Patent Publication Number: US-2023162514-A1

Title: Intelligent recommendation method, vehicle-mounted device, and storage medium

Description:
FIELD 
     The present disclosure relates to vehicle management technologies, in particular to an intelligent recommendation method, a vehicle-mounted device, and a storage medium. 
     BACKGROUND 
     Many vehicles have vehicle-mounted voice assistants like Siri and Amazon Alexa. Drivers can communicate with the vehicle-mounted voice assistant through natural language and can control related functions of the vehicle-mounted device. However, when the vehicle-mounted voice assistant responds to the driver&#39;s voice command, the vehicle-mounted voice assistant does not consider occupants except the driver in the vehicle. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a diagram of one embodiment of an application environment of an intelligent recommendation method provided by the present disclosure. 
         FIG.  2    is a block diagram of one recommendation system provided by the present disclosure. 
         FIG.  3    is a block diagram of another recommendation system provided by the present disclosure. 
         FIG.  4    is a flowchart of a first embodiment of an intelligent recommendation method provided by the present disclosure. 
         FIG.  5    is a flowchart of a second embodiment of the intelligent recommendation method provided by the present disclosure. 
         FIG.  6    is a flowchart of a third embodiment of an intelligent recommendation method provided by the present disclosure. 
         FIG.  7    is a flowchart of a fourth embodiment of the intelligent recommendation method provided by the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     In order to provide a more clear understanding of the objects, features, and advantages of the present disclosure, the same are given with reference to the drawings and specific embodiments. It should be noted that the embodiments in the present disclosure and the features in the embodiments may be combined with each other without conflict. 
     In the following description, numerous specific details are set forth in order to provide a full understanding of the present disclosure. The present disclosure may be practiced otherwise than as described herein. The following specific embodiments are not to limit the scope of the present disclosure. 
     Unless defined otherwise, all technical and scientific terms herein have the same meaning as used in the field of the art technology as generally understood. The terms used in the present disclosure are for the purposes of describing particular embodiments and are not intended to limit the present disclosure. 
       FIG.  1    illustrates a diagram of one embodiment of an application environment of an intelligent recommendation method provided by the present disclosure. 
     In this embodiment, the intelligent recommendation method is applied in an environment where an intelligent recommendation system  200  is formed by a vehicle-mounted device  3  and a cloud server  4  that are in communication with each other. The intelligent recommendation method is used for collecting voice information and attributes of occupants in a vehicle using the vehicle-mounted device  3 , and identifying, at the cloud server  4 , user intentions based on the voice information and attributes of occupants in the vehicle and performing, at the cloud server  4 , relevant recommendations based on the user intentions. Details are described below. 
     In this embodiment, the vehicle-mounted device  3  is installed on a vehicle  100 . The vehicle-mounted device  3  can also be called a vehicle-mounted computer, and includes a storage device  31 , at least one processor  32 , a detection device  33 , a microphone  34 , a communication device  35 , a display screen  36 , and a speaker  37  that are electrically connected to each other. 
     In this embodiment, the cloud server  4  includes a storage device  41 , at least one processor  42 , and a communication device  43 . The cloud server  4  and the vehicle-mounted device  3  establish a communication connection through the communication device  43  and the communication device  35 . The communication device  43  and the communication device  35  may be wireless communication devices. 
     Those skilled in the art should understand that the structures of the vehicle-mounted device  3  and the cloud server  4  shown in  FIG.  1    do not constitute a limitation of the embodiments of the present disclosure, and the vehicle-mounted device  3  and the cloud server  4  may respectively include 
     More or snore fewer additional hardware or software, or have different arrangements of components. For example, the vehicle-mounted device  3  may also include a speed sensor and other devices. The cloud server  4  may also include a display screen and the like. 
     It should be noted that the vehicle-mounted device  3  and the cloud server  4  are only examples, and other existing or future vehicle-mounted devices and cloud servers that can be adapted to this disclosure should also be included within the protection scope of this disclosure, and be incorporated herein by reference. 
     In some embodiments, the storage device  31  and the storage device  41  may store program codes of computer programs and various data. For example, the storage device  31  can be used to store a recommendation system  30  installed in the vehicle-mounted device  3 , and realize high-speed and automatic access to programs or data during an operation of the vehicle-mounted device  3 . The storage device  41  can be used to store a recommendation system  40  installed in the cloud server  4 , and realize high-speed and automatic access to programs or data during an operation of he cloud server  4 . The storage device  31  and the storage device  41  may include Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), One-time Programmable Read-Only Memory (OTPROM), Electronically Erasable Programmable Read-Only Memory (EEPROM), CD-ROM (Compact Disc Read-Only Memory) or other optical disk storage, magnetic disk storage, magnetic tape storage, or any other non-volatile computer-readable storage medium that can be used to carry or store data. 
     In some embodiments, the at least one processor  32  and the at least one processor  42  may each be comprised of integrated circuits. For example, each can be composed of a single packaged integrated circuit, or each can be composed of a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (CPU), microprocessor controller, digital processing chip, graphics processor and combination of various control chips, etc. The at least one processor  32  is a control unit of the vehicle-mounted device  3 , and uses various interfaces and lines to connect various components of the vehicle-mounted device  3 , and executes programs or modules or instructions stored in the storage device  31 , and invokes data stored in the storage device  31  to execute various functions and process data of the vehicle-mounted device  3 , for example, to execute the function of intelligent recommendation (for details, please refer to  FIG.  4   ,  FIG.  5   , and  FIG.  6   ). The at least one processor  42  is a control unit of the cloud server  4 , and uses various interfaces and lines to connect various components of the cloud server  4 , and executes programs or modules or instructions stored in the storage device  41 , and invoke the data stored in the storage device  41  to perform various functions and process data of the cloud server  4 , for example, to perform the function of intelligent recommendation (for details, please refer to  FIG.  4   ,  FIG.  5   , and  FIG.  6   ). 
     In this embodiment, the detection device  33  includes, but is not limited to, one or more cameras (also referred to as “camera modules”), pressure sensors, ultrasonic sensors, and gravity sensors installed on the vehicle  100 . 
     In this embodiment, the one or more cameras may be used to capture images of each occupant in the vehicle  100 . The one or more cameras may also be used to capture images of a scene in front of the vehicle  100 . 
     In this embodiment, the one or more cameras may include a 2D CMOS (Complementary Metal Oxide Semiconductor) camera with an infrared function or a ToF (Time of Flight) camera with a laser. The pressure sensor, ultrasonic sensor, or gravity sensor may be used to sense whether there is an occupant on the seats of the vehicle  100 . 
     The microphone  34  may be used to collect voice information, for example, to collect the voice information of the occupants in the vehicle  100 . 
     In this embodiment, the display screen  36  may be a touch display screen, which is used to display various data of the vehicle-mounted device  3 , such as a user interface of the recommendation system  30 . The speaker  37  can be used to output sound. 
     In the present embodiment, the recommendation system  30  can include one or more modules, and the one or more modules are stored in the storage device  31  and can be executed by one or more processors (e.g., the processor  32 ) to realize the functions provided by this disclosure. Referring to  FIG.  2   , in this embodiment, the recommendation system  30  may include a determination module  300 , an acquisition module  301 , and an execution module  302 . A “module” as referred to in this disclosure is a segment of computer program capable of performing a specific function. The detailed functions of each module will be described below with reference to  FIG.  4   ,  FIG.  5   , and  FIG.  6   . 
     In this embodiment, the recommendation system  40  may include one or more modules, and the one or more modules are stored in the storage device  41  and processed by one or more processors (e.g., the processor  42 ) to realize the functions provided by this disclosure. Referring to  FIG.  3   , in this embodiment, the recommendation system  40  may include a receiving module  401  and a response module  402 , The detailed functions of each module will be described below with reference to  FIG.  4   , 
       FIG.  5   , and  FIG.  6   . 
       FIG.  4    is a flowchart of a first embodiment of an intelligent recommendation method provided by present disclosure. 
     In this embodiment, the intelligent recommendation method can be applied to an environment including the vehicle-mounted device  3  and the cloud server  4 . For the vehicle-mounted device  3  and the cloud server  4  that need to perform intelligent recommendation, the functions for intelligent recommendation provided by this disclosure can be directly integrated on the vehicle-mounted device  3  and the cloud server  4 , or in form of software development kits (SDK) running in the vehicle-mounted device  3  and the cloud server  4 . 
     As shown in  FIG.  4   , the intelligent recommendation method includes the following blocks. According to different requirements, an order of the blocks in the flowchart can be changed, and some blocks can be omitted. 
     At block S 1 , the determination module  300  of the vehicle-mounted device  3  determines whether the vehicle  100  meets a preset condition. When the vehicle  100  meets the preset condition, the process goes to block S 2 . 
     In an embodiment, the vehicle  100  meeting preset conditions may include doors of the vehicle  100  being closed and/or an engine of the vehicle  100  is activated. 
     For example, when a door of the vehicle  100  is closed, a door lock detector of the vehicle-mounted device  3  (not shown in the figure sends a signal as to locking (hereinafter named as “lock signal”) to the vehicle-mounted device  3 . Therefore, the determination module  300  can determine that the vehicle  100  meets the preset condition when the lock signal is detected. It should be noted that because passengers of the vehicle  100  may be on and off at many times, and may cause the vehicle  100  meets the preset condition at each time, then the blocks of this intelligent recommendation method can be executed at each time. 
     At block S 2 , the execution module  302  of the vehicle-mounted device  3  captures images of occupants in the vehicle  100  by using the camera module of the detection device  33 , and obtains attributes of occupants in the vehicle  100  according to the captured images. 
     In one embodiment, the attributes of occupants in the vehicle  100  include, but are not limited to, a total number of occupants in the vehicle  100 , an identity of a driver of the vehicle  100 , an age and a gender of the driver. In one embodiment, the attributes of occupants in the vehicle  100  further includes identity information of each occupant, an age and a gender of each occupant, and a composition relationship of all occupants in the vehicle  100  (the composition relationship of all occupants in the vehicle  100  means a composition relationship of occupants currently in the vehicle  100 ). 
     In one embodiment, the composition relationship of all occupants in the vehicle  100  may refer to a relationship of lovers, a relationship of family, a relationship of parent-child, or another relationship such as a relationship of each being a stranger. 
     In this embodiment, when the vehicle  100  meets the preset condition, the execution module  302  may control the camera module of the detection device  33  to capture images of the occupants in the vehicle  100 . The execution module  302  of the vehicle-mounted device  3  may determine the age and the gender of each occupant according to the captured images by using a gender and age recognition algorithm based on face of each occupant n the captured image. In one embodiment, the execution module  302  of the vehicle-mounted device  3  pre-stores in the storage device  41  the identity information of each occupant who has ever ridden in the vehicle  100 , and the relationship between the occupants who have ever ridden in the vehicle  100 . In one embodiment, the identity information of the occupant who have ever ridden in the vehicle  100  includes, but is not limited to, a name, a face image, a role (i.e., a driver or a passenger) of the occupant when in the vehicle  100 , and a driving mode of the vehicle  100  when the occupant in the vehicle  100 . 
     In one embodiment, driving modes of the vehicle  100  may include, but are not limited to, a normal mode, a sport mode, and a highway mode. In one embodiment, different driving modes have different requirements on power output of the vehicle  100 . Wherein, the normal mode has the lowest requirement on the power output of the vehicle  100 , the sport mode has a higher requirement on the power output of the vehicle  100 , and the highway ode has the highest requirement on the power output of the vehicle  100 . 
     In one embodiment, when the execution module  302  of the vehicle-mounted device  3  is not able to obtain the identity information of an occupant from the storage device  41 , the execution module  302  determines that the occupant is a stranger. In one embodiment, the execution module  302  may use a face recognition algorithm to determine whether the occupant is a stranger based on a face image of the occupant pre-stored in the storage device  41  and the captured image of the occupant. 
     Specifically, the execution module  302  can obtain a face image of the occupant from the captured image of the occupant. When the execution module  302  recognizes that the obtained face image of the occupant does not match the face image of the occupant pre-stored in the storage device  41 , the execution module  302  can determine that the occupant is a stranger. On the contrary, when the obtained face image of the occupant matches the face image of the occupant pre-stored in the storage device  41 , the vehicle-mounted device  3  can determine that the occupant is an occupant who has ever ridden in the vehicle  100 . Therefore, the vehicle-mounted device  3  can also obtain other identity information of a non-first-time occupant from the storage device  41  based on the obtained face image of the occupant, the other identity information of the non-first-time occupant may include the name, the role of the non-first-time occupant, the driving mode of the vehicle, and a relationship between the non-first-time occupant and other non-first-time occupants when the non-first-tine occupant previously in the vehicle  100 , and the like. 
     In other embodiments, the execution module  302  of the vehicle-mounted device  3  may also obtain the image of the occupant in the vehicle  100  and send the obtained image of the occupant to the cloud server  4 , and the cloud server  4  can confirm the attributes of occupants in the vehicle  100  based on the obtained image. 
     At block S 3 , the acquisition module  301  of the vehicle-mounted device  3  collects information as the voice (hereinafter named as “voice information”) of the occupants in the vehicle  100  using the microphone  34 . 
     For example, when an occupant of the vehicle  100 , such as a driver, says “Please recommend a nearby restaurant”, the microphone  34  can collect corresponding voice information. 
     At block S 4 , the execution module  302  of the vehicle-mounted device  3  sends the voice information to the cloud server  4  through the communication device  35 . 
     At block S 5 , the receiving module  401  of the cloud server  4  receives the voice information through the communication device  43 . The response module  402  of the cloud server  4  obtains a user intention by analyzing the voice information and generates recommendation information based on the user intention. The response module  402  sends the user intention and the recommendation information to the vehicle-mounted device  3  through the communication device  43 . 
     In this embodiment, the response module  402  can first convert the voice information into text by using a voice recognition technology; and then analyze the text using an intention recognition algorithm such as a dictionary and template-based rule method to obtain the user intention. 
     For example, assuming that the voice information is “please recommend a nearby restaurant”, the response module  402  obtains the user intention as: “search”, “nearby restaurant” by using the intent recognition algorithm. The response module  402  obtains restaurant information within a preset distance such as  500  meters from a predetermined application (APP) such as a software of a Google map according to the user intention, namely “search” and “nearby restaurants”, and sets the restaurant information as the recommendation information. 
     At block S 6 , the acquisition module  301  of the vehicle-mounted device  3  receives the user intention and recommendation information through the communication device  35 . 
     At block S 7 , the execution module  302  of the vehicle-mounted device  3  determines whether the user intention is suitable for adding the attributes of occupants in the vehicle  100 . When it is determined that he user intention is not suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 8 . When it is determined that the user intention is suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 9 . 
     In one embodiment, the execution module  302  can determine whether the user intention is suitable for adding the attributes of occupants in the vehicle  100  in response to user input. 
     In one embodiment, the execution module  302  may generate a dialog box and display the dialog box on the display screen  36 . The execution module  302  may determine whether the user intention is suitable for adding the attributes of occupants in the vehicle  100  according to user&#39;s selection on the dialog box. 
     For example, since the cloud server  4  currently recommends restaurants without considering the attributes of occupants in the vehicle  100 , the recommended restaurants include many restaurants suitable for couples, but a composition relationship in the vehicle  100  is a relationship of parent-child, and such restaurant may not be a good choice. Accordingly, the user may select on the dialog and add the attributes of occupants in the vehicle  100 . Of course, the execution module  302  can also determine whether the user intention is suitable for adding the attributes of occupants in the vehicle  100  by analyzing the user&#39;s voice input. 
     In other embodiments, the execution module  302  of the vehicle-mounted device  3  may pre-store various user intentions that are suitable for adding the attributes of occupants in the vehicle  100 ; when the received user intention matches any of the pre-stored user intentions, the execution module  302  determines that the received user intention is suitable for adding the attributes of occupants in the vehicle  100 ; when the received user intention does not match any of the pre-stored user intentions, the execution module  302  determines that the received user intention is not suitable for adding the attributes of occupants in the vehicle  100 . 
     In this embodiment, the pre-stored user intentions may include keywords such as “restaurant”, “hotel”, or “scenic spots” which are related to human activities. 
     At block S 8 , when the user intention is not suitable for adding the attributes of occupants in the vehicle  100 , the execution module  302  of the vehicle-mounted device  3  displays the recommendation information. The execution module  302  of the vehicle-mounted device  3  may also broadcast aloud the recommendation information. 
     For example, the execution module  302  may display the recommendation information on the display screen  36 . The execution module  302  may invoke the speaker  37  to broadcast the recommendation information aloud. 
     At block S 9 , when the user intention is suitable for adding the attributes of occupants in the vehicle  100 , the execution module  302  of the vehicle-mounted device  3  obtains an updated user intention by adding the attributes of occupants in the vehicle  100  to the user intention, and sending the updated user intention to the cloud server  4  through the communication device  35 . 
     In other embodiments, the execution module  302  may obtain he updated user intention by only adding the composition relationship of all occupants in the vehicle  100  to the user intention. For example, the execution module  302  may obtain an updated user intention: “search”, “nearby restaurants”, and “parent-child” by adding the composition relationship of all occupants of the vehicle  100 , such as the relationship of parent-child to the user intention. In one embodiment, if the execution module  302  only adds the composition relationship of all occupants of the vehicle  100  to the user intention, the execution module  302  may also send other attributes such as the identity information of the occupants in the vehicle  100  to the cloud server  4 . 
     At block S 10 , the response module  402  of the cloud server  4  generates updated recommendation information based on the updated user intention. The response module  402  of the cloud server  4  also sends the updated recommendation information to the vehicle-mounted device  3  through the communication device  43 . 
     For example, the response module  402  sends a number of restaurants suitable for the relationship of parent-child to the vehicle-mounted device  3  in a form of a recommendation list. 
     In one embodiment, the cloud server  4  pre-stores the identity information of each occupant who has ever ridded/has previously travelled in the vehicle  100 , as well as preference information of each occupant. 
     In other embodiments, the response module  402  can obtain preference information of each occupant based on identity information of the each occupant. 
     In other embodiments, the response module  402  updates the recommendation information based on the updated user intention and the preference information of each occupant. 
     At block S 11 , the acquisition module  301  of the vehicle-mounted device  3  receives the updated recommendation information through the communication device  35 . The execution module  302  of the vehicle-mounted device  3  can also display the updated recommendation information on the display screen  36 . The execution module  302  of the vehicle-mounted device  3  can also broadcast the updated recommendation information using speaker  37 . 
       FIG.  5    is a flowchart of a second embodiment of an intelligent recommendation method provided by present disclosure. 
     In this embodiment, the intelligent recommendation method can be applied to an environment including the vehicle-mounted device  3  and the cloud server  4 . For the vehicle-mounted device.  3  and the cloud server  4  that need to perform intelligent recommendation, the functions of intelligent recommendation provided by this disclosure can be directly integrated on the vehicle-mounted device  3  and the cloud server  4 , or in form of software development kits (SDK) running in the vehicle-mounted device  3  and the cloud server  4 . 
     As shown in  FIG.  5   , the intelligent recommendation method includes the following blocks. According to different requirements, an order of the blocks in the flowchart can be changed, and some blocks can be omitted. 
     Block S 20  is the same as block S 1 , and details are not repeated here. 
     Block S 21  is the same as block S 2 , and details are not repeated here. 
     Block S 22  is the same as block S 3 , and will not be repeated here. 
     Block S 23  is the same as block S 4 , and details are not repeated here. 
     Block S 24  is the same as block S 5 , and details are not repeated here. 
     Block S 25  is the same as block S 6 , and details are not repeated here. 
     At block S 26 , the execution module  302  of the vehicle-mounted device  3  determines whether the user intention is suitable for adding the attributes of occupants in the vehicle  100 . When it is determined that the user intention is not suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 27 . When it is determined that the user intention is suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 28 . 
     The determining as to whether the user intention is suitable for adding the attributes of occupants in the vehicle  100  is the same as that described in the foregoing block S 7 , and therefore will not be repeated. 
     Block S 27  is the same as block S 8 , and details are not repeated here. 
     At block S 28 , when it is determined that the user intention is suitable for adding the attributes of occupants in the vehicle  100 , the execution module  302  of the vehicle-mounted device  3  sends the attributes of occupants in the vehicle  100  to the cloud server  4  through the communication device  35 . 
     At block S 29 , the response module  402  of the cloud server  4  updates the user intention based on the attributes of occupants in the vehicle  100 , and generates updated recommendation information based on the updated user intention. The response module  402  of the cloud server  4  also sends the updated recommendation information to the vehicle-mounted device  3  through the communication device  43 . 
     In one embodiment, the response module  402  obtains the updated user intention by adding the attributes of occupants in the vehicle  100  to the user intention. 
     In other embodiments, the response module  402  may only add the composition relationship of all occupants in the vehicle  100  to the user intention to obtain the updated user intention. For example, the response module  402  may add the composition relationship of all occupants in the vehicle  100 , such as a relationship of parent-child, to the user intention to obtain the updated user intention such as: “search”, “nearby restaurants”, and “parent-child”. 
     At block S 30 , the obtaining module  301  of the vehicle-mounted device  3  receives the updated recommendation information through the communication device  35 . The execution module  302  of the vehicle-mounted device  3  may also display the updated recommendation information on the display screen  36 . The execution module  302  of the vehicle-mounted device  3  may also use the speaker  37  to broadcast the updated recommendation information. 
     It should be noted that a difference between the intelligent recommendation methods shown in  FIG.  4    and  FIG.  5    is that the intelligent recommendation method shown in  FIG.  5    is that when it is determined that the user intention is suitable for adding the attributes of occupants in the vehicle  100 , the cloud server  4  is used to update user intention and update recommendations based on the updated user intention. Since a computing power of the cloud server  4  is greater than that of the vehicle-mounted device  3 , it can respond to the needs of the user more quickly. 
       FIG.  6    is a flowchart of a third embodiment of an intelligent recommendation method provided by present disclosure. 
     In this embodiment, the intelligent recommendation method can be applied to an environment including the vehicle-mounted device  3  and the cloud server  4 . For the vehicle-mounted device  3  and the cloud server  4  that need to perform intelligent recommendation, the functions of intelligent recommendation provided by this disclosure can be directly integrated on the vehicle-mounted device  3  and the cloud server  4 , or in form of software development kits (SDK) running in the vehicle-mounted device  3  and the cloud server  4 . 
     As shown in  FIG.  6   , the intelligent recommendation method includes the following blocks. According to different requirements, an order of the blocks in the flowchart can be changed, and some blocks can be omitted. 
     Block S 41  is the same as block S 22 , and details are not repeated here. 
     Block S 42  is the same as block S 23 , and details are not repeated here. 
     Block S 43  is the same as block S 24 , and details are not repeated here. 
     Block S 44  is the same as block S 25 , and details are not repeated here. 
     At block S 45 , the execution module  302  of the vehicle-mounted device  3  determines whether the user intention is suitable for adding the attributes of occupants in the vehicle  100 . When it is determined that the user intention is not suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 46 . When it is determined that the user intention is suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 47 . 
     The determining of whether the user intention is suitable for adding the attributes of occupants in the vehicle  100  is the same as that described in the foregoing blocks S 7  and S 26 , and thus will not be repeated here. 
     Block S 46  is the same as block S 27 , and details are not repeated here. 
     At block S 47 , the execution module  302  of the vehicle-mounted device  3  captures images of the occupants in the vehicle  100  using the camera module of the detection device  33 , and confirms the attributes of occupants in the vehicle  100  according to the captured images, and transmits the attributes of occupants in the vehicle  100  to the cloud server  4  via the communication device  35 . 
     It should be noted that, in this block S 47 , the capturing images of the occupants in the vehicle  100  using the camera module of the detection device  33 , and confirming the attributes of occupants in the vehicle  100  according to the captured images is the same as that in block S 2 , details will not be repeated here. Block S 48  is the same as block S 29 , and will not be repeated here. 
     Block S 49  is the same as block S 30 , and will not be repeated here. 
     It should be noted that a difference between the intelligent recommendation method shown in  FIG.  4    and  FIG.  6    is that the intelligent recommendation method shown in  FIG.  6    detects the attributes of occupants in vehicle  100  only when it is determined that the attributes of occupants in the vehicle  100  are suitable for adding to the user intention. Since the vehicle-mounted device  3  does not need to detect the attributes of occupants in vehicle  100  in advance, it does not need to detect the attributes of occupants in vehicle  100  when the attributes of occupants in vehicle  100  are not suitable for adding to the user intention, thus it saves data processing resources and responding to user needs more quickly. 
       FIG.  7    is a flowchart of a fourth embodiment of an intelligent recommendation method provided by present disclosure. 
     In this embodiment, the intelligent recommendation method can be applied to an environment including the vehicle-mounted device  3  and the cloud server  4 . For the vehicle-mounted device  3  and the cloud server  4  that need to perform intelligent recommendation, the functions of intelligent recommendation provided by this disclosure can be directly integrated on the vehicle-mounted device  3  and the cloud server  4 , or in form of software development kits (SDK) running in the vehicle-mounted device  3  and the cloud server  4 . 
     As shown in  FIG.  7   , the intelligent recommendation method includes the following blocks. According to different requirements, an order of the blocks in the flowchart can be changed, and some blocks can be omitted. 
     Block S 51  is the same as block S 1 , and details are not repeated here. 
     Block S 52  is the same as block S 2 , and details are not repeated here. 
     Block S 53  is the same as block S 3 , and details are not repeated here. 
     At block S 54 , the vehicle-mounted device  3  sends the voice information and the attributes of occupants in the vehicle  100  to the cloud server  4 . 
     At block S 55 , the receiving module  401  of the cloud server  4  receives the voice information through the communication device  43 . The response module  402  of the cloud server  4  obtains user intention by analyzing the voice information. 
     At block S 56 , the response module  402  of the cloud server  4  determines whether the user intention is suitable for adding the attributes of occupants in the vehicle  100 . When it is determined that the user intention is not suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 57 ; when it is determined that the user intention is suitable for adding the attributes of occupants in the vehicle  100 , the process goes to block S 58 . 
     In one embodiment, the cloud server  4  may pre-store various user intentions suitable for adding the attributes of occupants in the vehicle  100 ; if the obtained user intention matches a pre-stored user intention, it is determined that the obtained user intention is suitable for adding the attributes of occupants in the vehicle  100 ; if the obtained user intention does not match the pre-stored user intention, it is determined that the obtained user intention is not suitable for adding the attributes of occupants in the vehicle  100 . 
     In this embodiment, the various user intentions suitable for adding the attributes of occupants in the vehicle  100  may include keywords related to people such as “restaurant”, “hotel”, or “scenic spots”. 
     At block S 57 , when it is determined that the user intention is not suitable for adding the attributes of occupants in the vehicle  100 , the response module  402  of the cloud server  4  generates recommendation information based on the user intention. After block S 57  is performed, the process goes to block S 59 . 
     At block S 58 , when it is determined that the user&#39;s intention is suitable for adding the attributes of occupants in the vehicle  100 , the response module  402  of the cloud server  4  obtains the updated user intention by adding the attributes of occupants in the vehicle  100  to the user intention. The response module  402  of the cloud server  4  generates recommendation information based on the updated user intention. 
     At block S 59 , the response module  402  of the cloud server  4  sends the recommendation information to the vehicle-mounted device  3  through the communication device  43 . 
     At block S 60 , the obtaining module  301  of the vehicle-mounted device  3  receives the recommendation information through the communication device  35 . The execution module  302  of the vehicle-mounted device  3  may also display the recommended information on the display screen  36 . The execution module  302  of the vehicle-mounted device  3  may also use the speaker  37  to broadcast the recommended information. 
     In the several embodiments provided in this disclosure, it should be understood that the devices and methods disclosed can be implemented by other means. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a division according to logical function, which can be implemented in another way. 
     The modules described as separate parts may or may not be physically separate, and the parts displayed as modules may or may not be physical units, that is, may be located in one place, or may be distributed over multiple network units. Part or all of the modules can be selected according to the actual needs to achieve the purpose of this embodiment. 
     In addition, each functional unit in each embodiment of the present disclosure can be integrated into one processing unit, or can be physically present separately in each unit, or two or more units can be integrated into one unit. The above integrated unit can be implemented in a form of hardware or in a form of a software functional unit. 
     The above integrated modules implemented in the form of function modules may be stored in a storage medium. The above function modules may be stored in a storage medium, and include several instructions to enable a computing device (which may be a personal computer, server, or network device, etc.) or processor to execute the method described in the embodiment of the present disclosure. 
     The present disclosure is not limited to the details of the above-described exemplary embodiments, and the present disclosure can be embodied in other specific forms without departing from the spirit or essential characteristics of the present disclosure. Therefore, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present disclosure is defined by the appended claims. All changes and variations in the meaning and scope of equivalent elements are included in the present disclosure. Any reference sign in the claims should not be construed as limiting the claim. Furthermore, the word “comprising” does not exclude other units nor does the singular exclude the plural. A plurality of units or devices stated in the system claims may also be implemented by one unit or device through software or hardware. Words such as “first” and “second” are used to indicate names but not to signify any particular order. 
     The above description is only embodiments of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes can be made to the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and scope of the present disclosure are intended to be included within the scope of the present disclosure.