Patent Publication Number: US-2021192598-A1

Title: Proposal system, method, and computer-readable storage medium for storing program

Description:
BACKGROUND OF THE INVENTION 
     Field of the Invention 
     The present invention is related to a proposal system, a method, and a computer-readable storage medium for storing a program for estimating and proposing a device to be a purchase target. 
     Description of the Related Art 
     When a buyer tries to buy a new printer, because a large number of products are sold even just for household printers, it is difficult for the buyer to determine which printer, when compared against the buyer&#39;s usage situation, is the most appropriate to purchase. In Japanese Patent Laid-Open No. 2003-271801 a method for presenting a new product that should be purchased to replace the electronic product that a customer is currently using based on the usage information of the customer who is using the product is described. 
     However, a method such as in Japanese Patent Laid-Open No. 2003-271801 in which a new product is presented based only on the usage information of a customer would propose a new product on an assumption that the customer would use the product in the future as he/she used the product in the past. Accordingly, in a case where a customer is trying to purchase a new product at a timing when a usage situation changes from an existing one, it may not be possible to propose information for a new product. 
     SUMMARY OF THE INVENTION 
     The present invention provides a proposal system, a method, and a computer-readable storage medium for storing a program for proposing information as appropriate in accordance with the change of a usage situation. 
     The present invention in one aspect provides a proposal system, comprising: a first acquisition unit configured to acquire sales performance information that is related to a device; an acceptance unit configured to accept information that is related to a usage situation of a device from a customer; an inference unit configured to infer a device to be a purchase target by a learned model based on the information that is related to the usage situation of the device that was accepted by the acceptance unit and the sales performance information that was acquired by the first acquisition unit; and an output unit configured to output as proposal information a result of the inference by the inference unit. 
     By virtue of the present invention, information can be proposed appropriately in accordance with a change of a usage situation. 
     Further features of the present invention will become apparent from the following description of exemplary embodiments with reference to the attached drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a view that illustrates a configuration of a processing system. 
         FIG. 2  is a block diagram that illustrates a configuration of a server. 
         FIG. 3A  and  FIG. 3B  are external views of a printer. 
         FIG. 4  is a block diagram that illustrates a configuration of a printer. 
         FIG. 5  is a view that illustrates a software configuration of the processing system. 
         FIG. 6A  and  FIG. 6B  are views that conceptually illustrate a learning model and a learned model. 
         FIG. 7  is a view that illustrates a process of a processing system. 
     
    
    
     DESCRIPTION OF THE EMBODIMENTS 
     Hereinafter, embodiments will be described in detail with reference to the attached drawings. Note, the following embodiments are not intended to limit the scope of the claimed invention. Multiple features are described in the embodiments, but limitation is not made to an invention that requires all such features, and multiple such features may be combined as appropriate. 
     Furthermore, in the attached drawings, the same reference numerals are given to the same or similar configurations, and redundant description thereof is omitted. 
     [Configuration of Processing System] 
       FIG. 1  is a view that illustrates a configuration of a processing system  100  in the present embodiment. The processing system  100  is a proposal system that estimates a new printer to be a purchase target based on a customer&#39;s printer usage situation so far and purchase information that is related to other users who, in that printer usage situation, purchased a new printer, and proposes the result to the customer. A system  100  is a system that includes a cloud server  200 , an edge server  300 , and a device group  400 , and each device is connected via a network. In  FIG. 1 , a local area network  102  and the Internet  104  are examples of the network. The local area network  102  and the Internet  104  are networks that include a wireless communication network, a wired communication network, or both. Also, although the edge server  300  is connected to the local area network  102  in  FIG. 1 , it may be connected to the Internet  104 . 
     The device group  400  includes various devices that can perform a network connection. For example, the device group  400  includes a portable terminal  500  such as a smartphone, a printer  600 , a client terminal  401  such as a personal computer and a work station, and a digital camera  402 . However, a device that is included in the device group  400  is not limited to these types and may be a home electric appliance such as a refrigerator, a television, and an air conditioner, for example. The device group  400  is connected so that the respective devices are able to communicate with each other by the local area network  102  and can connect to the Internet  104  via a router  103  that is installed in the local area network  102 . 
     In  FIG. 1 , the router  103  is illustrated as a device that connects the local area network  102  and the Internet  104 . The router  103  may have a wireless LAN access point function that configures the local area network  102 . In such a case, each device in the device group  400  can connect to the local area network  102  by connecting to an access point by a wireless LAN aside from connecting to the router  103  by the wired LAN. For example, a configuration may be taken so that the printer  600  and the client terminal  401  are connected by the wired LAN and the portable terminal  500  and the digital camera  402  are connected by the wireless LAN. The device group  400  and the edge server  300  can communicate mutually with the cloud server  200  via the Internet  104  to which they are connected via the router  103 . 
     The edge server  300  and the device group  400  can communicate with each other via the local area network  102 . The respective devices of the device group  400  can communicate with each other via the local area network  102 . Also, the portable terminal  500  and the printer  600  can communicate by short-range wireless communication  101 . As the short-range wireless communication  101 , wireless communication that is compliant with a Bluetooth (registered trademark) standard and an NFC standard, for example, is used. Also, the portable terminal  500  is connected with a cellular network  105  and can communicate with the cloud server  200  via the cellular network  105 . 
     The configuration of the processing system  100  indicates an example, and another configuration may be used. For example, although an example in which the router  103  comprises an access point function was described above, the access point may be configured as a device that is different from the router  103 . Also, the connection between the edge server  300  and the device group  400  may be something that uses a configuration of connection aside from the local area network  102 . For example, the connection may be something that uses wireless communication such as near-field wireless communication, Bluetooth, ZigBee, and a LPWA aside from a wireless LAN; a wired connection such as a USB; infrared communication; and the like. 
     [Configuration of Server] 
       FIG. 2  is a block diagram that illustrates a configuration of the cloud server  200  and the edge server  300 .  FIG. 2  illustrates a hardware configuration that is common between the cloud server  200  and the edge server  300 . The cloud server  200  and the edge server  300  include a main board  210  that performs the comprehensive control of each device as a whole, a network connection unit  201 , and a hard disk unit  202 . 
     A CPU  211  in the form of a microprocessor that is arranged on the main board  210  operates based on a control program and data that are stored respectively in a program memory  213  and a data memory  214  to which the CPU  211  is connected via an internal bus  212 . The operation of each device (the cloud server  200  and the edge server  300 ) in the present embodiment is achieved by the CPU  211  executing a program that is stored in the program memory  213  with reference to data that is stored in the data memory  214 , for example. 
     The CPU  211  connects to a network such as the Internet  104  and the local area network  102  by controlling the network connection unit  201  via a network control circuit  215  and then performs communication with another device. The CPU  211  executes data read/write operations to and from the hard disk unit  202  to which the CPU  211  is connected via a hard disk control circuit  216 . In the hard disk unit  202 , an operating system (OS) that is loaded in the program memory  213  and then executed, control software of the cloud server  200  and the edge server  300 , and various data are stored. Also, in the hard disk unit  202  of the edge server  300 , a learned model  352  that will be described later is stored. Also, in the hard disk unit  202  of the cloud server  200 , a learning model  252  that will be described later is stored. The CPU  211  performs display of various user interfaces and of a state of a device on an operation unit  220  by controlling an operation unit control circuit  218  and accepts an operation from a user. 
     A GPU  217  can execute various computation processing in place of the CPU  211 . Because the GPU  217  can execute efficiently the calculation of parallel processing of data, in a case where deep learning is performed using a mathematical model such as a neural network, for example, processing is executed by the GPU  217 . In the present embodiment, in the learning processing by a learning unit  251  that will be described later, the CPU  211  and the GPU  217  are used. For example, in a case where a learning program that includes the learning model is executed, learning is performed by the CPU  211  and the GPU  217  cooperating to perform a calculation. However, in the processing of the learning unit  251 , configuration may be such that the processing is performed only by the CPU  211  or the GPU  217 . Also, regarding an inference (estimation) unit  351  that will be described later, the CPU  211  and the GPU  217  may be used as in the learning unit  251 . 
     Also, the configuration in  FIG. 2 , although described to be common between the cloud server  200  and the edge server  300 , is not limited to a common configuration. For example, a configuration may be taken so that although the GPU  217  is provided in the cloud server  200 , the GPU  217  is not provided in the edge server  300  or a configuration may be taken so that the GPU  217  of a different capability is provided. 
     [Appearance of Printer] 
       FIG. 3A  and  FIG. 3B  are external views of the printer  600 . As the printer  600 , a multi-function printer (MFP) that achieves integrally a scanner and another function, for example, is used.  FIG. 3A  illustrates an overall external view of the printer  600 . A document plate  601  is a transparent glass platform, and a document is placed thereon when performing a read by a scanner. A document cover  602  is a cover for pressing the document to the document plate so that the document does not lift up and external light does not enter the scanner unit when performing the read by the scanner. A printing sheet insertion port  603  is an insertion port for setting sheets of various sizes. The sheets that are set in the printing sheet insertion port  603  are conveyed one at a time to a printer unit (not illustrated), desired printing is performed, and the sheets are discharged from a printing sheet discharging port  604 . 
       FIG. 3B  illustrates an external view of the top face of the printer  600 . On the upper portion of the document cover  602  an operation panel  605  and a short-range wireless communication unit  606  are arranged. The short-range wireless communication unit  606  is a unit for performing short-range wireless communication, and short-range wireless communication with a short-range wireless communication unit of a communication partner within a predetermined distance is possible. A wireless LAN antenna  607  is an antenna for connecting with the local area network  102  using the wireless LAN and then performing communication. 
     [Configuration of Printer] 
       FIG. 4  is a block diagram that illustrates a configuration of the printer  600 . The printer  600  includes a main board  610  that performs the comprehensive control of the device as a whole, a wireless LAN unit  608 , the short-range wireless communication unit  606 , and the operation panel  605 . A CPU  611  in the form of a microprocessor that is arranged on the main board  610  operates based on a control program and data that are stored respectively in a program memory  613  and a data memory  614  to which the CPU  611  is connected via an internal bus  612 . A program memory  613  and a data memory  614  are configured by a ROM, a RAM, and the like, for example. The operation of the printer  600  in the present embodiment is achieved by a CPU  611  executing a control program that is stored in the program memory  613  and data that is stored in the data memory  614 , for example. 
     The CPU  611  controls a scanner unit  615  to read a document and stores the data in an image memory  616  within the data memory  614 . Also, the CPU  611  prints on a recording medium such as a sheet, image data of an image memory  616  within the data memory  614  by controlling a printer unit  617 . The CPU  611  performs wireless LAN communication with another communication terminal apparatus by controlling the wireless LAN unit  608  via a wireless LAN communication control unit  618 . Also, the CPU  611  detects a connection with another short-range wireless communication terminal and performs the transmission/reception of data to and from another short-range wireless communication terminal by controlling the short-range wireless communication unit  606  via a short-range wireless communication control circuit  619 . 
     The CPU  611  performs display of state displays and function selection menus of the printer  600  on an operation panel  605  by controlling an operation unit control circuit  620  and accepts operations from a user. A backlight is set in the operation panel  605 , and the CPU  611  controls to turn on and off the backlight via the operation unit control circuit  620 . For example, the CPU  611  controls to turn off the backlight in order to reduce the power consumption of the printer  600 . A storage unit  621  stores job information of a job that was inputted into the printer  600 . Job information that is stored in the storage unit  621  will be described later. 
     [Software Configuration] 
       FIG. 5  is a view that illustrates a software configuration of each device of the processing system  100 .  FIG. 5  illustrates those among a software configuration that are related to learning and inference processing in the present embodiment. 
     The cloud server  200  includes each module of a learning data generation unit  250 , the learning unit  251 , and a learning model  252  which are achieved by the CPU  211  executing a program, for example. A learning data generation unit  250  is a module that generates learning data that the learning unit  251  can process from information that was received from an external unit. Learning data is a data set of input data X of the learning model  252  and supervised data T that indicates a correct answer of a learning result. The learning unit  251  performs training using the learning data received from the learning data generation module  250  and the learning model  252 . In the present embodiment, discriminative deep learning is performed using the learning model  252  that is configured as a neural network. The learning unit  251  classifies input data by optimizing combine-weighting coefficients between each node of the neural network. The learning model  252  for which learning was performed is distributed as a learned model to the edge server  300  and then used in inference in the edge server  300 . 
     The edge server  300  includes each module of a data collection/provision unit  350 , an inference unit  351 , and a learned model  352  which are achieved by the CPU  211  executing a program, for example. The data collection/provision unit  350  transmits to the cloud server  200  information that was received from the device group  400  and information that was generated by the edge server  300  as information to be used in learning in the cloud server  200 . The inference unit  351  executes inference using the learned model  352  based on information that is inputted via an operation unit  218 . The inference unit  351  can execute inference using the learned model  352  based on information that is transmitted from the device group  400 . In such a case, it is possible to transmit to the device group  400  the result of the inference thereof. 
     The input data X of the learned model  352  is generated from device information that is transmitted from the device group  400  or information that is inputted via the operation unit  218 . The learned model  352  is used in inference that is performed in the edge server  300 . The learned model  352  is configured as a neural network similarly to the learning model  252 . However, the learned model  352  is generated based on the learning model  252  that was distributed from the cloud server  200 . At that time, the learned model  352  may be generated based on all the layers or the unit configuration of the learning model  252  or may be generated based on a portion of a configuration among the learning model  252  that is necessary for inference in the edge server  300 . 
     The device group  400  includes each module of an application unit  450  and a data transceiver unit  451  that are achieved by the CPU  611 , for example, executing a program. The application unit  450  is a module that achieves various functions that are executed in the device group  400  and is a module that is able to use the mechanism of learning/inference by deep learning. The data transceiver unit  451  is a module that performs data transmission/reception to and from the edge server  300 . In the data that is transmitted/received to and from the edge server  300 , a learning or inference request that is related to the edge server  300  and information that is related to the printer usage situation which will be described later are included. 
     For example, the data transceiver unit  451 , during learning, transmits to the data collection/provision unit  350  of the edge server  300  information that is to be used for learning in accordance with a request from the application unit  450 . The above information that is related to the printer usage situation is transmitted as information that is to be used for learning. Also, during inference, information that is to be used for inference is transmitted to the edge server  300  in accordance with a request from the application unit  450 , and when the result of that inference is received, the result of that inference is transferred to the application unit  450 . For example, the application unit  450  of the printer  600  performs display of a screen on the operation panel  605  based on the result of inference. Also, the result of inference may be displayed on the operation unit  218  of the edge server  300 . 
     Note that in the present embodiment, although an embodiment is described in which the learning model  252  for which learning was performed in the cloud server  200  is distributed, as the learned model  352 , to the edge server  300  and then used in the inferencing, the present embodiment is not limited to such a configuration. A configuration may also be taken so as to decide, in accordance with the hardware resource distribution and calculation amount and a data communication amount, in which of the cloud server  200 , the edge server  300 , and a device  400  to execute each of learning and inference. Alternatively, a configuration may also be taken so as to dynamically change, in accordance with the resource distribution and calculation amount and a data communication amount, in which of the cloud server  200 , the edge server  300 , and a device  400  to execute each of learning and inference. By configuring the entities that perform learning and inference to be different, it becomes possible to reduce the size of a learned model that is used on the side of the device that performs inference and to accelerate processing. 
     [Learning Model] 
       FIG. 6A  and  FIG. 6B  are views for describing input/output data for when generating the learning model  252  and the learned model  352 .  FIG. 6A  illustrates the relationship between the learning model  252  and the input/output data during learning. The input data X is data that is inputted into the input layer of the learning model  252 . As a result of the input data X being recognized by the learning model  252 , output data Y is outputted. During learning, the supervised data T is provided as correct answer data of an output result of the input data X, and a deviation amount L between the output data Y and the supervised data T is acquired from a loss function. By an error backpropagation method, a combine-weighting coefficient between the nodes of a neural network and the like that are configured as the learning model  252  are updated so that the deviation amount L becomes small. The error backpropagation method is one of the methods that adjusts a combine-weighting coefficient between the nodes of a neural network and the like so that the deviation amount L becomes small. Although in the present embodiment, the configuration of deep learning in which the neural network is used is described, another algorithm may be used in order to achieve the operation of the present embodiment. For example, the nearest neighbor method, the naive Bayes method, the decision tree, the support vector machine which are algorithms of machine learning may be used. 
       FIG. 6B  illustrates the relationship between the learned model  352  and the input/output data during inference. The input data X is data that is inputted into the input layer of the learned model  352 . As a result of the input data X being recognized by the learned model  352 , output data Y is outputted. Although the learned model  352  during inference is configured as a neural network similarly to the learning model  252 , it may be configured as a model for which a portion that is necessary for inference was extracted from the learning model  252 . By such a configuration, it is possible to reduce the size of the learned model  352  and shorten processing time during inference. 
     Hereinafter, an example of a use case of the present embodiment will be described. In a use case, the cloud server  200  and the edge server  300  in  FIG. 1 , for example, are arranged in a consumer electronics store, for example, and the device group  400  and the router  103  are arranged in a company or a residence. Also, in this use case, the edge server  300  is connected to the Internet  104  rather than to the local area network  102 . In the present embodiment, in the consumer electronics store, in addition to the printer usage situation so far of a customer that desires to purchase a new printer, purchase information (information of a purchased device) that is related to another user who, in that printer usage situation, purchased a new printer is considered. 
     A proposal of a new printer based only on the customer&#39;s printer usage situation so far would assume that the customer will continue the tendency of that usage situation in the future. However, in a case where a printer that has a revolutionary function has come out on the market, for example, it is expected that there will be a large number of users who will be motivated to purchase that printer even if the usage situation will be different from their printer usage situation so far. In such a case, if a proposal is made based only on the customer&#39;s printer usage situation so far, the proposed printer would not necessarily be the printer that has the above revolutionary function and may not be an appropriate proposal for the customer. 
     Accordingly, the present embodiment, based on a customer&#39;s printer usage situation so far and purchase information that is related to other users who, in that printer usage situation, purchased a new printer, and proposes a new printer to the customer. By virtue of such a configuration, it is possible to achieve a more appropriate proposal in which a current product trend is reflected when proposing a printer to a customer. 
       FIG. 7  is a flowchart that illustrates processing that is executed in the processing system of the present embodiment. Hereinafter, an operation of a processing system of the present embodiment will be described while referring to  FIG. 7 . The processing of the printer  600  in  FIG. 7  is achieved by the CPU  611  executing a program that was stored in the program memory  613 , for example. Also, each of the processes of the cloud server  200  and the edge server  300  is achieved by the CPU  211  or the GPU  217  of each device (GPU  217  in the description below) executing a program that was stored in the program memory  213 . Note that the user of the printer  600  is in a state in which he/she is registered as a user in the present processing system by an application that is provided from the cloud server  200  and the like. 
     In step S 101 , the CPU  611  of the printer  600 , when an execution of printing is instructed, acquires job information from the print job. Here, a case where printing is instructed includes a case where printing is instructed on the operation panel  605  of the printer  600 , a case where a print job is received via the short-range wireless communication unit  606 , and a case where a print job is received via the wireless LAN unit  608 . In other words, print jobs include a print job that was generated within the printer  600  and a print job that was received from a unit outside of the printer  600 . Job information is what is set in a print job and includes monochrome/color printing, the number of printed sheets, information of a print sheet, transmission source information of a print job, date and time information, user information, and printer identification information, for example. In step S 102 , the CPU  611  stores to the storage unit  621  the job information that was acquired in step S 101 . At that time, the job information may be grouped by user and then stored. By the processing in steps S 101  and S 102 , job information that was acquired from a print job that was executed by the printer  600  will be accumulated in the storage unit  621  of the printer  600 . 
     In step S 103 , the CPU  611 , at a predetermined timing such as when one month has elapsed, for example, acquires information that is related to the printer usage situation based on job information that was accumulated in the storage unit  621 . Then, the CPU  611  transmits to the cloud server  200  the acquired information that is related to the printer usage situation. At that time, the information that is related to the printer usage situation is transmitted together with printer identification information such as the model name of the printer  600  and user information. The information that is related to the printer usage situation is information that is aggregated from job information and includes the following information, for example.
         (1) Ratio of printing between monochrome printing and color printing   (2) Number of printed sheets within a preset period   (3) Information that is related to a position from which printing was instructed   (4) Information of a print sheet       

     Information (1) is a ratio between monochrome printing and color printing in a predetermined period in the past. Information (2) is a total number of printed sheets in a predetermined period in the past. Information (3) is acquired from the transmission source information of a print job and is a ratio between a print job that was instructed on the printer  600 , for example, and a print job that was instructed from a unit outside the printer  600  such as a portable terminal. Information (4) is information of a print sheet and is a ratio at which photograph paper was used, for example. Note that the information that is related to the printer usage situation may be transmitted to the cloud server  200  via the edge server  300 . 
     As described above, in the present embodiment, the cloud server  200  regularly collects from the printer  600  the information that is related to the printer usage situation as input data X for generating the learning model  252 . 
     On the other hand of steps S 101  to S 103 , in step S 104 , the GPU  217  of the edge server  300  acquires purchased printer information, which is of a printer that was actually purchased at a consumer electronics store at which the edge server  300  is arranged, and buyer information which is of the buyer thereof. Purchased printer information is a model name, for example, and is sales performance information of sales made on the store side. Also, buyer information, in a case where the buyer is already registered as a user in the present processing system, is identification information of the buyer. Meanwhile, there may be a case where the buyer is not registered as a user in the present processing system. In such a case, a configuration may also be taken so that the buyer inputs into the operation unit of the edge server  300 , by a method such as a survey, the information that is related to the above information (1) to (4) regarding the printer that he/she has been using so far. Also, a configuration may be taken so that at that time, information that is different from the information of the input data X such as information that is related to a function of a printer that a customer desires is inputted. 
     In step S 105 , the GPU  217  of the edge server  300  transmits to the cloud server  200  purchase information that includes the purchased printer information and the buyer information that were acquired in step S 104 . At that time, identification information that is used within the present processing system such as an ID number is transmitted as the buyer information. In a case where the buyer is not registered as a user in the present processing system, the above inputted information and the purchased printer information are transmitted to the cloud server  200  as the purchase information. 
     As described above, in the present embodiment, the cloud server  200  collects from the edge server  300  the purchase information as the supervised data T for generating the learning model  252 . Note that although in  FIG. 7 , step S 103  is illustrated to be at a timing after step S 105 , the timing may be reversed. 
     In step S 106 , the GPU  217  of the cloud server  200  receives the information that is related to the printer usage situation that was transmitted in step S 103 , associates that information with the user information and the printer identification information and then stores that information in a storage region of the hard disk unit  202  and the like. Also, the GPU  217  of the cloud server  200  receives and then stores the purchase information that was transmitted in step S 105  in a storage region of the hard disk unit  202  and the like. 
     In step S 107 , the GPU  217  of the cloud server  200  generates a learning data set that consists of the input data X and the supervised data T. In the generation of a learning data set, the information that is related to the printer usage situation and the purchased printer information based on user information, for example, are a learning data set. For example, assume a case where a user A who is using a printer A purchases a new printer B at a consumer electronics store at which the edge server  300  is set. In such a case, the information that is related to the printer usage situation (printer A) that corresponds to the user information that is transmitted in step S 103  and the purchased printer information (printer B) that is transmitted in step S 104  are generated as a learning data set. Also, in a case where the buyer is not registered as a user in the present processing system, the input information (information that is related to the printer usage situation inputted in step S 104 ) that was transmitted in step S 105  and the purchased printer information are generated as a learning data set. 
     In step S 107 , the GPU  217  of the cloud server  200  uses the generated learning data set to generate the learning model  252 . In step S 108 , the GPU  217  of the cloud server  200  transmits to the edge server  300  the generated learning model  252 . At that time, as described above, a portion of the generated learning model  252  may be transmitted to the edge server  300 . In step S 109 , the GPU  217  of the edge server  300  uses the transmitted learning model  252  to generate the learned model  352 . 
     As described above, in the generation of the learning model  252  and the learned model  352  in the present embodiment, the purchased printer information which is of a printer that was actually purchased based on the printer usage situation is used as the supervised data T. Accordingly, in an inference that uses the learned model  352 , the more that there is a trend for the current product that buyers of a printer in which a revolutionary new feature is implemented, for example, the higher the tendency that the information of that printer is outputted. 
     In step S 110 , the GPU  217  of the edge server  300  acquires information from a customer. The information that is acquired from a customer is the information that is related to the usage situation of the customer&#39;s current printer and is information such as the above information (1) to (4), for example. For example, a configuration may also be taken so as to accept information from a customer via the operation unit of the edge server  300 . Also, in a case where the customer is already registered as a user in the present processing system, a configuration may be taken so as to accept the identification information of the customer. Also, a configuration may also be taken so as to accept identification information of a printer that the customer is currently using. In either case, information that is related to the printer usage situation is requested in relation to the cloud server  200  based on the identification information of a customer or the identification information of a printer. The GPU  217  of the cloud server  200  transmits to the edge server  300  the requested identification information of a customer or information that is related to the printer usage situation which is associated with the identification information of a printer. Then, the GPU  217  of the edge server  300  acquires the information that is related to the printer usage situation that was transmitted from the cloud server  200 . 
     In step S 111 , the GPU  217  of the edge server  300  inputs into the learned model  352  the information acquired in step S 110  and then performs inference. Then, the GPU  217  of the edge server  300 , as a result of inference, outputs as proposal information the printer information that is classified to be in the highest probability. For example, a configuration may also be taken such that the proposal information is displayed on the operation unit  218  of the edge server  300 . 
     As described above, in the present embodiment, the generation of the learning model  252  and the learned model  352  is being performed using the information (1). Accordingly, it is possible to propose appropriately the purchasing of a color device or a monochrome printer based on the ratio between monochrome printing and color printing so far of a customer, for example. 
     Also, the generation of the learning model  252  and the learned model  352  is being performed using the information (2). Accordingly, it is possible to propose appropriately the purchasing of a printer on which a normal ink tank is provided or a printer on which a large-volume ink tank is provided based on the number of printed sheets for which printing was performed so far of the customer, for example. Note that a printer on which a large-volume ink tank is provided is a continuous ink supply system (CISS) printer, for example. 
     Also, the generation of the learning model  252  and the learned model  352  is being performed using the information (3). Accordingly, it is possible to propose as appropriate the purchasing of a printer whose coordination with a portable terminal is superior in a case where it is determined that a print instruction is often performed from a portable terminal based on positions from where the customer performed a print instruction so far, for example. 
     Also, the generation of the learning model  252  and the learned model  352  is being performed using the information (4). Accordingly, it is possible to propose as appropriate the purchasing of a printer whose image quality is high in a case where it is determined that a ratio of photography printing is high based on the information of print sheets on which the customer performed printing so far, for example. Also, it is possible to propose as appropriate a printer on which dye-based ink is provided or a printer on which pigment-based ink is provided in accordance with the respective ratios of document printing and photography printing, for example. 
     Furthermore, in the present embodiment, because the purchased printer information which is of a printer that was actually purchased based on the printer usage situation is used as the supervised data T, in addition to the above advantages, it is possible to reflect the current trend of products. Also, it is possible to propose in relation to the customer a new product that is similar to the model that was purchased by a person whose usage situation is very similar to that of the customer. As a result, it becomes possible to prevent the customer from mistakenly purchasing a model that does not suit the usage situation of the customer. 
     In the present embodiment, although the information (1) to (4) is described as information that is used as the input data X and is acquired in step S 110 , the information is not limited to those and other information may be used. For example, a configuration may also be taken so as to use as the input data X price information of a printer that is currently being used. By such a configuration, it becomes possible to propose as appropriate the purchasing of a product in which the price range of a printer that the customer is currently using is reflected. 
     Also, although the information (1) to (4) that corresponds to the input data X is described as information that is acquired in step S 110 , information that is acquired in step S 110  and information that corresponds to the input data X may be different. For example, a configuration may also be taken so as to acquire a feature that the customer desires in the future or information of an expected printer usage situation in the future as information that is acquired in step S 110 . By such a configuration, even if a change were to occur to the usage situation of a customer in the future, it becomes possible to propose a new product that considers that change. In such a case, it is possible to propose a new product based on the information of other users who are using a printer that has a feature that the customer would want in the future, for example. 
     Also, it was described that the information that is related to the printer usage situation is transmitted to the cloud server  200  without involving a user operation in step S 103 . However, the information that is related to the printer usage situation may be transmitted to the cloud server  200  by another method. For example, a configuration may also be taken so as to transmit by a user operation to the cloud server  200  via the operation panel  605  of the printer  600  or on an application on a portable terminal. 
     Also, in the present embodiment, it was described that in step S 111 , the printer information that is classified to be in the highest probability as a result of inference is outputted as proposal information. However, regarding the classification result, in a case where there is a printer of a manufacturer that is the same as that of the printer that the customer is currently using, for example, a configuration may also be taken to output that printer information with priority as proposal information. By such a configuration, the possibility that a manufacturer will have its printer purchased again will increase, and also, for the customer, because a printer that is of the same manufacturer which he/she has been using so far and trusts will be presented with priority, there will be less concern, and thus it is expected that the motivation to purchase will increase. 
     Also, for the customer, because a recommended model will be proposed by the present processing system, the customer will have less difficulty in deciding which product to buy, and thus it becomes possible to reduce the burden on the customer. Also, because the store clerk of a consumer electronics store will be able to sell goods based on clear grounds (data), it becomes possible to reduce sales behavior biased by emotion. 
     Also, in the present embodiment, an example of a case where the edge server  300  is set in the consumer electronics store and the customer that visits the store purchases a printer is described. However, the operation of the present embodiment can also be applied to a case where the customer purchases a printer on a website that is provided by the edge server  300  and the like. For example, a new product can be proposed similarly to the present embodiment by the customer inputting an identification number of the printer that he/she is currently using, and the like, on a pregenerated website that is linked with the learned model  352 , and the like. 
     Also, in the present embodiment, although a configuration that proposes a printer was described, it is not limited to a printer and may be another device. For example, it may be a device such as a camera and a smartphone or a home electric appliance such as a television. In such a case the information (1) to (4) is sufficient as long as the information is related to the usage situation of each device such as a shooting frequency, television viewing time, and the like. Also, the transmission in step S 103  does not have to be a transmission from a device and in a case of a smart house, for example, a configuration may be taken so as to detect behavior, tendency, and the like of a device or a resident by a sensor and the like and then transmit the result as IoT (Internet of Things) data. 
     OTHER EMBODIMENTS 
     Embodiment(s) of the present invention can also be realized by a computer of a system or apparatus that reads out and executes computer executable instructions (e.g., one or more programs) recorded on a storage medium (which may also be referred to more fully as a ‘non-transitory computer-readable storage medium’) to perform the functions of one or more of the above-described embodiment(s) and/or that includes one or more circuits (e.g., application specific integrated circuit (ASIC)) for performing the functions of one or more of the above-described embodiment(s), and by a method performed by the computer of the system or apparatus by, for example, reading out and executing the computer executable instructions from the storage medium to perform the functions of one or more of the above-described embodiment(s) and/or controlling the one or more circuits to perform the functions of one or more of the above-described embodiment(s). The computer may comprise one or more processors (e.g., central processing unit (CPU), micro processing unit (MPU)) and may include a network of separate computers or separate processors to read out and execute the computer executable instructions. The computer executable instructions may be provided to the computer, for example, from a network or the storage medium. The storage medium may include, for example, one or more of a hard disk, a random-access memory (RAM), a read only memory (ROM), a storage of distributed computing systems, an optical disk (such as a compact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™), a flash memory device, a memory card, and the like. 
     While the present invention has been described with reference to exemplary embodiments, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The scope of the following claims is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structures and functions. 
     This application claims the benefit of Japanese Patent Application No. 2019-229382, filed Dec. 19, 2019, which is hereby incorporated by reference herein in its entirety.