Method, user equipment, apparatus and medium for sending information from user equipment to vehicle

Abstract

The invention discloses a method for sending information from user equipment to a vehicle. The method comprises the following steps: acquiring data containing information to be sent in an application running on the user equipment; in response to a received instruction of sending information to the vehicle from a user, identifying the information to be sent according to the acquired data by using the AI model; and sending the identified information to the vehicle for further processing. The invention also relates to user equipment, a device and a medium supporting sending information to a vehicle.

Description

Technical Field

[0001] This application relates to the field of vehicles, and more particularly to methods, user equipment, apparatus, and computer-readable media for transmitting information from user equipment to a vehicle. Background Technology

[0002] Today, users' demands for the digitalization and intelligence of vehicles are constantly increasing, and related new functions are continuously being developed and applied to vehicles. Many models now feature central control displays that integrate vehicle control, map navigation, and audio-visual entertainment. However, compared to mobile communication devices such as smartphones, central control displays remain relatively limited or cumbersome in terms of human-computer interaction, especially input. Furthermore, when using on-screen keyboards to input navigation destinations, neither the functionality of the vehicle's built-in input method nor the performance and responsiveness of the vehicle's system can compare to mobile devices. This situation leads to a decreased user experience, and even causes some users to prefer using navigation apps on their smartphones instead of the vehicle's built-in navigation app.

[0003] To address this issue, some manufacturers have introduced voice input functionality and more intelligent voice assistants into their vehicles. However, voice interaction still suffers from problems such as inaccurate voice recognition and the need for further confirmation from the user when multiple candidate results exist, making it less convenient than operating directly on a mobile device. Other manufacturers have developed "Send to My Car" features. For example, when navigation is needed, users can select a destination in a relevant app on their mobile device, and then the information is sent to the associated vehicle via a backend server. However, current "Send to My Car" functionality requires first identifying or extracting the information to be sent (e.g., the navigation destination). Traditional methods typically use regular expressions or specific algorithms to extract this information, which has limited applicability and is not accurate enough.

[0004] Therefore, in order to further improve the "Send to My Car" function and enhance the accuracy and flexibility of information recognition, it is desirable to provide an improved solution for sending information from the user's device to the vehicle. Summary of the Invention

[0005] This summary is provided to introduce, in a simplified form, some concepts that will be further described in the following detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to help determine the scope of the claimed subject matter.

[0006] To address the above problems, according to a first aspect of the present invention, a method for sending information from a user equipment to a vehicle is provided, the method comprising: acquiring data containing information to be sent in an application running on the user equipment; in response to receiving an instruction from a user to send information to a vehicle, identifying the information to be sent using an AI model based on the acquired data; and sending the identified information to the vehicle for further processing.

[0007] In the technical solution of this invention, by using an AI model (e.g., LLM) to identify the information to be sent from the data selected or uploaded by the user, the information identification can be more accurate, the applicable scenarios can be more flexible, and the user experience can be further improved.

[0008] According to one embodiment of the present invention, the information is navigation destination information.

[0009] According to a further embodiment of the present invention, the AI ​​model is a large language model (LLM) deployed locally on the user equipment or deployed on a remote server communicatively connected to the user equipment.

[0010] According to a further embodiment of the present invention, using the AI ​​model to identify the information to be sent based on the acquired data further includes: generating a prompt word for the AI ​​model based on the data; and feeding the prompt word into the AI ​​model to identify the information to be sent.

[0011] According to a further embodiment of the present invention, the data includes text data or image data.

[0012] According to a further embodiment of the present invention, in the case where the data is image data, the method further includes: extracting text information contained in the image data for processing by the AI ​​model when acquiring the image data.

[0013] According to a further embodiment of the present invention, the extraction operation is performed using optical character recognition (OCR).

[0014] According to a further embodiment of the present invention, the method further includes: in response to receiving an instruction from a user to send information to a vehicle, checking whether the user has permission to send information to the vehicle; and in response to the user having permission to send information to the vehicle, sending the identified information to the vehicle for further processing.

[0015] According to a further embodiment of the present invention, the method further includes: determining whether the identified information is valid when identifying the information to be sent; if it is valid information, sending the identified information to the vehicle for further processing; if it is invalid information, displaying a prompt on the user device that valid information could not be identified.

[0016] According to a second aspect of the present invention, an apparatus for sending information from a user equipment to a vehicle is provided, the apparatus comprising: an information acquisition module for acquiring data containing information to be sent in an application running on the user equipment; an information identification module for identifying the information to be sent based on the acquired data using an AI model in response to receiving an instruction from a user to send information to the vehicle; and an information sending module for sending the identified information to the vehicle for further processing.

[0017] According to one embodiment of the present invention, the information is navigation destination information.

[0018] According to a further embodiment of the present invention, the AI ​​model is a Large Language Model (LLM) deployed locally on the user equipment or deployed on a remote server communicatively connected to the user equipment, and the information recognition module is further configured to: generate prompt words for the AI ​​model based on the data; and send the prompt words to the AI ​​model to recognize the information to be sent.

[0019] According to a further embodiment of the present invention, the data includes text data or image data, and in the case where the data is image data, the information recognition module is further configured to: when acquiring the image data, extract the contained text information from the image data using optical character recognition (OCR) for processing by the AI ​​model.

[0020] According to a third aspect of the present invention, a user equipment is provided, comprising: a communication unit configured to communicatively connect to a remote server associated with a vehicle; a processor; and a memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform the method as described in any of the preceding aspects.

[0021] According to a fourth aspect of the invention, a computer-readable medium storing processor-executable instructions is provided, which can be executed by a processor to perform the method as described in any of the preceding aspects.

[0022] To address the problems existing in the prior art, the present invention provides an improved scheme for sending information from a user device to a vehicle. This scheme uses, for example, OCR to extract text information, generates prompt words based on the text information, and utilizes an AI large language model to identify the information to be sent, thereby making information recognition more accurate, applicable scenarios more flexible, and further improving the user experience.

[0023] These and other features and advantages will become apparent from the following detailed description and with reference to the accompanying drawings. It should be understood that the foregoing general description and the following detailed description are illustrative only and do not limit the scope of the claims. Attached Figure Description

[0024] To gain a more detailed understanding of the manner in which the features of this disclosure are described above, reference can be made to the various embodiments for a more specific description of the above-briefly summarized aspects, some of which are illustrated in the accompanying drawings. However, it should be noted that the drawings illustrate only certain typical aspects of this disclosure and should not be considered as limiting its scope, as this description may allow for other equivalent and effective aspects.

[0025] Figure 1 A schematic communication flow diagram for sending information from a user equipment to a vehicle according to an embodiment of the present disclosure is shown.

[0026] Figure 2 This diagram illustrates a scenario in which navigation destination information is sent to a vehicle based on any selected text, according to an embodiment of the present disclosure.

[0027] Figure 3 This diagram illustrates a scenario in which navigation destination information is sent to a vehicle based on a selected or uploaded image, according to an embodiment of the present disclosure.

[0028] Figure 4 This illustration shows another scenario in which navigation destination information is sent to a vehicle based on a selected or uploaded image, according to one embodiment of the present disclosure.

[0029] Figure 5 A schematic architecture diagram of an apparatus for sending information from a user equipment to a vehicle according to an embodiment of the present disclosure is shown.

[0030] Figure 6 An example computing device architecture diagram is shown, illustrating an example computing device that can implement the various technologies described herein, according to an embodiment of the present disclosure. Detailed Implementation

[0031] The following detailed description is sufficient to enable any person skilled in the art to understand the technical content of one or more embodiments of this specification and to implement them accordingly. Furthermore, based on the specification, claims, and drawings disclosed in this specification, those skilled in the art can easily understand the objectives and advantages associated with one or more embodiments of this specification.

[0032] Throughout this specification, the term "vehicle" refers to any type of motor vehicle, including but not limited to cars, vans, trucks, buses, etc. For simplicity, the invention is described in relation to "motor vehicle." The terms "A or B" as used in this specification mean "A and B" and "A or B," and do not imply that A and B are exclusive unless otherwise stated.

[0033] Throughout this specification, the term "user equipment" refers to any type of mobile communication device or mobile computing device, including smart wearable devices such as smartphones, tablets, laptops, smartwatches, or wristbands. For simplicity, the invention is described using "smartphone" as an example.

[0034] As mentioned above, existing "send to my car" services require first identifying or extracting the information content to be sent (e.g., navigation address). However, traditional methods typically use regular expressions or specific algorithms to extract the information content, which are limited in applicability and not accurate enough.

[0035] To address this issue, the embodiments in this specification envision employing AI large language models (e.g., ChatGPT) to extract information, thereby making information recognition more accurate, applicable scenarios more flexible, and further improving the user experience.

[0036] For example, see Figure 1 The diagram illustrates a schematic communication flow 100 for sending information from a user equipment to a vehicle via a remote server according to an embodiment of the present disclosure.

[0037] like Figure 1 As shown, in step S1, the user interacts with the smartphone to select or upload data containing information to be sent to the vehicle, and in step S2, the data is transmitted to the relevant application for further processing.

[0038] In one implementation, a user can select text or images to be recognized, or directly capture photos using the camera function, on a smartphone application (e.g., a general mobile application provided by a third-party entity (hereinafter referred to as "third-party application") or a dedicated mobile application provided by a vehicle manufacturer that provides vehicle-to-everything (V2X) related services (hereinafter referred to as "vehicle-to-everything application")) for subsequent recognition and transmission.

[0039] In one embodiment, the information sent to the vehicle may include navigation destination information, multimedia file information such as music or video, schedule information, contact information, etc. For ease of explanation, the following description will use navigation destination information as an example; however, those skilled in the art will understand that this invention is applicable to sending any type of information from a user device to a vehicle.

[0040] For example, users can directly select or capture images associated with the navigation destination in the navigation-related services of the vehicle connectivity application, and activate the service by clicking on relevant controls such as "Send to my vehicle" to further carry out the subsequent information recognition and sending process.

[0041] As another example, a user may browse content of interest in a third-party application and wish to send the navigation destination information contained therein to the vehicle. The user can select the text or image and open the relevant mini-program (which has a service to send information to the vehicle) in the sharing service of the third-party application to further carry out the subsequent information recognition and sending process.

[0042] Subsequently, in step S3, the selected or uploaded data (e.g., text or images) is forwarded to a remote server associated with the vehicle.

[0043] Optionally, after the data is submitted to the backend, if the data type is an image (e.g., a picture captured directly in the application using the camera function or a picture selected from the album), then in step S4, the text information contained in the image data can be extracted for processing by the AI ​​model.

[0044] Specifically, text recognition services provided by optical character recognition (OCR) providers can be used, for example, to extract text information contained in image data and return the extracted text information to a remote server. OCR recognition may include, for example, image preprocessing, text detection, and text recognition, wherein image preprocessing may include, for example, grayscale conversion (if it is a color image), noise reduction, binarization, character segmentation, and normalization.

[0045] In step S5, upon obtaining the data to be identified, the uploaded or extracted text can be used to generate prompt words for the AI ​​model. Then, in step S6, the prompt words can be submitted to the AI ​​model provider to generate or identify information such as POI (Point of Interest) information (i.e., information related to the navigation destination).

[0046] In one implementation, the AI ​​model can be an AI Large Language Model (LLM), such as ChatGPT, Claude, etc., which can be deployed locally on the user device (not shown) or on a remote server connected to the user device (e.g., Figure 1 ).

[0047] In step S7, the AI ​​model provider can return the generated POI information.

[0048] In step S8, the validity of the POI information can be verified at the remote server, for example, by identifying whether the obtained information contains a valid address.

[0049] If the information is valid, then in step S9, the POI information (e.g., via MQTT) can be sent to the vehicle (e.g., the vehicle's navigation unit) so that the vehicle can start navigation based on the received POI information, for example, in step S10.

[0050] Of course, it's understandable that information can be sent to the vehicle via multiple communication channels, for example, besides... Figure 1 In addition to sending information via a remote server, information can also be sent to the vehicle via a short-range communication connection (e.g., USB, Bluetooth).

[0051] If the information is invalid, in step S11, the relevant application can notify the user that the information is invalid and prompt the user to select or upload data again.

[0052] Therefore, by employing AI large language models to perform semantic and contextual analysis on the data selected / uploaded by the user, more accurate identification results can be obtained regarding the information to be sent, thus making the "sending information to the vehicle" service more intelligent and improving the user experience. Further details are provided below. Figure 2-4 This illustrates various scenarios for sending information to vehicles.

[0053] like Figure 2 As shown, Figure 2 This diagram illustrates a scenario 200 in which navigation destination information is sent to a vehicle based on any selected text, according to an embodiment of the present disclosure.

[0054] exist Figure 2 In an example scenario, a user might find interesting content (e.g., restaurant address information) while browsing a third-party app (such as an app recommending restaurants) and wish to send the navigation destination information contained within it to their vehicle. The user can then select and copy the content containing the address information and open a connected car app with a "Send Message to Vehicle" service. The app will then display a prompt asking the user to confirm whether they want to send the message to the vehicle. In response to the user's confirmation, the following steps can be performed: Figure 1The process involves steps S3 and S5-S10. Specifically, in response to the user's confirmation to send information to the vehicle, the text information selected by the user is transmitted to the backend, prompt words are generated and fed into the AI ​​large language model to generate or recognize accurate restaurant address information. After verifying its validity, this information is sent to the vehicle's infotainment system as the navigation destination. Subsequently, the vehicle's infotainment system receives the navigation destination information sent by the application and prompts the user to start navigation, for example, on the vehicle's large screen.

[0055] like Figure 3 As shown, Figure 3 This illustration shows a scenario 300 in which navigation destination information is sent to a vehicle based on a selected or uploaded image, according to an embodiment of the present disclosure.

[0056] exist Figure 3 In an example scenario, a user in a connected car application with a "send a message to the vehicle" service uses the camera (e.g., by clicking a relevant control) to capture a photo containing the address information the user wants to go to, or alternatively, selects an image from the album (e.g., a screenshot containing the address information) by clicking "Album." Then, in response to the user's confirmation to send a message to the vehicle, the following can be performed: Figure 1 The process involves steps S3-S10. Specifically, OCR technology can be used to perform text recognition on the captured or selected image to extract its text information. Based on the extracted text information, prompt words are generated and fed into an AI large language model to generate or recognize accurate navigation destination information. The application can then verify whether the recognized information contains valid address information. If valid information is found, the corresponding address information is sent to the vehicle's infotainment system. Subsequently, the vehicle's infotainment system receives the navigation destination information sent by the application and prompts the user to start navigation, for example, on the vehicle's large screen.

[0057] Of course, it is understandable that, in addition to the vehicle connectivity applications provided by vehicle manufacturers, other third-party applications or mini-programs with the service of "sending information to the vehicle" can also be used to carry out the above-mentioned procedures for sending information to the vehicle.

[0058] like Figure 4 As shown, Figure 4 This illustration shows another scenario 400 of sending navigation destination information to a vehicle based on a selected or uploaded image, according to one embodiment of the present disclosure.

[0059] exist Figure 4 In the example scenario, a user selects or captures an image in a third-party application (e.g., selecting an image in a chat interface within WeChat) and opens the image via, for example, a vehicle connectivity mini-program (which has a "send a message to the vehicle" service). Subsequently, in response to the user's confirmation to send a message to the vehicle, a similar process can be performed... Figure 1 The process involves steps S3-S10. Specifically, OCR technology can be used to extract the text information contained in the image. Based on the extracted text information, prompt words are generated and fed into an AI large language model to generate or recognize the address information contained therein. Then, it can be verified whether the recognized information contains valid address information. If it does, the corresponding address information is sent to the vehicle's infotainment system. Subsequently, the infotainment system receives the navigation destination information sent via the mini-program and prompts the user to start navigation, for example, on the vehicle's large screen.

[0060] Figure 5 This diagram illustrates a schematic architecture of an apparatus 500 for sending information from a user equipment to a vehicle according to an embodiment of the present disclosure. The apparatus 500 may include at least an information acquisition module 502, an information identification module 504, and an information sending module 506.

[0061] The information acquisition module 502 can acquire data (including, for example, text data or image data) containing information to be sent from an application running on the user device (e.g., a vehicle connectivity application or a third-party application).

[0062] In one implementation, the above information includes navigation destination information.

[0063] The information recognition module 504 can respond to a user's instruction to send information to the vehicle by using an AI model to identify the information to be sent based on the acquired data.

[0064] Specifically, the information recognition module 504 can respond to receiving an instruction from the user to send information to the vehicle, generate prompt words for the AI ​​model based on the acquired data, and send the prompt words to the AI ​​model to identify the information to be sent.

[0065] In one implementation, the AI ​​model may be a large language model LLM (including, for example, ChatGPT, Claude, etc.) deployed locally on the user device or deployed on a remote server connected to the user device in communication.

[0066] In the case where the acquired data is an image, the information recognition module 504 can further use OCR to extract the text information contained in the image data for processing by the AI ​​model while acquiring the image data.

[0067] In one implementation, in response to receiving an instruction from a user to send information to a vehicle, it can first check whether the user has permission to send information to the vehicle, and only if the user has permission to send information to the vehicle can it further identify the data and send the relevant information to the vehicle.

[0068] In one implementation, when identifying the information to be sent, it can be determined whether the identified information is valid. If it is valid, the subsequent sending process continues. If it is invalid, a prompt can be displayed on the user device that a valid message could not be identified.

[0069] The information sending module 506 can then send the identified information to the vehicle for further processing. For example, if the identified information is navigation destination information, it can send the information to the navigation unit of the vehicle's infotainment system for the vehicle to perform subsequent navigation operations.

[0070] Figure 6 An example computing device architecture 600 is shown, illustrating an example computing device (e.g., a user device) according to an embodiment of the present disclosure, capable of implementing various technologies described herein. The computing device may be implemented as, for example, a smartphone, a smartwatch, etc.

[0071] The components of computing device architecture 600 are shown to be in electrical communication with each other using bus 614. The example computing device architecture 600 includes a processor 604 and a bus 614 that couples various computing device components, including memory 602 (such as read-only memory (ROM) and random access memory (RAM)), to the processor 604.

[0072] The memory 602 may store computer-executable instructions that, when executed by the processor 604, cause the at least one processor to perform various functions described herein, including: acquiring data containing information to be sent in a running application; identifying the information to be sent based on the acquired data using an AI model in response to receiving an instruction from a user to send information to a vehicle; and sending the identified information to the vehicle for further processing.

[0073] Processor 604 may include a CPU, which in some examples may be a multi-core CPU. Instructions executed at the CPU may be loaded, for example, from program memory associated with the CPU or from memory 602. The one or more processors 604 may also include additional processing components tailored for specific functions, such as a graphics processing unit (GPU), a digital signal processor (DSP), a neural processing unit (NPU), or a multimedia processing unit. In some examples, the one or more processors may be based on the ARM or RISC-V instruction set.

[0074] The computing device architecture 600 may also include a cache 606 of high-speed memory that is directly connected to, adjacent to, or integrated into the processor 604. The computing device architecture 600 can copy data from the memory 602 to the cache 606 for fast access by the processor 604. In this way, the cache provides a performance boost, preventing latency for the processor 604 while waiting for data. These and other modules can control or be configured to control the processor 604 to perform various actions. Other computing device memories 602 may also be available. Memory 602 may include various different types of memory with different performance characteristics. The processor 604 may include any general-purpose processor and special-purpose processor (where software instructions are incorporated into the processor design). The processor 604 may be a self-contained system containing multiple cores or processors, buses, memory controllers, caches, etc. Multi-core processors may be symmetric or asymmetric.

[0075] To enable user interaction with computing device architecture 600, input device 608 can represent any number of input mechanisms, such as a microphone for voice, a touchscreen for gesture or graphical input, a keyboard, a mouse, etc. Output device 610 can also be one or more of a variety of output mechanisms known to those skilled in the art, such as a display, projector, television, or speaker device. In some instances, multimodal computing devices enable users to provide multiple types of input to communicate with computing device architecture 600. Communication interface 612 generally manages and controls user input and computing device output. There are no limitations on operation on any particular hardware arrangement, and therefore the underlying features here can be easily replaced to obtain improved hardware or firmware arrangements as they are developed.

[0076] Understandable. Figure 6 This is merely one example of a computing device, and other computing devices that include fewer, additional, or alternative aspects may also be consistent with this disclosure.

[0077] Furthermore, this application also discloses a computer-readable storage medium including computer-executable instructions stored thereon, which, when executed by a processor, cause the processor to perform the methods of the embodiments described herein.

[0078] The various illustrative blocks and modules described herein can be implemented or executed using a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices (e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working in conjunction with a DSP core, or any other such configuration).

[0079] The functions described herein can be implemented in hardware, software executed by a processor, firmware, or any combination thereof. If implemented in software executed by a processor, the functions can be stored or transmitted as one or more instructions or code on a computer-readable medium. Other examples and implementations fall within the scope of this disclosure and the appended claims. For example, due to the nature of software, the functions described herein can be implemented using software executed by a processor, hardware, firmware, hardwired, or any combination thereof. Features implementing the functions can also be physically located in various locations, including being distributed such that different parts of the function are implemented at different physical locations.

[0080] The foregoing description includes examples of various aspects of the claimed subject matter. It is certainly impossible to describe every conceivable combination of components or methods for the purpose of depicting the claimed subject matter, but those skilled in the art will recognize that many further combinations and arrangements of the claimed subject matter are possible. Thus, the disclosed subject matter is intended to cover all such changes, modifications, and variations that fall within the spirit and scope of the appended claims.

Claims

1. A method for sending information from a user equipment to a vehicle, the method comprising: The application running on the user equipment acquires data containing information to be sent; In response to receiving a command from the user to send a message to the vehicle, The AI ​​model is used to identify the information to be sent based on the acquired data; as well as The identified information is sent to the vehicle for further processing.

2. The method as described in claim 1, characterized in that, The information described is navigation destination information.

3. The method as described in claim 1, characterized in that, The AI ​​model is a large language model (LLM) deployed locally on the user equipment or deployed on a remote server connected to the user equipment.

4. The method as described in claim 3, characterized in that, The process of using the AI ​​model to identify the information to be sent based on the acquired data further includes: Based on the data, prompt words are generated for the AI ​​model; and The prompt words are fed into the AI ​​model to identify the information to be sent.

5. The method as described in claim 1, characterized in that, The data includes text data or image data.

6. The method as described in claim 5, characterized in that, In the case where the data is image data, the method further includes: When acquiring the image data, the text information contained in the image data is extracted for processing by the AI ​​model.

7. The method as described in claim 6, characterized in that, The extraction operation is performed using Optical Character Recognition (OCR).

8. The method as described in claim 1, characterized in that, The method further includes: In response to receiving an instruction from a user to send a message to the vehicle, the system checks whether the user has permission to send a message to the vehicle; and In response to the user having permission to send information to the vehicle, the identified information is sent to the vehicle for further processing.

9. The method as described in claim 1, characterized in that, The method further includes: When identifying the information to be sent, it is determined whether the identified information is valid; If the information is valid, the identified information is sent to the vehicle for further processing. If the information is invalid, a message will be displayed on the user device indicating that no valid information could be identified.

10. An apparatus for transmitting information from a user equipment to a vehicle, the apparatus comprising: An information acquisition module is used to acquire data containing information to be sent from an application running on the user device. The information recognition module is used to respond to a command from a user to send information to the vehicle, and to use an AI model to identify the information to be sent based on the acquired data. as well as The information sending module is used to send the identified information to the vehicle for further processing.

11. The apparatus as claimed in claim 10, characterized in that, The information described is navigation destination information.

12. The apparatus as claimed in claim 10, characterized in that, The AI ​​model is a Large Language Model (LLM) deployed locally on the user equipment or deployed on a remote server connected to the user equipment, and the information recognition module is further configured to: Based on the data, prompt words are generated for the AI ​​model; and The prompt words are fed into the AI ​​model to identify the information to be sent.

13. The apparatus as claimed in claim 10, characterized in that, The data includes text data or image data, and in the case where the data is image data, the information recognition module is further configured to: When acquiring the image data, optical character recognition (OCR) is used to extract the text information contained in the image data for processing by the AI ​​model.

14. A user equipment, comprising: A communication unit configured to communicatively connect to a remote server associated with the vehicle; processor; as well as A memory storing computer-executable instructions that, when executed by the processor, cause the processor to perform the method as described in any one of claims 1-9.

15. A computer-readable medium storing processor-executable instructions, which can be executed by a processor to perform the method as claimed in any one of claims 1-9.

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