computing device; receive charging data related to an energy storage state of the computing device or an energy transfer state between the wireless charger and the computing device; determine, based on the motion data and the charging data, a reference vector associated with at least two charging rates, each charging rate corresponding to an amount of energy transferred per unit time between the wireless charger and the computing device; determine an alignment vector between the computing device and the wireless charger based on the reference vector and the associated charging rates; and generate an output based on the alignment vector to guide movement of the computing device to align with the wireless charger.

[0015] The system may further include one or more sensors, wherein the one or more sensors include at least one of the following: an accelerometer, a gyroscope, and an optical sensor.

[0016] The system may further include a communication module configured to measure a signal strength of a connection between the computing device and the wireless charger; wherein the one or more processors are further configured to: receive a signal strength measurement of the connection between the computing device and the wireless charger; and determine a relative position of the wireless charger and the computing device, wherein the alignment vector is further determined based on the relative position of the wireless charger and the computing device.

[0017] The system may further include one or more output devices, wherein the one or more output devices include at least one of the following: a display, a tactile interface, and a speaker.

[0018] One or more processors of the system may also be configured to: receive past motion data that captures motion of the computing device placed on a surface; and based on the past motion data, train one or more models for predicting a movement vector of the computing device when the computing device is placed on the surface. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a block diagram of an example system in accordance with aspects of the present disclosure.

[0020] Figure 2 is a schematic diagram illustrating an example system in accordance with aspects of the present disclosure.

[0021] Figure 3 Illustrates an example computing device and an example wireless charger in accordance with aspects of the present disclosure.

[0022] Figure 4 Illustrates an example of aligning a computing device with a wireless charger using motion data in accordance with aspects of the present disclosure.

[0023] Figure 5 Illustrates determining in accordance with aspects of the present disclosure Figure 4 an example of an alignment vector between a computing device and a wireless charger.

[0024] Figure 6 Illustrates determining in accordance with aspects of the present disclosure Figure 4 an example of an orientation of a computing device relative to a wireless charger.

[0025] Figure 7 Illustrates another example of aligning a computing device with a wireless charger using motion data and other data in accordance with aspects of the present disclosure.

[0026] Figure 8 Illustrates additional examples of outputs for assisting a user in making a charging alignment between a Figure 7 computing device and a wireless charger in accordance with aspects of the present disclosure.

[0027] Figure 9 is a flowchart in accordance with aspects of the present disclosure. DETAILED DESCRIPTION

[0028] OVERVIEW

[0029] This technology generally relates to wireless charging alignment. As described above, the charging rate or efficiency between a wireless charger and a wireless charging device may depend to a large extent on the alignment between the transmitter coil and the receiver coil. However, it may be difficult for a user to align the two coils because the position of the transmitter coil inside the wireless charger and / or the position of the receiver coil inside the wireless charging device may not be visible to the user. In addition, the user may wish to use different wireless chargers on different occasions based on availability. Therefore, it may be even more difficult to attempt to align a wireless charging device with an unfamiliar wireless charger. To address these issues, a system can be configured to use motion data and charging data to determine the wireless charging alignment between a computing device and a wireless charger and provide alignment instructions to the user in real time.

[0030] In this regard, one or more processors can receive motion data from one or more sensors of a computing device, the motion data indicating the movement of the computing device. For example, the motion data can include inertial measurements measured by an inertial measurement unit (IMU) of the computing device. For example, the inertial measurements can include acceleration measurements from an accelerometer of the computing device. The acceleration measurements can include direction information, such as a three-dimensional vector. For another example, the inertial measurements can include rotational or orientation measurements from a gyroscope of the computing device.

[0031] The processor can also receive charging data of the computing device. For example, the charging data may be related to the energy storage state of the computing device or the energy transfer state between the wireless charger and the computing device. For example, the charging data can include a charging rate measurement, such as the amount of energy transferred per unit time between the wireless charger and the computing device. In some cases, the processor can determine the charging rate based on the charging data, such as based on the amount of charge in the battery at different time points.

[0032] Based on the received motion data and charging data, the processor can determine a reference vector associated with at least two charging rates. For example, each charging rate can correspond to the amount of energy transferred per unit time between the wireless charger and the computing device. For example, the displacement vector can be determined based on the acceleration measurements, such as by double integrating the acceleration vector. The displacement vector and the charging data can be matched based on their respective timestamps. Therefore, the displacement vector or a combination of displacement vectors can be selected as the reference vector such that the reference vector is associated with at least two charging rates. For example, the start of the reference vector can be associated with a first charging rate, while the end of the reference vector can be associated with a second charging rate.

[0033] Based on the reference vector and the associated charging rate, the processor can determine the alignment vector between the computing device and the wireless charger. For example, for a wireless charger according to a standard configuration, the charging rate at a predetermined distance from the center of the transmitter coil of the wireless charger can be known. Thus, the charging rate of the wireless charger can be represented by a pattern, such as a series of consecutive rings or spheres centered on the center of the transmitter coil. The processor can determine the position of the reference vector in the pattern and then determine the alignment vector that connects the end of the reference vector to the center of the transmitter coil.

[0034] Once the alignment vector is determined, the one or more processors can generate an output that guides the movement of the computing device to align with the wireless charger. For example, the output can be a display that shows a graphical representation of the relative positions of the transmitter coil of the wireless charger and the receiver coil of the computing device and the alignment vector. Additionally or alternatively, the output can include the display of other graphics or text, audio output, tactile output, etc.

[0035] Instead of determining the alignment vector, the processor can determine whether the most recent movement of the computing device has caused an increase or a decrease in the charging rate and generate an output that guides the movement of the computing device based on that determination. For example, if the charging rate increases during the most recent movement, the processor can generate an output instructing the user to continue moving in the same direction. For another example, if the charging rate decreases during the most recent movement, the processor can generate an output instructing the user to move in the opposite direction.

[0036] Additionally or alternatively, the processor can also receive and use other types of data to determine the charging alignment. For example, the processor can receive image data from a camera and can identify the wireless charger around the computing device. The processor can determine the relative position of the wireless charger and the computing device and generate an output that guides the movement of the computing device based on that relative position. For another example, the processor can receive signal strength measurements for the wireless connection between the computing device and the wireless charger. The processor can determine the distance between the wireless charger and the computing device based on the signal strength measurements and generate an output that guides the movement of the computing device based on that distance.

[0037] In another aspect, one or more models can be trained to predict the movement of the computing device when placed on the wireless charger. For example, when the computing device is placed on a surface, past movement data that captures the movement of the computing device can be received by the processor. The processor can use the past movement data to train a model to identify the movement patterns of the computing device. Once trained, the model can be used to predict the movement of the computing device when placed on the wireless charger, such as predicting the vector of the movement. Thus, the processor can further use the predicted vector to determine the charging alignment and generate instructions.

[0038] This technology is advantageous because it allows the system to assist the user in accurately aligning the computing device with the wireless charger. With better alignment, a higher charging rate can be achieved, making the charging process more energy efficient. The system can determine the charging alignment of the computing device with a wireless charger of any of a variety of shapes or sizes. Additionally, the system can determine the charging alignment even when the wireless charging capability is provided by an accessory (such as a cover or stand) of the computing device. The technology also provides a trained model to predict the movement of the computing device when it is placed on the wireless charger, which can further improve the speed and accuracy of the alignment process.

[0039] Example System

[0040] Figure 1 and Figure 2 FIG. illustrates an example system 100 in which the features described herein may be implemented. It should not be regarded as limiting the scope of the present disclosure or the usefulness of the features described herein. In this example, system 100 may include computing devices 110, 120, and 130, a wireless charger 140, and a storage system 150. For example, as shown, computing device 110 includes one or more processors 112, a memory 114, and other components commonly present in a general-purpose computing device.

[0041] Memory 114 may store information accessible by one or more processors 112, including instructions 116 executable by the one or more processors 112. Memory 114 may also include data 118 retrievable, manipulable, or stored by processor 112. Memory 114 can be any non-transitory type capable of storing information accessible by the processor, such as a hard disk drive, a memory card, ROM, RAM, DVD, CD-ROM, writeable and read-only memories.

[0042] Instructions 116 can be any set of instructions directly executable by the one or more processors, such as machine code, or any set of instructions indirectly executable, such as a script. In this regard, the terms "instructions", "application", "steps", and "program" may be used interchangeably herein. Instructions may be stored in object code format for direct processing by the processor, or in any other computing device language, including scripts or collections of independent source code modules that are interpreted on demand or pre-compiled. The functions, methods, and routines of the instructions are explained in more detail below.

[0043] The one or more processors 112 may retrieve, store, or modify data 118 according to instructions 116. For example, although the subject matter described herein is not limited to any particular data structure, the data may be stored in computer registers, in a relational database as a table with many different fields and records, or as an XML document. The data may also be formatted in any computer-readable format, such as but not limited to binary values, ASCII, or Unicode. Additionally, the data may include any information sufficient to identify relevant information, such as numbers, descriptive text, proprietary codes, pointers, references to data stored in other memories (such as other network locations), or information used by a function to compute relevant data.

[0044] The one or more processors 112 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor may be a dedicated component, such as an application specific integrated circuit (“ASIC”) or other hardware-based processor. Although not required, the computing device 110 may include dedicated hardware components to perform specific computing processes faster or more efficiently, such as decoding video, matching video frames with images, distorting video, encoding distorted video, etc.

[0045] Although Figure 1 Functionally, the processor, memory, and other elements of the computing device 110 are shown within the same box, but the processor, computer, computing device, or memory may actually include multiple processors, computers, computing devices, or memories, which may or may not be stored within the same physical housing. For example, the memory may be a hard disk drive or other storage medium located in a housing different from that of the computing device 110. Accordingly, references to a processor, computer, computing device, or memory will be understood to include references to a collection of processors, computers, computing devices, or memories that may or may not operate in parallel. For example, the computing device 110 may include computing devices operating in a distributed system, etc. Additionally, although some of the functions described below are indicated as occurring on a single computing device with a single processor, aspects of the subject matter described herein may be implemented by multiple computing devices, such as, by transmitting information over a network 160.

[0046] Each of the computing devices 110, 120, 130 may be located at different nodes of the network 160 and be capable of communicating directly and indirectly with other nodes of the network 160. Although Figure 1 and Figure 2Only a few computing devices are depicted, but it should be understood that a typical system can include a large number of connected computing devices, each different computing device located at a different node of network 160. The network 160 and intermediate nodes described herein can be interconnected using various protocols and systems such that the network can be part of the Internet, World Wide Web, a particular intranet, a wide area network, or a local network. The network can utilize standard communication protocols such as Ethernet, WiFi, and HTTP, one or more company-proprietary protocols, and various combinations of the foregoing. Although certain advantages are obtained when sending or receiving information as described above, other aspects of the subject matter described herein are not limited to any particular information transmission means.

[0047] Each of computing devices 120 and 130 can be configured similarly to computing device 110, having one or more processors, memory, and instructions as described above. For example, as Figure 1 and Figure 2 shown, computing devices 110 and 120 can each be client computing devices for use by user 210 and have all of the component units typically associated with a personal computing device, such as a central processing unit (CPU), memory for storing data and instructions (e.g., RAM and an internal hard drive), input and / or output devices, sensors, a communication module, a clock, etc. For another example as Figure 1 and Figure 2 shown, computing device 130 can be a server computer and can have all of the components typically associated with a server computer, such as a processor and memory for storing data and instructions.

[0048] Although computing devices 110 and 120 can each include a full-sized personal computing device, they can alternatively include mobile computing devices capable of wirelessly exchanging data with a server over a network such as the Internet. For example, computing device 110 can be a laptop computer as Figure 2 shown, or a tablet PC or netbook capable of obtaining information over the Internet. For another example, computing device 120 can be a mobile phone as Figure 2 shown, or some other mobile device, such as a PDA with wireless capabilities. In other cases, one or more of computing devices 110 and 120 can be wearable computing devices, such as a smartwatch or a head-mounted device.

[0049] Computing devices 110 and 120 may include one or more user inputs, such as respective user inputs 111 and 121. For example, user inputs may include mechanical actuators, soft actuators, peripherals, sensors, and / or other components. For example, mechanical actuators may include buttons, switches, etc. Soft actuators may include touchpads and / or touchscreens. Peripherals may include keyboards, mice, etc. Sensors for user input may include microphones for detecting voice commands, visual or optical sensors for detecting gestures, etc.

[0050] Computing devices 110 and 120 may include one or more output devices, such as respective output devices 113 and 123. For example, output devices may include user displays, such as screens or touchscreens, for displaying information or graphics to the user. Output devices may include one or more speakers, transducers, or other audio outputs. Output devices may include tactile interfaces or other tactile feedback that provide non-visual and non-auditory information to the user.

[0051] Computing devices 110 and 120 may include one or more sensors, such as respective sensors 115 and 125. For example, sensors may include visual sensors, such as cameras, or other types of optical sensors, such as infrared sensors. Sensors may include audio sensors, such as microphones. Sensors may also include motion sensors, such as IMUs. According to some examples, an IMU may include an accelerometer, such as a 3-axis accelerometer, and a gyroscope, such as a 3-axis gyroscope. Sensors may also include barometers, vibration sensors, thermal sensors, radio frequency (RF) sensors, magnetometers, and pressure sensors. Additional or different sensors may also be employed.

[0052] To be powered, computing devices 110 and 120 may include one or more charging systems, such as respective charging systems 117 and 127. Charging systems 117 and / or 127 may be configured to receive charge without the need for a conductive connection (such as a wired connection). In this regard, charging systems 117 and / or 127 may be configured to perform wireless charging in any of a variety of ways, such as by inductive charging. For example, charging systems 117 and / or 127 may each include one or more receiver coils for inductively receiving electromagnetic energy from one or more transmitter coils. In some cases, charging systems 117 and / or 127 may be configured for wireless charging according to a standard configuration, such as the Qi standard, the Power Matters Alliance (PMA) standard, etc. In other cases, charging systems 117 and / or 127 may additionally or alternatively be configured for wireless charging according to a non-standard protocol (such as a proprietary protocol).

[0053] Additionally or alternatively, charging systems 117 and / or 127 can be configured to charge using a conductive connection such as a conductive contact or a wired connection. In cases where charging systems 117 and / or 127 are not configured for wireless charging, an accessory can be used to enable wireless charging. For example, a lid or a stand can include one or more receiver coils for inductively receiving electromagnetic energy from a wireless charger, and can also include one or more conductive elements such as contacts, wires, or dongles for connecting to the charging system 117 of computing device 110, or the charging system 127 of computing device 120.

[0054] Charging systems 117 and / or 127 can be configured to collect charging data of computing device 110 and / or 120. For example, charging systems 117 and / or 127 can include one or more energy storage devices such as batteries, and the collected charging data can include states such as the amount of charge in the energy storage. For another example, when being charged, charging systems 117 and / or 127 can measure the amount of energy received per unit time (e.g., in W (watts) or J / s (joules per second)) or the charging rate. Alternatively or additionally, charging systems 117 and / or 127 can receive charging data from wireless charger 140.

[0055] To obtain information from and send information to remote devices such as server computing device 130, wireless charger 140, and to each other, computing devices 110 and 120 can each include a communication module, such as separate communication modules 119 and 129. The communication module can implement a wireless network connection, a wireless ad-hoc connection, and / or a wired connection. Via the communication module, the computing device can establish a communication link, such as a wireless link. For example, communication modules 119 and / or 129 can include one or more antennas, transceivers, and other components for operating at radio frequencies. Communication modules 119 and / or 129 can be configured to support communication via cellular, LTE, 4G, WiFi, GPS, and other networking architectures. Communication modules 119 and / or 129 can be configured to support Bluetooth LE, near field communication, and non-networked wireless arrangements. Communication modules 119 and / or 129 can support a wired connection such as USB, micro-USB, USB Type-C, or other connectors, such as to receive data and / or power from a laptop computer, a tablet computer, a smartphone, or other devices.

[0056] Using their respective communication modules, one or more of computing devices 110 and / or 120 can be paired with wireless charger 140 for transmitting and / or receiving data from each other. For example, computing devices 110 and / or 120 can enter within a predetermined distance of wireless charger 140, and can communicate via a nearby The wireless charger 140 is discovered. Thus, the computing device 110 and / or 120, or the wireless charger 140 can initiate pairing. Before pairing, the computing device 110 and / or 120 or the wireless charger 140 can request user authentication. In some cases, pairing may require an authentication process. For example, pairing may require two-way authentication, where the user must authenticate the pairing on both of the two devices to be paired (e.g., on both the computing device 110 and the wireless charger 140).

[0057] The communication modules 119 and 129 can be configured to measure the signal strength of the wireless connection. For example, the communication modules 119 and 129 can be configured to measure the received signal strength (RSS) of the connection. In some cases, the communication modules 119 and 129 can be configured to receive the measured RSS from another device, such as from the wireless charger 140.

[0058] The computing devices 110 and 120 can each include one or more internal clocks. The internal clocks can provide timing information, which can be used for time measurement of applications and other programs running on the computing devices, as well as for the basic operations of the computing devices, sensors, input / output, GPS, communication systems, etc.

[0059] Further as Figure 1 and Figure 2 shown, the wireless charger 140 can be configured to charge one or more devices without the need for a wired connection. In this regard, the wireless charger 140 can include one or more charging systems, such as the charging system 147. The charging system 147 can be configured to provide wireless charging in any of a variety of ways, such as by inductive charging. For example, the charging system 147 can include one or more transmitter coils for transmitting electromagnetic energy to one or more receiver coils. In some cases, the charging system 147 can be configured for wireless charging according to a standard (such as the Qi standard, the Power Matters Alliance (PMA) standard, etc.). In other cases, the charging system 147 can additionally or alternatively be configured for wireless charging according to a non-standard protocol (such as a proprietary protocol). In some cases, the charging system 147 can be configured to collect charging data of the device being charged by the wireless charger 140 (e.g., the computing device 110 or 120), and can send the charging data to another device, such as the device being charged.

[0060] As Figure 2As shown in the example, the wireless charger 140 can be configured with a surface on which a wireless charging device such as computing device 110 or 120 can be placed for wireless charging. For example, the wireless charger 140 can have a flat top surface similar to the top surface of a table. In this regard, the transmitter coil in the wireless charger 140 can be configured in a plane parallel to the top surface. The wireless charger 140 can be configured to accommodate wireless charging devices of any of a variety of shapes and sizes. The wireless charger 140 is alternatively or additionally configured with other features, such as having an inclined surface on which a wireless charging device can be placed, a bracket or adjustable bracket for holding the wireless charging device, a recess in which the wireless charging device can be placed, etc.

[0061] The wireless charger 140 can be configured similarly to the computing devices 110, 120, or 130, having some or all of the components typically associated with computing devices, such as one or more processors, a memory for storing data and instructions (e.g., RAM and an internal hard drive), input and / or output devices, sensors, a communication module, a charging system, a clock, etc. In other cases, the wireless charger 140 can include one or more charging systems 147 without any of the components typically associated with computing devices.

[0062] Similar to the memory 114, the storage system 150 can be any type of computerized storage capable of storing information that can be accessed by one or more of the computing devices 110, 120, 130, and / or the wireless charger 140, such as a hard drive, a memory card, ROM, RAM, a DVD, a CD-ROM, writeable and read-only memories. Additionally, the storage system 150 can include a distributed storage system in which data is stored on multiple different storage devices that can be physically located in the same or different geographical locations. The storage system 150 can be connected to the computing devices via a network 160 as Figure 1 shown or can be directly connected to any one of the computing devices 110, 120, 130, and / or the wireless charger 140 (not shown).

[0063] Example methods

[0064] In addition to the example systems described above, example methods are now described. Such methods can be performed using any of the above systems, their modifications, or a variety of systems with different configurations. It should be understood that the operations involved in the following methods need not be performed in the exact order described. Instead, the various operations can be processed in a different order or simultaneously, and operations can be added or omitted.

[0065] For example, user 210 may decide to wirelessly charge computing device 110 using wireless charger 140. Thus, user 210 may place computing device 110 on the surface of wireless charger 140, such as the top surface of wireless charger 140. User 210 may need to position computing device 110 on wireless charger 140 such that energy can be inductively transferred from the charging system 147 of the wireless charger to the charging system 117 of computing device 110.

[0066] Figure 3 An example of a computing device and a wireless charger in accordance with aspects of the present disclosure is illustrated. Referring Figure 3 to the example in, wireless charger 140 includes a transmitter coil 147A configured to emit electromagnetic energy. For example, transmitter coil 147A may be part of the charging system 147 of wireless charger 140. Further as Figure 3 shown, computing device 110 includes a receiver coil 117A configured to receive electromagnetic energy. For example, receiver coil 117A may be part of the charging system 117 of computing device 110. Thus, when computing device 110 is near wireless charger 140, such as within a predetermined distance, transmitter coil 147A may electromagnetically engage receiver coil 117A such that energy can be inductively transferred from transmitter coil 147A to receiver coil 117A.

[0067] Electromagnetic energy may be transferred at different rates depending on the distance between transmitter coil 147A and receiver coil 117A. For example, electromagnetic energy may be transferred at a higher rate when receiver coil 117A is placed near transmitter coil 147A compared to when receiver coil 117A is placed farther away from transmitter coil 147A. Thus, the charging rate using wireless charger 140 may be represented by pattern 310. For example, pattern 310 may represent contour lines of the charging rate. For example, the charging rate may be highest when the center 320 of transmitter coil 147A is precisely aligned with the center 330 of receiver coil 117A and decreases as the center 330 of receiver coil 117A moves away from the center 320 of transmitter coil 147A. As shown, pattern 310 includes a series of concentric rings, where each concentric ring may be a predetermined distance from the center 320 of transmitter coil 147A. In the case where wireless charger 140 is configured according to a standard such as the Qi standard, the charging rate at positions on each ring may be predetermined.

[0068] Although, for ease of illustration, pattern 310 is shown as two-dimensional, pattern 310 can also be three-dimensional, such as including a series of concentric spheres. It should be understood that pattern 310 can have a shape that depends on the geometry of transmitter coil 147A and / or receiver coil 147B, and need not be a series of concentric rings / spheres. Depending on the geometry of transmitter coil 147A and / or receiver coil 147B, the charging rate can be highest at points that do not correspond to the alignment of the geometric centers of transmitter coil 147A and receiver coil 147B. Additionally, in the case where charger 140 is not configured according to a standard configuration, the charging rate pattern of charger 140 can be estimated based on the monitored movement of computing device 110 and the charging rate generated by that movement.

[0069] To increase the charging rate, user 210 can align receiver coil 117A with transmitter coil 147A. In some cases, the position of transmitter coil 147A can be marked on the surface of wireless charger 140 to facilitate alignment. For example, the center 320 of transmitter coil 147A can be marked on the top surface of wireless charger 140. Thus, user 210 can attempt to position computing device 110 as close as possible to the marked center 320 of transmitter coil 147A. However, the position of receiver coil 117A may not be similarly marked on computing device 110. Thus, user 210 may not be able to effectively align the two coils. For example, user 210 can attempt to align the center of computing device 110 with the marked center 320 of transmitter coil 147A, but as shown, the center 330 of receiver coil 117A does not lie at the center 110 of the computing device. Additionally, even if the position of receiver coil 117A is also marked on the surface of computing device 110, user 210 may not easily align the two marks because computing device 110 may block the view of the mark on the surface of wireless charger 140 while the computing device 110 is placed on wireless charger 140.

[0070] Moreover, aligning a computing device with a larger form factor, such as a laptop or a tablet computer, can be particularly difficult. For example, in the case where the position of receiver coil 117A is not marked, receiver coil 117A can be located anywhere within a relatively large space (e.g., several tens of centimeters) inside computing device 110. In contrast, for a computing device with a smaller form factor, such as a mobile phone, the position of the receiver coil will be restricted to a smaller space (e.g., several centimeters). For another example, when a user places a computing device on a wireless charger, a larger computing device may block the user's view more. Thus, computing device 110 can be configured to assist user 210 during the charging alignment process.

[0071] Figure 4Illustrated is an example of aligning a computing device with a wireless charger using motion data in accordance with aspects of the present disclosure. The motion data includes information associated with the motion of the computing device (including portions thereof) in space. For example, the motion data of the motion can include one or more vectors associated with the angle and speed of the motion, and the one or more vectors can include a series of 3D coordinates associated with the position of the computing device or a portion of the computing device at different times. In this regard, the motion data of the computing device 110 can be collected by one or more sensors of the computing device 110, such as one or more sensors in the IMU. Referring Figure 4 , the one or more sensors can include an accelerometer 115A of the computing device 110, such as a triaxial accelerometer that can measure acceleration in three-dimensional space.

[0072] In the case where the user consents to use such data, sensor data from the computing device 110 can be used to determine the charging alignment between the computing device 110 and the wireless charger 140. For example, the computing device 110 can enter within a predetermined distance of the wireless charger 140 and detect electromagnetic energy emitted from the wireless charger 140. Accordingly, the computing device 110 can determine that the user 210 will attempt to use the wireless charger 140 to charge the computing device 110 and can assist the user 210 in the charging alignment. For example, the computing device 110 can display a prompt asking the user 210 whether the sensor data can be used for charging alignment. In some cases, the computing device 110 can allow the user 210 to select the type of data that the user authorizes to be used in the charging alignment of the computing device 110. Alternatively or additionally, the user 210 may have preconfigured authorization settings in the computing device 110 to allow the use of sensor data for charging alignment.

[0073] The processor 112 can then receive sensor data from its sensors. The received sensor data can include motion data detected by one or more sensors of the computing device 110, such as inertial measurements. In Figure 4 the example shown, the sensor data includes inertial measurements from the accelerometer 115A. For example, the accelerometer 115A can measure the acceleration of the computing device 110 relative to three axes in three-dimensional space. For example and as Figure 4 shown, two axes x and y can correspond to two directions in the plane of the surface of the computing device 110 (e.g., the bottom surface of the housing of a laptop computer), and one axis z can correspond to the direction perpendicular to the surface of the computing device 110. In other examples, the axes x, y, and z can be some other axes sufficient to define three-dimensional space.

[0074] Processor 112 may receive a time-based series of acceleration measurements from accelerometer 115A, such as [t1; a_x1, a_y1, a_z1], [t2; a_x2, a_y2, a_z2],..., [tn; a_xn, a_yn, a_zn]. For example, each acceleration measurement may be associated with a timestamp provided by the internal clock of computing device 110. For example, at time t1, a_x1 may be the value of the acceleration along the x-axis in the plane of the bottom surface of the laptop housing, a_y1 may be the value of the acceleration along the y-axis in the plane of the bottom surface of the laptop housing, and a_z1 may be the value of the acceleration along the z-axis perpendicular to the bottom surface of the laptop housing. Thus, the acceleration measurements in the time-based sequence may be vectors.

[0075] Based on the acceleration measurements received from accelerometer 115A, processor 112 may generate additional motion data. As an example, a time-based velocity sequence may be generated based on the time-based sequence of acceleration measurements, such as by integrating the time-based sequence of acceleration measurements with respect to time. For another example, processor 112 may determine a time-based displacement sequence based on the time-based sequence of acceleration measurements. For example, processor 112 may perform a double integral of the time-based sequence of acceleration measurements with respect to time. Since the acceleration measurements include direction information, the integration with respect to time may be performed separately for each direction. Thus, the time-based displacement sequence may be [t1; x1, y1, z1], [t2; x2, y2, z2],..., [tn1; xn, yn, zn]. The displacements in the time-based sequence, like the acceleration measurements, may also be vectors. Figure 4 Example displacement vectors 410, 420, 430, 440 are shown, which are connected to represent a continuous movement. Each displacement vector represents a movement from a previous position of computing device 110 to a new position of computing device 110.

[0076] Processor 112 may also receive charging data of computing device 110. For example, the charging data may include data related to the energy storage state or data related to the energy transfer state. For example, the charging data may include the state of a battery or other type of energy storage, such as the charge amount in the battery. For another example, the charging data may include the amount or rate of energy transfer between two devices. As described above regarding the example system, charging system 117 may be configured to collect the charging data of computing device 110. For example, charging system 117 may measure the energy received per unit time or the charging rate (e.g., in W or J / s) as the charging data. Each measurement of the charging data may be associated with a timestamp, for example, the timestamp may be provided by the clock of computing device 110. Thus, processor 112 may receive a sequence of time-based charging rate measurements from charging system 117, such as [t1'; R1], [t2'; R2]..., [tn'; Rn].

[0077] In the case where charging system 117 does not directly measure the charging rate as the charging data, processor 112 may determine the charging rate based on other charging data. For example, charging system 117 may measure the total charge amount stored in the battery of charging system 117 (e.g., in J). Each measurement may be provided with a timestamp, for example, by the clock of computing device 110. Processor 112 may then determine the charging rate by finding the difference between the total charge amounts stored in the battery at two different timestamps and dividing the difference by the duration between the two timestamps. In other examples, processor 112 may receive charging data from wireless charger 140.

[0078] Based on the motion data and the charging data, processor 112 may determine the charging alignment. In this regard, processor 112 may determine a reference vector associated with at least two charging rates. For example, processor 112 may match each charging rate with the inertial measurement having the timestamp closest in time. In the case where the charging rate and the inertial measurement are measured at the same frequency, each displacement vector in the time-based sequence may be matched with the charging rate measurement. For example, displacement vector 410 [t1; x1, y1, z1] may be matched with [t1'; R1], and the displacement vector 420 of [t2; x2, y2, z2] at the next moment may be matched with [t2'; R2], etc. Thus, displacement vector 410 [t1; x1, y1, z1] may be determined as the reference vector associated with charging rates R1 and R2. In other words, during the movement represented by the reference vector [t1; x1, y1, z1], the charging rate changes from R1 to R2.

[0079] In the case where the charging rate is measured at a frequency lower than the inertial measurement, two or more consecutive displacement vectors can be combined into a reference vector such that the reference vector can be associated with at least two charging rates. For example, if the charging rate is measured at half the inertial measurement frequency, the displacement vector 410[t1; x1, y1, z1] can be combined with the displacement vector 420[t2; x2, y2, z2] into a vector [t1; x2 - x1, y2 - y1, z2 - z1] and matched with [t1'; R1], and the displacement vector 430[t3; x3, y3, z3] can be combined with the displacement vector 440[t4; x4, y4, z4] into a vector [t3; x4 - x3, y4 - y3, z4 - z3] and matched with [t2'; R2], etc. Thus, the vector [t1; x2 - x1, y2 - y1, z2 - z1] can be determined as a reference vector associated with the charging rates R1 and R2.

[0080] In the case where the charging rate is measured at a frequency higher than the inertial measurement, more than one charging rate can be matched with each displacement vector. For example, if the charging rate is measured at twice the inertial measurement frequency, the displacement vector [t1; x1, y1, z1] can be matched with [t1A'; R1A] and [t1B'; R1B], and the displacement vector [t2; x2, y2, z2] can be matched with [t2A'; R2A] and [t2B'; R2B]. Thus, the displacement vector [t1; x1, y1, z1] can be determined as a reference vector associated with the charging rates R1A, R1B, R2A.

[0081] Based on the reference vector and the associated charging rate, the processor 112 can determine an alignment vector between the computing device 110 and the wireless charger 140. Figure 5 An example of determining an alignment vector in accordance with aspects of the present disclosure is illustrated. Figure 5 Illustrated is the transmitter coil 147A of the wireless charger 140, the center 320 of the transmitter coil 147A, and a pattern 310 representing the charging rate at a plurality of predetermined distances from the center 320. Figure 5 Further illustrated is the receiver coil 117A of the computing device 110, the center 330 of the receiver coil 117A, and a reference vector 510 associated with two charging rates R1 and R2. For example, the reference vector 510 and the associated charging rate can be determined as described above.

[0082] At this point, the processor 112 can determine the position of the reference vector 510 within the pattern 310. For example, the processor 112 can determine that during the movement represented by the reference vector 510, the charging rate has increased from 6W to 8W. Accordingly, the processor 112 can determine two loops / contour lines within the pattern 310 corresponding to the 6W and 8W charging rates, respectively. The processor 112 can then identify a first point 520 on the 6W loop and a second point 530 on the 8W loop such that the vector starting from the first point 520 and ending at the second point 530 will be the same as the reference vector 510. In cases where one or more associated charging rates do not correspond to a predefined loop of the pattern 310, the position of the reference vector 510 within the pattern 310 can be interpolated. For example, if R1 is 6.5W, the processor 112 can estimate that the first point 520 is approximately midway between the 6W loop and the 7W loop. For another example, if the 6W loop and the 7W loop are 1 cm (centimeter) apart, the processor 112 can interpolate that the first point 520 is approximately 5 mm (millimeter) from the 6W loop and 5 mm from the 7W loop.

[0083] Based on the position of the reference vector 510 within the pattern 310, the processor 112 can determine an alignment vector between the center 330 of the receiver coil 117A and the center 320 of the transmitter coil 147A. As Figure 5 shown, there is only one vector that connects any point of the pattern 310 to the center 320 of the transmitter coil 147A. Accordingly, since the second point 530 corresponds to the center 330 of the receiver coil 117A at the end of the movement represented by the reference vector 510, the processor 112 can determine the vector 147A that connects the second point 530 to the center 320 of the transmitter coil as the alignment vector 540.

[0084] In the above example, the determination of the alignment vector assumes that the axes of the coordinate system of computing device 110 (shown as x, y, z) are substantially parallel to the axes of the coordinate system of wireless charger 140 (shown as x', y', z'). In other words, the above example assumes that computing device 110 is being placed on wireless charger 140 such that the bottom surface of the housing of computing device 110 remains substantially parallel to the top surface of wireless charger 140. Such an assumption may not always be correct. Additionally, the acceleration measurements from the accelerometer can be total acceleration values that do not distinguish between linear acceleration and angular acceleration. Thus, it may be difficult to determine whether computing device 110 is linearly moving, rotating, or some combination of both based solely on the acceleration measurements. Further, although in this example both charger 140 and computing device 110 are shown as having flat surfaces and their respective coils are positioned in parallel planes as surfaces, in other cases, charger 140 and / or computing device 110 may not have flat surfaces, or their respective charging coils may not be parallel to the outer surfaces of charger 140 and / or computing device 110. In such cases, determining the orientation of computing device 110 and / or differentiating between the linear and angular motion of computing device 110 may be used to determine whether transmitter coil 147A and receiver coil 117A are in parallel planes.

[0085] Thus, in other cases, to further improve the accuracy of the alignment vector, processor 112 can additionally determine the orientation of computing device 110 relative to wireless charger 140 and further determine the alignment vector based on the orientation of computing device 110. Figure 6 An example of determining the orientation of a computing device relative to a wireless charger in accordance with aspects of the present disclosure is shown. Figure 6 Also shown are computing device 110 and wireless charger 140, and their respective receiver coil 117A and transmitter coil 147A. In Figure 6 one or more sensors can include gyroscope 115B of computing device 110, such as a three-axis gyroscope that can measure the roll (α-axis), pitch (β-axis), and yaw (γ-axis) and / or angular velocity of computing device 110. In other words, the rotational measurements provide orientation information of computing device 110 relative to its three rotational axes. Processor 112 can receive the rotational measurements as a time-based series of rotational measurements, where each rotational measurement can be associated with a timestamp provided by the clock of computing device 110.

[0086] The processor 112 can use the received rotation measurements to determine the orientation of the computing device 110 relative to the wireless charger 140. For example, as shown in the figure, the processor 112 can determine that the computing device 110 has a pitch angle of β1 relative to the β axis. Further as shown in the figure, the rotation axes can be selected to correspond to the x-axis, y-axis, and z-axis of the computing device 110 such that the α axis corresponds to the x-axis, the β axis corresponds to the y-axis, and the γ axis corresponds to the z-axis. For a wireless charger with a flat top surface, such as the wireless charger 140 shown, the processor 112 can then determine that due to the pitch angle β1, the x-axis of the computing device 110 is offset by an angle β1 relative to the x'-axis of the wireless charger 140, and the z-axis of the computing device 110 is offset by an angle β1 relative to the z'-axis of the wireless charger 140.

[0087] In this way, the processor 112 can first transform the reference vector into values corresponding to the coordinate system of the wireless charger 140 before determining the alignment vector. For example, returning to the reference Figure 5 , the processor 112 can first transform the reference vector 510 from [t1; x1, y1, z1] relative to the x, y, z axes to [t1; x1', y1', z1'] relative to the x', y', z axes. The processor 112 can then determine the position of the transformed reference vector within the pattern 310. Based on the position of the transformed reference vector, an alignment vector that takes into account the orientation information of the computing device 110 can be determined.

[0088] In addition, the processor 112 can determine a change in the orientation of the computing device 110 based on the rotation measurements. Continuing with the above example, if at time t2 the pitch angle changes from β1 to β2, then the processor 112 can determine that the computing device 110 is rotating about its own axis relative to the β axis. In some cases, the processor 112 can correlate acceleration measurements and rotation measurements in order to separate linear motion from angular motion. For example, the processor 112 can determine values including linear displacement, linear velocity, linear acceleration, angular rotation, angular velocity, angular acceleration, etc., which the processor 112 can use in the determination of the alignment vector.

[0089] Returning to the reference Figure 4, once the alignment vector is determined, the processor 112 can generate an output that guides the movement of the computing device 110 to align with the wireless charger 140. As shown, an example output 450 can be a graphical representation of the relative positions of the center 320 of the transmitter coil 147A and the center 330 of the receiver coil 117A, as well as the alignment vector 540. Further as shown, the output 450 can include text instructing the user 210 to move the computing device 110 in the direction of the alignment vector 540. Although not shown, the output 450 can also include text or graphics instructing the user 210 to rotate the computing device 110 such that the receiver coil 117A is in a parallel plane with the transmitter coil 147A. In other examples, such as those further described below, the output can additionally or alternatively include other graphics and / or text, as well as other types of outputs, such as audio, tactile, etc.

[0090] The processor 112 can continue to monitor the relative positions of the transmitter coil 147A and the receiver coil 117A, and continue to generate instructions until the correct charging alignment is achieved. For example, based on the instructions in the output 450, the user 210 can move the computing device 110, and the processor 112 can continue to receive motion data and charging data. Based on the motion data and the charging data, the processor 112 can determine whether the appropriate charging alignment has been achieved, such as whether the charging alignment meets a predetermined threshold. An example predetermined threshold can be to meet 90% of the maximum charging rate possible for a given wireless charger. Another example predetermined threshold can be to have an offset of less than 1 cm between the center 320 of the transmitter coil 147A and the center 330 of the receiver coil 117A. In the case where the processor 112 determines that the user's movement has not correctly aligned the two coils, the processor 112 can determine a new alignment vector and generate a new output according to the same example process as described above.

[0091] As an alternative or supplement to determining the alignment vector and outputting guidance for the movement of the computing device 110 based on that alignment vector, the processor 112 can determine whether the user's most recent direction of movement has caused an increase or a decrease in the charging rate, and generate an output based on that determination. For example, the processor 112 can determine that at the end of the most recent movement represented by the displacement vector [t1; x1, y1, z1], the charging rate changes from R1 to R2. The processor 112 can compare R1 with R2, and can generate an output indicating that the user should continue to move in that direction or in the opposite direction. For example, if R2 is greater than R1, the processor 112 can generate an output indicating that the user should continue to move in the same direction. For another example, if R2 is less than R1, the processor can generate an output indicating that the user should move in the opposite direction.

[0092] The processor 112 may repeat this process until the alignment between the transmitter coil 147A and the receiver coil 117A meets a predetermined threshold. Thus, the processor 112 may assist the user in achieving the correct alignment in a manner similar to the "hotter - colder" game. As described above, such a process may be particularly useful when the charger 140 and / or the computing device 110 are not designed for wireless charging according to a standard. Additionally, since subsequent movements of the computing device 110 by the user based on instructions are unlikely to be in exactly the same or exactly opposite directions as the alignment vector, subsequent determinations by the processor 112 may fine - tune the alignment. For example, based on the movement of the computing device 110 that increases the charging rate in the direction along the x - axis, the processor 112 may generate an output indicating that the user should continue to move in the direction along the x - axis. However, when the user 210 continues to move the computing device 110 in the direction along the x - axis, the user 210 may also inadvertently move the computing device 110 slightly in the direction along the y - axis, which reduces the charging rate. Based on this, the processor 112 may generate the following output, which indicates that the user 210 should continue to move in the same direction along the x - axis but also move in the opposite direction along the y - axis.

[0093] On the other hand, in the case where the user consents to the use of such data, past motion data can be used to train one or more models to further assist in charging alignment. The model can be any type of machine - learning model. For example, the model can be a neural network or a decision - tree model. For another example, the model can be a regression model or a classifier model. For example, when the computing device 110 is placed on a surface, the processor 112 can receive past motion data that captures the movement of the computing device 110. The past motion data can be used to train the model in an unsupervised manner, such as using the past motion data as training input and having no training output. Alternatively, the model can be trained in a supervised or semi - supervised manner. For example, the past motion data can be used as training input, and patterns and / or vectors determined and / or verified by humans can be used as training output. In the case where the processor 112 receives other types of data in addition to the motion data, such additional data can be used to further train the model.

[0094] For example, the processor 112 may train a model to predict movement when the computing device 110 is placed on a wireless charger. For example, the model may be trained to recognize that the user 210 tends to place the computing device 110 downwards and move it in a certain direction, such as from left to right. For another example, the model may be trained to predict that if the user 210 starts to place the computing device 110 in a direction away from the user 210, the user 210 will ultimately place the computing device 110 at least a certain distance away from the user 210. For yet another example, the model may be trained to predict one or more vectors that represent the predicted movement of the computing device 110 when the computing device 110 is being placed on a surface (such as on the surface of a wireless charger).

[0095] The trained model may be stored on the computing device 110, such as in the memory 114, such that the processor 112 can access the trained model when determining charging alignment. For example, before the user 210 starts to place the computing device 110 down, the processor 112 may use the trained model to predict that the user 210 will place the computing device 110 approximately 10 cm to the left of the user 210. Accordingly, the processor 112 may generate an output indicating that the user 210 should position the computing device 110 based on this prediction. In this way, the processor 112 can assist the user 210 before receiving all of the motion data and charging data required to determine the alignment vector. For another example, when the user 210 places the computing device 110 down, the processor may use the trained model to predict the movement vector of the computing device 110 and may determine the alignment vector based on the predicted movement vector.

[0096] Figure 7 Another example of determining charging alignment based on motion data and charging data in accordance with aspects of the present disclosure is shown. In this example shown, the wireless charger 740 includes multiple transmitter coils, labeled 750A and 750B, but may be otherwise configured similar to the wireless charger 140. Each of the two transmitter coils 750A and 750B has their respective centers, labeled 760A and 760B. Additionally, in this example, the user 210 is shown aligning the computing device 120, which is a mobile phone, with the wireless charger 740. The computing device 120 in this example does not have wireless charging capabilities. Instead, the wireless charging capabilities are provided by an accessory, shown as a lid 710 in which the computing device 120 is fitted. For example, the lid 710 may include a receiver coil 720 having a center 730 and may have conductive elements, such as wires, contacts, or dongles, for transferring energy from the receiver coil 720 to the computing device 120.

[0097] Although the receiver coil 720 is located in the lid 710 rather than in the computing device 120, the processor 122 of the computing device 120 can also determine the alignment between the receiver coil 720 of the lid 710 and the transmitter coil 750A or 750B in accordance with the same process as described with respect to Figure 4 、 Figure 5 and Figure 6 as long as the lid 710 is attached to the computing device 120 such that the receiver coil 720 moves with the computing device 120. For example, the processor 122 can receive motion data from one or more sensors (such as the accelerometer 125A and the gyroscope 125B), and can receive charging data from the charging system 127. For example, based on the motion data and the charging data as described above, one or more reference vectors and alignment vectors can be determined.

[0098] Figure 7 It is further illustrated that the processor 122 can additionally or alternatively use other types of sensor data to determine the alignment between the receiver coil 720 of the lid 710 and the transmitter coil 750A or 750B. For example, the one or more sensors 125 of the computing device 120 can include additional sensors. For example, the additional sensors can be one or more visual sensors, such as the camera 125C. Alternatively or additionally, the one or more sensors 125 can also include optical sensors, such as infrared sensors. In this regard, the processor 122 can receive image data from the camera 125C that captures the wireless charger 740. For example, the received image data can be a time-based series of image data, such as a series of frames or images, each frame or image being associated with a timestamp provided by the clock of the computing device 120.

[0099] The processor 122 can use the image data to generate an output that guides the movement of the computing device 120. For example, the processor 122 can use a pattern or object recognition model, such as a machine learning model, to identify the wireless charger 740 in the received image data. The processor 122 can then determine the relative position of the computing device 120 and the wireless charger 740 based on the wireless charger 740 identified in the image data. When the computing device 110 is moved by the user 210 based on the image data, the processor 122 can also track the relative position of the computing device 120 and the wireless charger 740.

[0100] Thus, the processor 122 can generate an output that guides the movement of the computing device 120 based on the relative position determined using the image data. For example, the output can be generated in a manner similar to the above-described "hotter-colder" game. For example, the processor 122 can indicate to the user 210 to move the computing device 120 in a particular direction based on the relative position determined using the image data. The processor 122 can then determine, based on the image data, whether the computing device 120 is moving closer to the wireless charger 740 and generate further instructions accordingly until a predetermined threshold is met.

[0101] Generating output instructions using image data can be advantageous in many ways. For example, the processor 122 may be able to identify and locate the wireless charger 740 based on the image data, even when the computing device 120 is at a distance where the receiver coil 720 is not close enough to be engaged by either of the transmitter coils 750A or 750B. Thus, the processor 122 can generate an output that guides the movement of the computing device 120 toward the wireless charger 740 even before the processor 122 can determine alignment based on the charging data.

[0102] For another example, the processor 122 can further identify, based on the image data, that the wireless charger 740 includes two transmitter coils 750A and 750B, which is not possible to achieve based on detecting the emitted electromagnetic energy. For example, markings can be provided on the top surface of the wireless charger 740 to indicate the positions of the transmitter coils 750A and 750B. For another example, markings can be provided on the top surface of the wireless charger 740 to indicate the positions of the centers 760A and 760B. Thus, the processor 122 can determine, based on the image data, that the transmitter coil 750A is closer to the computing device 120 than the transmitter coil 750B. Based on the relative positions of the two transmitter coils 750A and 750B, the processor 122 can generate an output indicating to the user to move toward the transmitter coil 750A identified as being closest to the computing device 120.

[0103] In the case where the user consents to use such data, Figure 7 It is further illustrated that the computing device 120 can use signal strength measurements in determining charging alignment. In this regard, signal strength measurements are likely to have been used by the computing device 120 and / or the wireless charger 740 to establish and / or maintain a connection. For example, the communication module 129 of the computing device 120 can measure the signal strength of the communication link 780 between the computing device 120 and the wireless charger 740. For example, the signal strength can be used for Connected RSS measurements. Each signal strength measurement can be associated with a timestamp provided by the clock of computing device 120, and thus, processor 122 can receive a time-based series of signal strength measurements from communication module 129. Alternatively or additionally, the signal strength can be measured by communication module 770 of wireless charger 740 and sent to processor 122.

[0104] For example, processor 122 can determine the distance between computing device 120 and wireless charger 740 based on the signal strength measurements. Similar to the image data, when computing device 120 is at a distance where receiver coil 720 is not sufficient to be engaged by either transmitter coil 750A or 750B, processor 122 may be able to identify and locate wireless charger 740 based on the signal strength measurements. For example, for many communication systems, such as the signal strength may decrease as the distance between the two devices increases. For example, from the signal strength pattern of a device can be represented by a series of concentric rings, where each ring is a predetermined distance from the device and each ring has a known signal strength value. Thus, based on the value of the signal strength measurement, processor 122 can determine the distance between computing device 120 and wireless charger 740.

[0105] In this way, processor 122 can generate an output that guides the movement of computing device 120 based on the distance determined using the signal strength measurements, for example, in a manner similar to the above-described "hotter - colder" game. For example, processor 122 can instruct user 210 to move computing device 120 by the distance determined using the signal strength measurements. Processor 122 can then determine whether computing device 120 is approaching wireless charger 740 based on the signal strength measurements and generate further instructions accordingly until a predetermined threshold is met.

[0106] Although in the above examples, determining charging alignment using motion data and charging data, image data, and signal measurements are described separately, any of the various combinations of the above types of data can be used to determine charging alignment. For example, motion data, charging data, and image data can be used in combination when determining the alignment vector. Additionally, although Figures 4 to 7 the examples describe some types of sensor data, other types of data can be used additionally or alternatively.

[0107] Furthermore, Figure 8 other examples of outputs that can be used to guide the movement of a computing device to a wireless charger according to aspects of the present disclosure are shown. Figure 8 Also shown is computing device 120 and lid 710 having the various features described above with respect to Figure 7 and having the various features described above with respect toFigure 7 A wireless charger 740 with the described various features. Figure 8 It is further shown that the computing device 120 includes a display 123A as an output device, as well as other output devices, such as a haptic interface 123B and a speaker 123C. As shown, the display 123A can show a graphical representation 810, as shown in this example, and the graphical representation 810 can be an arrow in the direction of the alignment vector. Further as shown, the haptic interface 123B can generate a haptic output 820, such as a vibration, in the direction of the alignment vector. For another example, the speaker 123C can generate an audio output 830, such as an audio indicating to the user to slow down or move the computing device 120 in the direction of the alignment vector as shown in this example.

[0108] Any one of the multiple output devices can be used to generate any one of the multiple outputs for guiding the movement of the computing device during charging alignment. For example, in Figures 4 to 6 the example where the computing device 110 is relatively large, a visual display may be advantageous because the display 113A of the computing device 110 may be easy to view and follow. For another example, in Figure 7 and Figure 8 the example where the computing device 120 is relatively small, haptic output and audio output may be advantageous because the display 123A may be small and the user 210 may be more sensitive to haptic output from a smaller handheld computing device.

[0109] Although in the above description, the processor 112 of computing device 110 (or the processor 122 of computing device 120) may receive data and make various determinations for charging alignment, alternatively, a processor remote from computing device 110 (or computing device 120) may also be configured to receive data and make determinations. For example, the processor 132 of server computing device 130 may receive sensor data from computing device 110, such as motion data and charging data. The processor 132 may then determine a reference vector, an associated charging rate, an alignment vector, etc., as described above. The processor 132 may also generate an output for indicating to the user and send the output to computing device 110 such that computing device 110 may display the output to the user. For another example, the above model may be trained on server computing device 130. The processor 132 may receive past motion data from computing device 110 and store it in memory 136. Then the past motion data may be used by the processor 132 to train a model for predicting the user's motion when computing device 110 is placed down. The trained model may then be stored in memory 134 for use by the processor 132 later when predicting the user's motion. In some cases, computing device 130 may send the trained model to computing device 110 for predicting the user's motion.

[0110] Figure 9 An example flowchart that may be executed by one or more processors (such as one or more processors 112 of computing device 110) is shown. For example, the processor 112 of computing device 110 may receive data and make various determinations as shown in the flowchart. For another example, the processor 132 of server computing device 130 may receive data and make various determinations as shown in the flowchart. Refer to Figure 9 , in block 910, motion data may be received from one or more sensors of a computing device, the motion data indicating the motion of the computing device. In block 920, charging data related to the energy storage state of the computing device or the energy transfer state between the wireless charger and the computing device may be received. In block 930, based on the motion data and the charging data, a reference vector associated with at least two charging rates may be determined, each charging rate corresponding to the amount of energy transferred per unit time between the wireless charger and the computing device. In block 940, based on the reference vector and the associated charging rates, an alignment vector between the computing device and the wireless charger may be determined. In block 950, based on the alignment vector, an output may be generated to guide the movement of the computing device to align with the wireless charger.

[0111] This technology is advantageous because it allows the system to assist the user in accurately aligning the computing device with the wireless charger. With better alignment, a higher charging rate can be achieved, making the charging process more energy efficient. The system can determine the charging alignment of the computing device with a wireless charger of any of a variety of shapes or sizes. Additionally, the system can determine the charging alignment even when the wireless charging capability is provided by an accessory (such as a cover or a stand) of the computing device. This technology also provides a trained model to predict the user's movement when placing the computing device for wireless charging, which can further improve the speed and accuracy of the alignment process.

[0112] Unless otherwise stated, the above alternative examples are not mutually exclusive, but can be implemented in various combinations to achieve unique advantages. Since these and other variations and combinations of the features discussed above can be utilized without departing from the subject matter defined by the claims, the foregoing description of the embodiments should be understood by way of illustration rather than limitation of the subject matter defined by the claims. Additionally, the provision of the embodiments described herein and the clauses expressed as "for example", "including", etc. should not be construed as limiting the subject matter of the claims to specific embodiments; rather, these examples are only intended to illustrate one of many possible embodiments. Additionally, the same reference numerals in different figures may identify the same or similar elements.

Claims

1. A method for determining wireless charging alignment, comprising: Receiving, by one or more processors, motion data from one or more sensors of a computing device, the motion data indicating motion of the computing device; Receiving, by the one or more processors, charging data related to an energy storage state of the computing device or an energy transfer state between a wireless charger and the computing device; Determining, by the one or more processors, an alignment vector between the computing device and the wireless charger, wherein the determination is at least partially based on motion data associated with at least two charging rates at different times during the motion, each charging rate corresponding to an energy transfer rate between the wireless charger and the computing device; And Generating, by the one or more processors, an output based on the alignment vector for guiding movement of the computing device to align with the wireless charger.

2. The method according to claim 1, wherein, The motion data includes acceleration measurements of the motion of the computing device.

3. The method according to claim 2, further comprising: Determining, by the one or more processors, a displacement of the computing device relative to a previous position of the computing device based on the acceleration measurements.

4. The method according to claim 1, wherein, The motion data includes rotation measurements of the motion of the computing device.

5. The method according to claim 4, further comprising: Determining, by the one or more processors, orientation information based on the rotation measurements, wherein the alignment vector is determined based on the orientation information.

6. The method according to claim 1, wherein, The alignment vector is a vector connecting a position of a charging system of the computing device to a position of a charging system of the wireless charger.

7. The method according to claim 1, wherein The alignment vector is a vector connecting a center of a receiver coil of the computing device to a center of a transmitter coil of the wireless charger.

8. The method according to claim 1, further comprising: Receiving, by the one or more processors, past motion data that captures motion of the computing device placed on a surface; Training, by the one or more processors, one or more models based on the past motion data, the one or more models for predicting a movement vector of the computing device when the computing device is placed on the surface.

9. The method according to claim 8, further comprising: Predicting, by the one or more processors, a movement vector of the computing device when the computing device is being placed on the wireless charger using the one or more models, wherein the alignment vector is further determined based on the predicted movement vector.

10. The method according to claim 1, wherein The output includes a graphical representation of a relative position between the computing device and the wireless charger and a graphical representation of the alignment vector.

11. The method according to claim 1, wherein, The output includes a haptic output in a direction of the alignment vector.

12. The method according to claim 1, wherein, The output includes audio instructions.

13. The method according to claim 1, further comprising: Receiving, by the one or more processors, image data from the one or more sensors; Identifying, by the one or more processors, the wireless charger based on the image data; The one or more processors determine a relative position between the wireless charger and the computing device based on the image data, wherein the alignment vector is further determined based on the relative position between the wireless charger and the computing device.

14. The method according to claim 1, further comprising: receiving, by the one or more processors, a signal strength measurement of a wireless connection between the wireless charger and the computing device; determining, by the one or more processors, a relative position between the wireless charger and the computing device based on the signal strength measurement, wherein the alignment vector is further determined based on the relative position between the wireless charger and the computing device.

15. The method according to any one of the preceding claims, further comprising: determining, by the one or more processors, that the wireless charger includes a plurality of charging systems; identifying, by the one or more processors, one of the plurality of charging systems that is closest to the computing device, wherein the alignment vector is determined for the identified charging system that is closest to the computing device.

16. A system for determining wireless charging alignment, comprising: one or more processors configured to: receive motion data from one or more sensors of a computing device, the motion data indicative of motion of the computing device; receive charging data related to an energy storage state of the computing device or an energy transfer state between the wireless charger and the computing device; determine an alignment vector between the computing device and the wireless charger, wherein the determination is at least partially based on motion data associated with at least two charging rates at different times during the motion, each charging rate corresponding to an energy transfer rate between the wireless charger and the computing device; and generate, based on the alignment vector, an output for guiding movement of the computing device to align with the wireless charger.

17. The system according to claim 16, further comprising: the one or more sensors, wherein the one or more sensors include at least one of the following: an accelerometer, a gyroscope, and an optical sensor.

18. The system according to claim 16, further comprising: a communication module configured to measure a signal strength of a connection between the computing device and the wireless charger; wherein the one or more processors are further configured to: receive a signal strength measurement of a connection between the computing device and the wireless charger; determine a relative position between the wireless charger and the computing device, wherein the alignment vector is further determined based on the relative position between the wireless charger and the computing device.

19. The system according to claim 16, further comprising: one or more output devices, wherein the one or more output devices include at least one of the following: a display, a haptic interface, and a speaker.

20. The system according to any one of claims 16 to 19, wherein, The one or more processors are further configured to: receive past motion data that captures motion of the computing device placed on a surface; Train one or more models based on the past motion data, the one or more models being used to predict the movement vector of the computing device when the computing device is placed on a surface.

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