Handwriting input candidate quick prediction method based on vehicle-mounted input method and electronic device

Abstract

The application discloses a handwriting input candidate quick prediction method based on a vehicle-mounted input method and electronic equipment, based on the writing habits of a user in a handwriting input mode of a vehicle-mounted input method, the current incomplete character or word input is combined with the current working condition of the vehicle, the application program calling the vehicle-mounted input method and the personalized word library constructed in advance based on the use history of the user in the application program, the handwriting input of the user is predicted in advance and the corresponding candidate result is pushed, in particular, the prediction process does not depend on a networking environment, so the disadvantages brought by network processing are avoided. The application aims at the pain point of vehicle-mounted handwriting input, when the user only inputs initial few strokes or letters, the word expected to be input by the user can be quickly predicted and several more targeted candidate items are pushed out, so that the handwriting input efficiency in a driving scene can be greatly improved, and then the input use experience can be improved, and the safety hidden danger can be significantly reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicle-mounted input method, in particular to a handwriting input candidate quick prediction method based on vehicle-mounted input method and electronic equipment. BACKGROUND

[0002] With the increasing amount of information that people need to input and process, the use requirements of vehicle-mounted input method configured in the car system also increase accordingly. Through investigation, users generally have expectations such as fast input, efficient prediction, and simple operation for this specific product of vehicle-mounted input method.

[0003] However, the handwriting input mode in the vehicle-mounted input method currently used on the market is similar to the handwriting input strategy of the input method in the conventional non-vehicle-mounted environment, and both need the user to recognize and push the candidate after handwriting inputting a complete character (or nearly complete). However, in the specific application scenario of driving a vehicle, this processing method is not time-efficient, and there is a great safety hazard.

[0004] In addition, most existing vehicle-mounted input methods are highly dependent on network environment when pushing candidates, and the driving scene determines that the stability of the network environment cannot be ensured. Therefore, if the network connection is not smooth, it may also cause obvious delay in recognition and candidate pushing, which also brings poor user experience and hidden dangers to the use of vehicle-mounted input method in the driving scene. SUMMARY

[0005] In view of the above, the present application aims to provide a handwriting input candidate quick prediction method, device and electronic equipment based on vehicle-mounted input method to solve the aforementioned drawbacks of existing vehicle-mounted input method in the driving scene.

[0006] The technical solution adopted by the present application is as follows:

[0007] In a first aspect, the present application provides a handwriting input candidate quick prediction method based on vehicle-mounted input method, which comprises:

[0008] When the vehicle is in driving condition, it is detected whether the current application in use wakes up the handwriting input mode of the vehicle-mounted input method;

[0009] After the handwriting input mode is activated, the initial strokes or initial letters written by the current user are received;

[0010] Using the initial strokes or initial letters, a personalized word library corresponding to the current user and the current application is obtained and matched; wherein the personalized word library is constructed in advance based on the use history of the current user in the current application;

[0011] According to the matching result, a plurality of pre-judgment target words are obtained, and the number of push candidate words is controlled based on the pre-judgment target words;

[0012] The target candidate words are pushed and displayed according to the number.

[0013] In at least one possible implementation, the pre-judgment method further comprises: recording the writing habits of the current user in advance based on writing units constituting words.

[0014] In at least one possible implementation, the recording of the writing habits of the current user in advance based on writing units constituting words comprises:

[0015] Obtaining and recording a user ID using a vehicle-mounted input method;

[0016] When the user uses a handwriting input mode for the first time, guiding the user to input a plurality of writing units constituting characters or words;

[0017] Storing the handwriting track information of each writing unit in association with the user ID.

[0018] In at least one possible implementation, a handwriting interface with a preset grid number is used to collect the writing units input by the user.

[0019] In at least one possible implementation, the manner of constructing the personalized word library comprises:

[0020] Extracting word information corresponding to the current user from historical use data of an application program;

[0021] Constructing a point of interest set using the word information;

[0022] Texting the word information, and disassembling each word from the word text data to obtain a writing unit set corresponding to each word;

[0023] Obtaining the personalized word library based on the point of interest set and the writing unit set.

[0024] In at least one possible implementation, the extracting of the word information corresponding to the current user from the historical use data of the application program comprises:

[0025] Obtaining a plurality of first words input by the current user in the past and a plurality of second words related to the used preset content;

[0026] According to the use frequency and / or number of times of the first words and the second words, a plurality of word information are screened out.

[0027] In at least one possible implementation, the controlling the number of candidate words pushed based on the pre-judged target words comprises: pushing a number of candidate words less than or equal to a preset number threshold.

[0028] In a second aspect, the present application provides a handwriting input candidate quick pre-judgment device based on an in-vehicle input method, which comprises:

[0029] a handwriting mode detection module configured to detect whether a current application in use wakes up a handwriting input mode of the in-vehicle input method when the vehicle is in a driving condition;

[0030] an initial input receiving module configured to receive initial strokes or initial letters written by a current user after the handwriting input mode is activated;

[0031] a word matching module configured to obtain and perform matching processing on a personalized word library corresponding to the current user and the current application by using the initial strokes or the initial letters, wherein the personalized word library is pre-constructed based on a use history of the current user in the current application;

[0032] an input pre-judgment module configured to obtain a plurality of pre-judged target words according to a matching result, and control a number of candidate words pushed based on the pre-judged target words;

[0033] a candidate pushing module configured to push and display target candidate words in the number.

[0034] In a third aspect, the present application provides an electronic device, which comprises:

[0035] one or more processors, a memory, and one or more computer programs, the memory can be in the form of a non-volatile storage medium, wherein the one or more computer programs are stored in the memory, and the one or more computer programs comprise instructions, when the instructions are executed by the device, the device executes the method as in the first aspect or any possible implementation of the first aspect.

[0036] The main idea of the present application is that, based on the writing habits of the user in the handwriting input mode of the vehicle-mounted input method, the current input incomplete character or word is combined with the current working condition of the vehicle, the application program calling the vehicle-mounted input method, and the personalized word library constructed in advance based on the use history of the user in the application program, the handwriting input of the user is predicted in advance and the corresponding candidate results are pushed, especially, the prediction process does not depend on the networking environment, so the disadvantages brought by network processing are also avoided. The present application aims at the pain points of vehicle-mounted handwriting input, and in an efficient way, the user can quickly predict the word expected to be input by the user and push out several more targeted candidate items after inputting only a small number of strokes or letters, so as to greatly improve the handwriting input efficiency in the driving scene, and then improve the input use experience while significantly reducing the safety hazards. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described below in combination with the drawings, in which:

[0038] Figure 1 The flowchart of an embodiment of the handwriting input candidate fast prediction method based on the vehicle-mounted input method provided by the present application;

[0039] Figure 2 The schematic diagram of an embodiment of the handwriting input candidate fast prediction device based on the vehicle-mounted input method provided by the present application;

[0040] Figure 3 The schematic diagram of an embodiment of the electronic device provided by the present application. DETAILED DESCRIPTION

[0041] The embodiments of the present application will be described in detail below, and the examples of the embodiments are shown in the drawings, in which the same or similar reference numerals represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present application, and cannot be explained as a limitation of the present application.

[0042] The present application proposes at least one embodiment of the handwriting input candidate fast prediction method based on the vehicle-mounted input method, as shown in Figure 1 The specific steps can include:

[0043] Step S1, when the vehicle is in the driving working condition, detecting whether the current application in use wakes up the handwriting input mode of the vehicle-mounted input method;

[0044] Step S2, after the handwriting input mode is activated, receiving the initial strokes or initial letters written by the current user;

[0045] Step S3, obtaining a personalized word library corresponding to the current user and the current application program by using the initial stroke or initial letter, and performing matching processing; wherein the personalized word library is pre-constructed based on the use history of the current user in the current application program;

[0046] Step S4, obtaining a plurality of pre-judgment target words according to the matching result, and controlling the number of push candidate words based on the pre-judgment target words;

[0047] Step S5, pushing and displaying the target candidate words according to the number.

[0048] In order to realize the personalized matching mentioned in the foregoing step S3, the pre-judgment method further includes the step S0, pre-recording the writing habits of the current user based on the writing units constituting the words (for example, but not limited to, horizontal, vertical, hook, nodule, dot, fold, lift, etc. corresponding to Chinese characters, or 26 letters corresponding to English, which is not exhaustively listed here), which can be specifically referred to as follows:

[0049] Step S01, obtaining and recording the user ID using the vehicle-mounted input method;

[0050] Step S02, when the user initially uses the handwriting input mode, guiding the user to enter a plurality of writing units constituting a character or a word;

[0051] Step S03, associating and storing the handwriting trajectory information of each writing unit with the user ID.

[0052] Due to the particularity of handwriting input, the writing methods of different users for the same word or even the same stroke are likely to have specific differences, so the vehicle-mounted input method can be pre-learned to adapt to the writing habits of different users. Taking Chinese characters as an example, the trajectory of an independent writing unit determines the structure of a Chinese character, so the grid fuzzy constraint idea is preferably used to identify each writing unit (horizontal, vertical, hook, nodule, dot, fold, lift, etc.) corresponding to the handwriting trajectory. Among them, the information collection of handwriting input can be understood as collecting writing coordinate points in a fixed (such as 800*1024) grid interface, and forming a handwriting trajectory representing different strokes from the coordinate information, so as to obtain the characteristics of the writing font of different users, that is, to identify the personalized writing habits. It can be supplemented here that through experimental induction, it is found that too fine grid background is not only redundant, but also makes the operation burden of stroke trajectory recognition extremely heavy, so a small grid writing interface with a preset specification is preferably used in the step of learning the writing habits of different users. The small grid writing interface with a preset specification refers to a non-fine grid background, that is, the number of grids in the length and width directions of the interface can be limited.

[0053] Regarding the aforementioned personalized word library, it can also be further elaborated that the specific method for constructing the personalized word library is as follows:

[0054] Step S31: Extract word information corresponding to the current user from the historical usage data of the application;

[0055] Step S32: Use the word information to construct a set of points of interest;

[0056] Step S33: Textualize the word information, and disassemble each word from the word text data to obtain a set of writing units corresponding to each word;

[0057] Step S34: Based on the set of points of interest and the set of writing units, obtain the personalized word library.

[0058] Regarding the decomposition operation of the word symbols itself, there are already mature text processing technologies that can be borrowed, so it will not be elaborated here. What can be further explained is the specific idea of step S31:

[0059] Step S311: Obtain multiple first words previously input by the current user and multiple second words related to the preset content used;

[0060] Step S311: Screen out several pieces of the word information according to the usage frequency and / or times of the first words and the second words.

[0061] Here, a specific introduction is given with an actual application example: Taking the music APP configured in the car machine as an example, the singer names, song names, lyrics, etc. that a certain user has historically queried can be counted as the first words, and the words involved in the sub-items such as the song lists and music channels listened to when using the music APP in the past can be extracted to form the second words. Then, the high-frequency first words and second words are screened out, used as the word information directly related to the personal interests of the current user to form a set of points of interest, and these word information are disassembled to obtain the writing units constituting the corresponding words respectively, generating a set of writing units

[0062] When the user uses the music APP for handwritten input, through the initially input writing units, such as 丿, 一, 亅, the user identity can be locked first and the aforementioned two sets corresponding to the current user can be called. Through the position splicing information of these few current writing units, it can be matched from the set of writing units that what the user expects to input is "周" (Zhou in Chinese), so that one or more singer names with the surname Zhou (or song names starting with Zhou, etc.) in the set of points of interest can be used as the predicted target words.

[0063] As for the design concept of step S4, in order to form an efficient matching and prediction mechanism, the number of candidate words pushed is limited in addition to the constraint from the word library as described above. The conventional input method pushing mechanism displays as many candidate words as possible for the user to select, and may also use the network for cloud search. However, in the present application, in order to ensure driving safety, the number of displayed words is controlled when pushing the candidates, for example, the given number threshold is 5, if the prediction result obtained based on the prediction link is less than or equal to 5, the prediction result is displayed, if the prediction result is more than 5, only the first 5 prediction results are output as target candidates. Those skilled in the art can understand that the candidate sorting strategy involved here is a mature technology of input method, which is not described or limited in the present application. That is, the present application emphasizes not to provide too many candidates for the user to make a quick screening decision, avoiding distraction caused by too many choices (which is the same logic as the present application only uses a small number of strokes or letters for candidate prediction).

[0064] Finally, it can also be pointed out that in the real handwriting input scene, the above-mentioned embodiments of the present application can be used as the first round prediction mechanism for candidate word selection, that is, in some preferred embodiments, the candidate prediction method proposed by the present application is used preferentially, if the present application fails to provide suitable candidates or the prediction fails due to some factors, the conventional handwriting pushing mechanism can be restored to remedy the situation with the conventional candidate prediction strategy.

[0065] In summary, the main idea of the present application is to combine the current input of incomplete characters or words with the current working condition of the vehicle, the application program calling the vehicle-mounted input method, and the personalized word library constructed based on the user's use history in the application program, to predict and push the corresponding candidate results in advance based on the user's writing habits in the handwriting input mode of the vehicle-mounted input method. In particular, the prediction process does not depend on the network environment, thus avoiding the disadvantages brought by network processing. The present application addresses the pain points of vehicle-mounted handwriting input, and in an efficient way, the user can quickly predict the word expected to be input by inputting only a small number of initial strokes or letters, and push out several more targeted candidates, thereby greatly improving the handwriting input efficiency in the driving scene, and thus improving the input experience while significantly reducing the safety hazards.

[0066] Corresponding to the above embodiments and preferred solutions, the present application also provides an embodiment of a handwriting input candidate fast prediction device based on a vehicle-mounted input method, as shown in Figure 2 which can specifically include the following components:

[0067] The handwriting mode detection module 1 is used to detect whether the currently used application has activated the handwriting input mode of the in-vehicle input method when the vehicle is in driving condition.

[0068] The initial input receiving module 2 is used to receive the initial stroke or initial letter written by the current user after the handwriting input mode is activated;

[0069] The word matching module 3 is used to obtain a personalized word library corresponding to the current user and the current application using initial strokes or initial letters and to perform matching processing; wherein, the personalized word library is pre-built based on the current user's usage history in the current application;

[0070] Input prediction module 4 is used to obtain several predicted target words based on the matching results, and control the number of candidate words to be pushed based on the predicted target words;

[0071] The candidate push module 5 is used to push and display the target candidate words according to the stated quantity.

[0072] The above should be understood Figure 2 The division of components in the handwriting input candidate rapid prediction device based on in-vehicle input method shown is merely a logical functional division. In actual implementation, all or part of these components can be integrated into a single physical entity, or they can be physically separated. Furthermore, these components can be implemented entirely in software via processing element calls; they can be implemented entirely in hardware; or some components can be implemented in software via processing element calls, while others are implemented in hardware. For example, a certain module can be a separate processing element or integrated into a chip in the electronic device. The implementation of other components is similar. Moreover, these components can be integrated together or implemented independently. During implementation, each step of the above method or each of the above components can be completed through integrated logic circuits in the hardware of the processor element or through software instructions.

[0073] For example, these components can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, these components can be integrated together to form a System-On-a-Chip (SOC).

[0074] In light of the above embodiments and preferred solutions, those skilled in the art can understand that, in actual operations, the technical concept involved in the present application can be applied to various embodiments. The following carrier is used as an illustrative description:

[0075] (1) An electronic device. The device can specifically include one or more processors, a memory, and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when executed by the device, cause the device to perform the steps / functions of the foregoing embodiments or equivalent implementations.

[0076] The electronic device can be a computer-related electronic device, such as but not limited to various interactive terminals and electronic products, mobile terminals, and the like.

[0077] Figure 3 The structural schematic diagram of the embodiment of the electronic device provided by the present application is specifically shown in FIG. 9. The electronic device 900 includes a processor 910 and a memory 930. The processor 910 and the memory 930 can communicate with each other through an internal connection path to transfer control and / or data signals. The memory 930 is used to store a computer program, and the processor 910 is used to call and run the computer program from the memory 930. The processor 910 and the memory 930 can be combined into one processing device, and more commonly, they are independent components. The processor 910 is used to execute the program code stored in the memory 930 to realize the above functions. In specific implementation, the memory 930 can also be integrated in the processor 910, or independent of the processor 910.

[0078] In addition, in order to make the function of the electronic device 900 more perfect, the device 900 can further include one or more of an input unit 960, a display unit 970, an audio circuit 980, a camera 990, and a sensor 901. The audio circuit can further include a speaker 982 and a microphone 984. The display unit 970 can include a display screen.

[0079] Further, the device 900 can further include a power supply 950 for providing power to various devices or circuits in the device 900.

[0080] It should be understood that the operation and / or function of each component in the device 900 can be specifically referred to the description of the method, system, and the like embodiments in the foregoing, and the detailed description is appropriately omitted here to avoid repetition.

[0081] It should be understood that, Figure 3The processor 910 in the electronic device 900 shown can be a system on chip (SOC), which can include a central processing unit (CPU) and can further include other types of processors, such as a graphics processing unit (GPU), and the like, as described in more detail below.

[0082] In summary, the various processors or processing units within the processor 910 can cooperate to implement the above-described method flows, and the corresponding software programs for the various processors or processing units can be stored in the memory 930.

[0083] (2) A computer data storage medium having stored thereon a computer program or the above-described apparatus, which, when executed, cause a computer to perform the steps / functions of the above-described embodiments or equivalent implementations.

[0084] In several embodiments provided in the present application, any function realized in the form of a software function unit and sold or used as an independent product can be stored in a computer data storage medium. Based on this understanding, some of the technical solutions of the present application or parts of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product as described below.

[0085] In particular, it should be noted that the storage medium can be a server or a similar computer device, and specifically, the storage medium can be a storage device in the server or similar computer device that stores the above-described computer program or apparatus.

[0086] (3) A computer program product (which can include the above-described apparatus), which, when executed on a terminal device, causes the terminal device to perform the handwriting input candidate quick prediction method based on the vehicle-mounted input method of the above-described embodiments or equivalent implementations.

[0087] As can be clearly understood by the above description of the embodiments, those skilled in the art can clearly understand that all or part of the steps in the above-described embodiments can be implemented by means of software and a necessary general hardware platform. Based on this understanding, the above-described computer program product can include, but is not limited to, an APP.

[0088] With the preceding, the device / terminal can be a computer device, and the hardware structure of the computer device can further include at least one processor, at least one communication interface, at least one memory and at least one communication bus; the processor, the communication interface, the memory can all communicate with each other through the communication bus. The processor can be a central processing unit CPU, a DSP, a microcontroller or a digital signal processor, and can further include a GPU, an embedded neural network processing unit (Neural-network Process Units, hereinafter referred to as NPU) and an image signal processor (Image Signal Processing, hereinafter referred to as ISP), and the processor can further include a specific integrated circuit ASIC, or one or more integrated circuits configured to implement the embodiments of the present application, and the processor can have the function of operating one or more software programs, and the software programs can be stored in the memory or other storage medium; and the aforementioned memory / storage medium can include a non-volatile memory such as a non-removable disk, a U disk, a mobile hard disk, an optical disk, a read-only memory (Read-Only Memory, hereinafter referred to as ROM), a random access memory (Random Access Memory, hereinafter referred to as RAM) and the like.

[0089] In the embodiments of the present application, "at least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B can represent the cases of A alone, A and B together, and B alone. Wherein A, B can be singular or plural. The character " / " generally represents that the associated objects before and after are in an "or" relationship. "At least one of the following" and the like means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, wherein a, b, c can be single or multiple.

[0090] Those skilled in the art can appreciate that the modules, units and method steps described in the embodiments disclosed in the specification can be realized by electronic hardware, computer software and combination of electronic hardware and computer software. Whether the functions are realized in hardware or software mode depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different ways to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0091] Furthermore, the modules, units, or the like described as separate components may or may not be physically separate. That is, they may be located in one place, or they may be distributed over multiple places, such as nodes of a system network. Depending on the actual requirements, some or all of the modules, units, or the like can be selected to achieve the purpose of the above embodiments. Those skilled in the art can understand and implement without creative labor.

[0092] The above describes the structure, features and effects of the present application in detail according to the embodiments shown in the drawings, but the above is only the preferred embodiment of the present application. It should be noted that the technical features involved in the above embodiments and preferred modes can be reasonably combined and matched into various equivalent schemes by those skilled in the art without departing from or changing the design idea and technical effects of the present application. Therefore, the present application is not limited by the drawings shown in the drawings. Any change or modification made according to the concept of the present application, or any equivalent embodiment within the scope of the spirit of the specification and drawings, shall be within the scope of protection of the present application.

Claims

1. A handwriting input candidate quick prediction method based on an on-vehicle input method, characterized by, The method comprises: detecting whether a current application in use wakes up a handwriting input mode of an in-vehicle input method when a vehicle is in a driving mode; receiving initial strokes or initial letters written by a current user after the handwriting input mode is activated; obtaining a personalized word library corresponding to the current user and the current application by using the initial strokes or the initial letters and performing matching processing; wherein the personalized word library is pre-constructed based on a use history of the current user in the current application; obtaining a plurality of pre-judgment target words according to a matching result, and controlling a number of candidate words to be pushed based on the pre-judgment target words; pushing and displaying target candidate words in the number.

2. The method of claim 1, wherein the method further comprises: The pre-judgment method further comprises: recording writing habits of the current user based on writing units constituting words in advance.

3. The method of claim 2, wherein the method further comprises: The recording of the writing habits of the current user based on the writing units constituting words in advance comprises: obtaining and recording a user ID using the in-vehicle input method; guiding the user to input a plurality of writing units constituting characters or words when the user uses the handwriting input mode for the first time; storing handwriting track information of each writing unit in association with the user ID.

4. The method of claim 2, wherein the method further comprises: A handwriting interface with a preset number of grids is adopted to collect writing units input by the user.

5. The method of claim 1, wherein the method further comprises: The method of constructing the personalized word library comprises: extracting word information corresponding to the current user from historical use data of the application; constructing a point of interest set by using the word information; textualizing the word information, and decomposing each word from the word text data to obtain a writing unit set corresponding to each word; obtaining the personalized word library based on the point of interest set and the writing unit set.

6. The method of claim 5, wherein the method further comprises: The extracting of the word information corresponding to the current user from the historical use data of the application comprises: obtaining a plurality of first words input by the current user in the past and a plurality of second words related to the used preset content; screening a plurality of word information according to the use frequency and / or the number of times of the first words and the second words.

7. The method according to any one of claims 1 to 6, wherein the method is characterized by, The controlling of the number of candidate words to be pushed based on the pre-judgment target words comprises: the number of candidate words to be pushed is less than or equal to a preset number threshold.

8. A handwriting input candidate quick prediction device based on an on-vehicle input method, characterized by, The method comprises: a handwriting mode detection module configured to detect whether a current application in use wakes up a handwriting input mode of an in-vehicle input method when a vehicle is in a driving mode; an initial input receiving module configured to receive initial strokes or initial letters written by a current user after the handwriting input mode is activated; a word matching module configured to obtain a personalized word library corresponding to the current user and the current application by using the initial strokes or the initial letters and perform matching processing; wherein the personalized word library is pre-constructed based on a use history of the current user in the current application; an input pre-judgment module configured to obtain a plurality of pre-judgment target words according to a matching result, and control a number of candidate words to be pushed based on the pre-judgment target words; a candidate pushing module configured to push and display target candidate words in the number.

9. An electronic device, comprising: The method comprises: One or more processors, memories, and one or more computer programs, wherein the one or more computer programs are stored in the memories, the one or more computer programs comprising instructions, when executed by the electronic device, causing the electronic device to perform the handwriting input candidate quick prediction method based on the vehicle-mounted input method according to any one of claims 1-7.

10. A computer data storage medium, characterized by The computer data storage medium has stored therein a computer program, when the computer program is run on a computer, causing the computer to perform the handwriting input candidate quick prediction method based on the vehicle-mounted input method according to any one of claims 1-7.

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