Information processing method, model generation method, computer program, and information processing device

Abstract

To provide an information processing method and the like that can provide an estimate of vehicle inspection in a short time.SOLUTION: An information processing method according to one embodiment of the present disclosure acquires vehicle information related to a vehicle subject to vehicle inspection, inputs the acquired vehicle information into a learning model that has been trained to output necessity information for items that require part replacements or inspection when the vehicle information is input, and outputs the necessity information. In addition, the vehicle information includes maintenance information related to maintenance of the vehicle subject to vehicle inspection. The maintenance information includes the number of days since the last maintenance of the vehicle subject to vehicle inspection, mileage, the number of days since first registration, the number of inspections, or information regarding part replacements.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present technology relates to an information processing method, a model generation method, a computer program, and an information processing device. [Background technology]

[0002] Traditionally, vehicle maintenance or repairs are carried out by vehicle inspection companies during vehicle inspections (shaken) conducted in accordance with the Road Transport Vehicle Act. The details of the vehicle maintenance or repairs are estimated by the inspection company based on an inspection conducted before the vehicle inspection and proposed to the customer.

[0003] For example, the system device described in Patent Document 1, when the results of the inspections performed by the inspector for each item are input, displays the results to the customer and calculates an estimated price. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-190049 Summary of the Invention [Problem to be solved by the invention]

[0005] In the system device described in Patent Document 1, an inspector must actually inspect the customer's vehicle, and it takes a lot of time to make an estimate for the vehicle inspection.

[0006] The present disclosure has been made in consideration of the above circumstances, and aims to provide an information processing method and the like that enables estimates to be made at the time of vehicle inspection in a short period of time. [Means for solving the problem]

[0007] An information processing method according to one embodiment of the present disclosure acquires vehicle information about a vehicle subject to inspection, inputs the acquired vehicle information into a learning model that has been trained to output necessity information for items that require part replacement or inspection when the vehicle information is input, and outputs necessity information.

[0008] In an information processing method according to an embodiment of the present disclosure, the vehicle information includes maintenance information related to maintenance of the vehicle subject to vehicle inspection.

[0009] In an information processing method according to one embodiment of the present disclosure, the maintenance information includes information regarding the number of days since the last maintenance of the vehicle subject to vehicle inspection, mileage, number of days since the first year of registration, number of inspections, or part replacement.

[0010] In an information processing method according to an embodiment of the present disclosure, the necessity information includes whether or not replacement or inspection of a part is necessary, the likelihood of the necessity, or a classification of the likelihood of the necessity.

[0011] An information processing method according to an embodiment of the present disclosure lists and outputs the items, the necessity information, and the main factors among the vehicle information that had the greatest influence on the output of the necessity information.

[0012] An information processing method according to one embodiment of the present disclosure outputs a command to create a first proposal for a vehicle inspection company, in which the necessity information is described, and a command to create a second proposal for a customer, which is different from the first proposal.

[0013] An information processing method according to one embodiment of the present disclosure outputs, in the second proposal, the items, the necessity information, the major factors among the vehicle information that had the greatest impact on the output of the necessity information, and a message corresponding to the items and the major factors.

[0014] An information processing method according to an embodiment of the present disclosure outputs the items for which the likelihood of the need for part replacement or inspection is equal to or greater than a predetermined value.

[0015] An information processing method according to one embodiment of the present disclosure refers to a table that stores required items that must be included in a proposal regardless of the necessity information, and includes the required items in the proposal regardless of the output of the necessity information.

[0016] An information processing method according to one embodiment of the present disclosure refers to a table that stores free items for which no fee is charged for replacement or inspection of the part, and among the items for which necessity information has been output, those items that correspond to the free items are entered in a proposal.

[0017] A model generation method according to one embodiment of the present disclosure acquires training data including vehicle information about a vehicle subject to inspection and necessity information for items that require replacement or inspection of parts of the vehicle subject to inspection, and generates a learning model that outputs the necessity information when the vehicle information is input based on the acquired training data.

[0018] In a model generation method according to one embodiment of the present disclosure, the original data for the training data is collected from store devices installed in the stores of multiple vehicle inspection companies, the training data is generated by converting the store management numbers included in the original data into common management numbers by referring to a table that stores correspondence between store management numbers assigned to each item, which differ for each store device, and common management numbers common to all stores, and the vehicle information and necessity information included in the training data are associated by the common management number.

[0019] A computer program according to one embodiment of the present disclosure acquires vehicle information about a vehicle subject to inspection, and causes a computer to execute a process of inputting the acquired vehicle information into a learning model that has been trained to output necessity information for items that require part replacement or inspection when the vehicle information is input, and outputting the necessity information.

[0020] An information processing device according to one embodiment of the present disclosure includes a processing unit that acquires vehicle information regarding a vehicle subject to inspection, inputs the acquired vehicle information into a learning model that has been trained to output necessity information for items that require part replacement or inspection when the vehicle information is input, and outputs necessity information. [Effects of the Invention]

[0021] In the information processing method according to an embodiment of the present disclosure, it is possible to make an estimate at the time of vehicle inspection in a short time. [Brief explanation of the drawings]

[0022] [Figure 1] FIG. 1 is an explanatory diagram illustrating an example of the configuration of a vehicle inspection proposal system. [Figure 2] FIG. 1 is a block diagram illustrating an example of the configuration of an information processing device. [Figure 3] FIG. 2 is a block diagram showing an example of the configuration of a store device. [Figure 4] FIG. 10 is an explanatory diagram illustrating an example of a store vehicle information table. [Figure 5] FIG. 4 is an explanatory diagram illustrating an example of a vehicle information table. [Figure 6] FIG. 10 is an explanatory diagram illustrating an example of an item table. [Figure 7] FIG. 1 is an explanatory diagram of a learning model. [Figure 8] FIG. 10 is an explanatory diagram of a learning model according to a first modified example of the first embodiment. [Figure 9] FIG. 10 is an explanatory diagram of a learning model according to a second modified example of the first embodiment. [Figure 10] FIG. 10 is an explanatory diagram showing an example of an output result display screen. [Figure 11] FIG. 10 is an explanatory diagram showing an example of a first proposal. [Figure 12] FIG. 10 is an explanatory diagram showing an example of a second proposal. [Figure 13] FIG. 10 is an explanatory diagram illustrating an example of a message table. [Figure 14] 10 is a flowchart illustrating an example of processing by an information processing device. [Figure 15] 10 is a flowchart illustrating an example of processing performed by a store device. [Figure 16] FIG. 10 is a block diagram showing an example of the configuration of a store device according to a second embodiment. [Figure 17] FIG. 10 is an explanatory diagram illustrating an example of a required item table. [Figure 18] FIG. 10 is an explanatory diagram showing an example of a store vehicle information table according to the second embodiment. [Figure 19] FIG. 10 is an explanatory diagram illustrating an example of a maintenance pack table. [Figure 20] FIG. 10 is an explanatory diagram showing an example of a second proposal according to the second embodiment. [Figure 21] 10 is a flowchart showing an example of processing performed by a store device according to the second embodiment. [Figure 22] FIG. 10 is a block diagram showing an example of the configuration of an information processing device according to a third embodiment. [Figure 23] FIG. 10 is a block diagram showing an example of the configuration of a store device according to a third embodiment. [Figure 24] FIG. 11 is an explanatory diagram showing an example of a vehicle inspection history table according to the third embodiment. [Figure 25] FIG. 10 is an explanatory diagram illustrating an example of an item master table. [Figure 26] 10 is a flowchart illustrating an example of a learning model generation processing procedure. DETAILED DESCRIPTION OF THE INVENTION

[0023] (Embodiment 1) The present invention according to a first embodiment will be described below with reference to the drawings. FIG. 1 is an explanatory diagram showing an example of the configuration of a vehicle inspection proposal system, FIG. 2 is a block diagram showing an example of the configuration of an information processing device, and FIG. 3 is a block diagram showing an example of the configuration of a store device. The vehicle inspection proposal system S includes an information processing device 1 and multiple store devices 2. The store devices 2 are devices installed in the stores of vehicle inspection companies. The information processing device 1 and the store devices 2 communicate via a network N. Each store device 2 accepts and stores input vehicle information regarding vehicles to be inspected that are owned by customers at the respective stores. The information processing device 1 acquires vehicle information from the store devices 2 and, based on the acquired vehicle information, outputs necessity information for items that require replacement or inspection (maintenance) for the vehicles to be inspected. The store device 2 acquires the necessity information output by the information processing device 1, creates a vehicle inspection proposal (vehicle inspection proposal) that includes the necessity information, and presents it to the vehicle inspection company and the customer.

[0024] The information processing device 1 is, for example, a server computer, and includes a processing unit 11, a storage unit 12, and a communication unit 13. The processing unit 11 is configured with a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphical Processing Unit), a quantum processor, or the like, and performs various control processes, arithmetic processes, and the like by reading and executing a program P (program product) and a database pre-stored in the storage unit 12. Note that a database server or the like may be provided outside the information processing device 1, and the database may be read from the database server or the like. Furthermore, the functions of the information processing device 1 may be realized by multiple server devices or computers. Furthermore, the information processing device 1 may correspond to a node on a blockchain.

[0025] The storage unit 12 of the information processing device 1 is, for example, a volatile memory and a non-volatile memory. The storage unit 12 stores a program P, a plurality of learning models M, a vehicle information table 121, and an item table 122. The program P may be provided to the information processing device 1 using a storage medium 12a on which the program P is stored in a computer-readable manner. The storage medium 12a is, for example, a portable memory. Examples of the portable memory include a CD-ROM, a USB (Universal Serial Bus) memory, an SD card, a micro SD card, and a Compact Flash Memory (registered trademark). When the storage medium 12a is a portable memory, the processing element of the processing unit 11 may read the program P from the storage medium 12a using a reading device (not shown). The read program P is written to the storage unit 12. Furthermore, the program P may be provided to the information processing device 1 by the communication unit 13 communicating with an external device. Details of the learning model M, the vehicle information table 121, and the item table 122 will be described later.

[0026] The communication unit 13 of the information processing device 1 is a communication module or communication interface for communicating with the in-store device 2 via a wired or wireless connection, such as a wide-area wireless communication module. The processing unit 11 communicates with the in-store device 2 via the communication unit 13, for example, through an external network N such as the Internet. The processing unit 11 acquires information about the customer's vehicle (vehicle information) from the in-store device 2 via the communication unit 13, and transmits output results of the learning model M to the in-store device 2. The learning model M may be executed by another computer (not shown). In addition, although the present embodiment illustrates an example in which the learning model M is executed by the information processing device 1, this is not limiting. For example, the trained learning model M may be deployed to the in-store device 2, and the in-store device 2 may output information on the necessity of items requiring replacement or inspection (maintenance).

[0027] The in-store device 2 is, for example, a server computer, a personal computer, a smartphone, or a tablet terminal installed in a store of a vehicle inspection company. The in-store device 2 includes a processing unit 21, a storage unit 22, a communication unit 23, and an input / output I / F 24. The processing unit 21 and the communication unit 13 have the same configuration as the processing unit 11 and the communication unit 13 of the information processing device 1.

[0028] The storage unit 22 of the store device 2 stores a store vehicle information table 221 and a message table 222. The store vehicle information table 221 stores vehicle information related to vehicles owned by customers in the store where the store device 2 is installed. The message table 222 stores messages to be written in a second proposal 42, which will be described later. The store vehicle information table 221 and the message table 222 will be described in detail later.

[0029] The input / output I / F 24 conforms to a communication standard such as USB or DSUB, and is a communication interface for serial communication with an external device connected to the input / output I / F 24. A display unit 241 such as a display and an input unit 242 such as a mouse and keyboard are connected to the input / output I / F 24, and the processing unit 21 outputs the results of information processing performed based on an execution command or an event input from the input unit 242 to the display unit 241. Also connected to the input / output I / F 24 is a printing device 243 that prints a proposal, which will be described later.

[0030] FIG. 4 is an explanatory diagram showing an example of a store vehicle information table. The store vehicle information table 221 stores vehicle information related to vehicles subject to vehicle inspection for customers of each store. The store vehicle information table 221 includes, for example, a customer name field, a customer number field, a registration number field, a vehicle model field, a number of days since initial registration field, a total mileage field, a number of vehicle inspections field, a number of inspections field, a number of days since inspection field, a mileage since inspection field, a maintenance information field, and a maintenance date field. The customer name field stores the name of the customer who owns the vehicle subject to vehicle inspection. The customer number field stores a number assigned to the customer. The registration number field stores the registration number printed on the license plate of the vehicle registered by the local government. The vehicle model field stores the vehicle model of the vehicle subject to vehicle inspection. The number of days since initial registration field stores the number of days elapsed since the initial registration of the vehicle subject to vehicle inspection. The total mileage field stores the mileage of the vehicle subject to vehicle inspection from the initial registration year to the present. The Number of Inspections field stores the number of inspections that have been performed on the vehicle subject to inspection from the time of its initial registration to the present. The Number of Inspections field stores the number of inspections that have been performed on the vehicle subject to inspection from the time of its initial registration to the present. Inspections are not inspections based on the Road Transport Vehicle Act, but are inspections conducted at the request of the customer. Note that inspections may also be conducted at the same time as vehicle inspections. The Mileage After Inspection field stores the distance traveled by the vehicle subject to inspection from the last inspection to the present. The Maintenance Information field stores the parts that have been previously maintained (parts replaced or inspected) on the vehicle subject to inspection. The Maintenance Date field stores the date on which the maintenance stored in the Maintenance Information field was performed. Note that the store vehicle information table 221 may contain multiple maintenance information fields and maintenance date fields.

[0031] For example, when the processing unit 21 of the store device 2 receives an input of a proposal creation instruction, the processing unit 21 inserts a new record into the store vehicle information table 221. At this time, the processing unit 21 references records already stored in the store vehicle information table 221 for the corresponding vehicle to be inspected, and creates a new record to be inserted into the store vehicle information table 221. For example, if a record with the same registration number exists in the past, the number of days since initial registration field and the number of days since inspection field are stored with the number of days calculated based on the number of days stored in the corresponding record, the date on which the corresponding record was created, and the current date. The number of inspections stored in the number of inspections field is the number of times incremented by one from the previous record with the same registration number. The number of inspections stored in the number of inspections field is the number of inspections since the initial registration of the vehicle to be inspected, which is obtained by referencing an inspection history table (not shown). The total mileage field and the mileage since inspection field may store a newly entered distance based on the display on the meter of the vehicle to be inspected.

[0032] FIG. 5 is an explanatory diagram showing an example of a vehicle information table. The vehicle information table 121 stores information about vehicles subject to vehicle inspection. The vehicle information table 121 includes, for example, a store field, a customer name field, a customer number field, a registration number field, a vehicle model field, a number of days since initial registration field, a total mileage field, a number of vehicle inspections field, a number of inspections field, a number of days since inspection field, a mileage since inspection field, and a replacement parts field. The store field stores the name of the store that performs the vehicle inspection. The other fields are the same as those included in the store vehicle information table 221, and store vehicle information acquired by the processing unit 11 of the information processing device 1 from the memory unit 22 of the store device 2.

[0033] FIG. 6 is an explanatory diagram showing an example of an item table. The item table 122 stores items for which necessity information is output. The item table 122 includes, for example, an item number field, a type field, and an item field. The item number field stores a number assigned to each item. The type field stores the type to which each item belongs. The item field stores items that may require maintenance during vehicle inspection. In this embodiment, the items are classified into three types: regularly replaced parts, functional parts, and value-added products.

[0034] 7 is an explanatory diagram of the learning model M. The learning model M is a machine learning model that takes vehicle information of a vehicle subject to vehicle inspection as input data and outputs necessity information for each item stored in an item table. In this embodiment, the necessity information is output as the likelihood that each item will require maintenance at the time of vehicle inspection.

[0035] A plurality of learning models M are stored for each item. That is, a learning model M is stored that outputs necessity information for each item, such as a first learning model M that uses vehicle information as input data and outputs information on whether a battery change is necessary, a second learning model M that outputs information on whether a tire change is necessary, and a third learning model M that outputs information on whether an engine oil change is necessary. Each learning model M has the same configuration.

[0036] The learning model M is constructed using, for example, LGBoost (Light Gradient Boosting). LGBoost is an ensemble learning method that combines gradient boosting and random forests. The learning model M is configured to construct multiple decision trees (weak learners) and perform boosting, which uses information from the previous decision tree to construct a new decision tree. Specifically, a new decision tree is constructed using the error (gradient of the loss function) that could not be predicted by the previous decision tree as the objective variable. In each decision tree, input data is classified according to conditions on the way from the root to the branches, and when it reaches the terminal leaf node, the value assigned to that terminal leaf node is output as the predicted value.

[0037] The input data of the learning model M is vehicle information. The vehicle information is information stored in each field of the vehicle information table 121. In this embodiment, the input data is information stored in the following fields of the vehicle information table 121: a vehicle type field, a number of days since initial registration field, a total mileage field, a number of vehicle inspections field, a number of inspections field, a number of days since inspection field, a mileage since inspection field, a maintenance information field, and a maintenance date field. The vehicle information that serves as input data of the learning model M may include customer information such as the age, gender, residential area, license classification, or information about insurance coverage of the customer who owns the vehicle subject to inspection. Alternatively, only vehicle usage information including the number of days since initial registration, total mileage, number of days since inspection, and mileage since inspection may be used as input data of the learning model M. However, the input data of the learning model M is not limited to this, and only a portion of the information included in the vehicle information may be used as input data.

[0038] When vehicle information is input, the learning model M outputs the likelihood that maintenance will be required at the time of vehicle inspection for each item. The likelihood value ranges from 0 to 1.

[0039] The output of the learning model M may be a classification result of whether each item is necessary (necessary or unnecessary). FIG. 8 is an explanatory diagram of the learning model M according to a first modified example of the first embodiment. In this example, when vehicle information is input, the learning model M outputs the accuracy for each class of necessary or unnecessary. The learning model M can output the class whose accuracy is equal to or greater than a threshold value as the output value.

[0040] Furthermore, the learning model M may be a multi-class classification model that classifies the level of likelihood that maintenance is required for each item at the time of vehicle inspection. FIG. 9 is an explanatory diagram of the learning model M according to a second modification of the first embodiment. In this example, the learning model M outputs a level according to the likelihood, such as a first level when the likelihood is equal to or greater than 0 and less than 0.25, a second level when the likelihood is equal to or greater than 0.25 and less than 0.5, a third level when the likelihood is equal to or greater than 0.5 and less than 0.75, and a fourth level when the likelihood is equal to or greater than 0.75 and less than 1. The learning model M according to the second modification includes a feature extraction layer m1 and multiple output layers m2. Each output layer m2 outputs a likelihood level for each item.

[0041] Although the above describes an example in which the learning model M is LGBoost, the configuration of the learning model M is not limited as long as it can output the likelihood that maintenance will be required at the time of vehicle inspection for each item. The learning model M may be, for example, a neural network such as a Transformer, a Convolution Neural Network (CNN), a Recurrent Neural Network (RNN), or a Long Short Term Memory (LSTM), or may use other learning algorithms such as a support vector machine, a logistics regression, a random forest, or XGBoost (eXtreme Gradient Boosting). Furthermore, the input data to the learning model M may be time-series data including vehicle information previously registered in the vehicle information table 121.

[0042] The processing unit 11 calculates the contribution of each piece of information included in the vehicle information input to the learning model M, thereby identifying the information (main factor) that contributes most to the output likelihood. The method for calculating the contribution is not limited, but may use, for example, SHAP (Shapley Additive exPlanation), LIME (Local Interpretable Model-Agnostic Explanations), or an attention mechanism. The greater the contribution of information, the greater the influence it has on the output likelihood.

[0043] 10 is an explanatory diagram showing an example of an output result display screen. The processing unit 11 of the information processing device 1 transmits the necessity information for each item and the identified main factors output by the learning model M to the store device 2. The store device 2 displays the necessity information for each item and the identified main factors acquired from the information processing device 1 on the display unit 241. The output result display screen 3 includes a vehicle information field 31, a previous vehicle inspection and maintenance field 32, a most recent maintenance field 33, a recommended vehicle inspection and maintenance field 34, and a command field 35.

[0044] The vehicle information field 31 stores vehicle information of the vehicle subject to inspection that was input data for the learning model M. The previous inspection maintenance field 32 displays the details, date, and fee of maintenance performed at the previous inspection of the vehicle subject to inspection. The details written in the previous inspection maintenance field 32 are read from a vehicle inspection history table (not shown) that stores the history of vehicle inspections of the vehicle, and is stored in the memory unit 22 of the store device 2. The most recent maintenance field 33 displays the maintenance information that is closest to the current date among the maintenance information stored in the maintenance information field of the store vehicle information table 221.

[0045] The recommended vehicle inspection and maintenance field 34 displays the items for which the information processing device 1 has output information on whether the items are necessary, the item numbers of the items, the main causes identified by the information processing device 1, and the maintenance recommendation level. For example, the maintenance recommendation level based on the likelihood output by the learning model M is indicated by the number of stars. In this example, if the likelihood is equal to or greater than 0 and less than 0.25, 0 stars are displayed; if the likelihood is equal to or greater than 0.25 and less than 0.5, 1 star is displayed; if the likelihood is equal to or greater than 0.5 and less than 0.75, 2 stars are displayed; and if the likelihood is equal to or greater than 0.75 and less than 1, 3 stars are displayed. For example, each item is displayed in descending order of recommendation level for each type of item.

[0046] The command field 35 includes a first proposal creation command 351 and a second proposal creation command 352. Details of the first proposal 41 and the second proposal 42 will be described later; the first proposal 41 is a proposal to be presented to the vehicle inspection company, and the second proposal 42 is a proposal to be presented to the customer. When the first proposal creation command 351 is selected, the processing unit 21 of the in-store device 2 creates the first proposal 41 and causes the printing device 243 to print it. The same is true when the second proposal creation command 352 is selected. The first proposal 41 and the second proposal 42 may be displayed on the display unit 241 of the in-store device 2 or on a smartphone or the like owned by the customer.

[0047] FIG. 11 is an explanatory diagram showing an example of a first proposal. The first proposal 41 includes a vehicle information field 411, a previous vehicle inspection and maintenance field 412, a most recent maintenance field 413, and a recommended vehicle inspection and maintenance field 414. That is, the first proposal 41 contains the same content as that displayed on the output result display screen 3, except for the command field 35. The recommended vehicle inspection and maintenance field 414 may also contain the date of the previous maintenance for each item. In this case, if a corresponding item exists in the maintenance information field of the shop vehicle information table 221, the maintenance date of the corresponding item is entered. Furthermore, items with a low level of recommendation may not be entered in the recommended vehicle inspection and maintenance field 414. For example, items with zero stars displayed on the output result display screen 3 may not be entered in the first proposal 41. The first proposal 41 is printed as a proposal for the vehicle inspection company.

[0048] FIG. 12 is an explanatory diagram showing an example of the second proposal, and FIG. 13 is an explanatory diagram showing an example of a message table. The second proposal 42 includes a vehicle information field 421, a recommended vehicle inspection and maintenance field 424, and a message field 425. The contents of the vehicle information field 421 and the recommended vehicle inspection and maintenance field 424 are the same as the contents of the vehicle information field 411 and the recommended vehicle inspection and maintenance field 414 of the first proposal. Note that, like the recommended vehicle inspection and maintenance field 414 of the first proposal 41, the recommended vehicle inspection and maintenance field 424 of the second proposal 42 may include the date of the last maintenance for each item. Furthermore, items with a low level of recommendation may not be included in the recommended vehicle inspection and maintenance field 424. For example, items with zero stars displayed on the output result display screen 3 may not be included in the second proposal 42. The second proposal 42 is printed as a proposal for the customer.

[0049] The message field 425 contains messages encouraging the customer to replace or inspect parts for multiple items, in descending order of the maintenance recommendation level. The messages to be contained in the message field 425 are selected by referring to the message table 222 based on the items for which necessity information has been output and the identified major factors. As shown in FIG. 13 , the message table 222 includes, for example, an item field, a major factor field, and a message field. The item field stores maintenance items. The major factor field stores vehicle information that may be major factors. The message field stores messages to be contained in the message field 425 of the second proposal for the items and major factors.

[0050] When the second proposal creation command 352 is selected, the processing unit 21 of the in-store device 2 determines the items to include in the message in the second proposal 42. In the example shown in FIG. 12, it determines to include messages for three items with high maintenance recommendation levels (wiper replacement, air filter replacement, and V-belt replacement). Next, the processing unit 21 of the in-store device 2 references the message table and identifies the items to include in the message and a message based on the main factors. The processing unit 21 creates the second proposal 42, in which the determined items and the identified messages are listed in the message field 425, and causes the printing device 243 to print it. In the example shown in FIG. 12, for the item "wiper replacement," the main factor that increased the maintenance recommendation level (likelihood level) was identified as "number of days since inspection." Therefore, the processing unit 21 references the message table 222 and determines the message to include in the second proposal 42 as "Several days have passed since the inspection. Uneven wiping increases eye fatigue. Would you like to replace it now?" This can increase the customer's motivation to perform maintenance. The message written in the second proposal 42 may be specified based on the season in which the vehicle inspection is to be performed or on customer information. The processing unit 21 may also refer to a fee table (not shown) that stores the amount required for maintenance for each item, and write in the second proposal 42 an estimated amount that is the sum of the amount required for maintenance for items whose maintenance recommendation level is equal to or higher than a certain level.

[0051] FIG. 14 is a flowchart illustrating an example of processing by the information processing device 1. For example, when vehicle information on a vehicle to be inspected is transmitted from the store device 2, the processing unit 11 of the information processing device 1 starts the following processing. The processing unit 11 receives the vehicle information on the vehicle to be inspected from the store device 2 (S1). The processing unit 11 stores the vehicle information received from the store device 2 in the vehicle information table 121 (S2). The processing unit 11 inputs the vehicle information on the vehicle to be inspected into the learning model M (S3) and outputs necessity information for each item requiring maintenance (S4). The processing unit 11 calculates the contribution of each piece of information included in the vehicle information input in S4 to the output of the necessity information (S5), and identifies major factors in the vehicle information based on the calculated contribution (S6). The processing unit 11 transmits the necessity information output in S4 and the major factors identified in S6 to the store device 2 (S7), and ends the processing.

[0052] FIG. 15 is a flowchart illustrating an example of processing by a store device. The processing unit 21 of the store device 2 reads the latest record from the store vehicle information table 221 of past records related to the vehicle to be inspected based on the registration number (S11). The processing unit 21 updates the vehicle information stored in the read record (S12) and inserts the new record into the store vehicle information table 221 (S13). The processing unit 21 transmits the vehicle information to be stored in the new record to the information processing device 1 (S14). The processing unit 21 receives necessity information and major factors for each item from the information processing device 1 (S15). The processing unit 21 displays the necessity information and major factors received from the information processing device 1 for each item on the output result display screen 3 (S16). Note that in S16, items with a low degree of recommendation do not need to be displayed on the output result display screen 3. The processing unit 21 reads vehicle information and maintenance information from the store vehicle information table 221 (S17). The processing unit 21 also reads out the vehicle inspection history from the vehicle inspection history table (S18). The processing unit 21 displays the vehicle information, the vehicle inspection history, and the maintenance information on the output result display screen 3 (S19). The processing unit 21 also displays the first proposal creation command 351 and the second proposal creation command 352 in the first proposal 41 (S20). The processing unit 21 accepts the selection of the first proposal creation command 351 (S21) and creates the first proposal 41 (S22). Specifically, the processing unit 21 creates the first proposal 41 by filling in the vehicle information field 411, the previous vehicle inspection maintenance field 412, the most recent maintenance field 413, and the recommended vehicle inspection maintenance field 414. The processing unit 21 then causes the printing device 243 to print the first proposal 41 (S23). The processing unit 21 accepts the selection of the second proposal creation command 352 (S24) and determines the items in which to write messages in the second proposal 42 based on the maintenance recommendation level of each item (S25). The processing unit 21 reads the message table 222 (S26) and determines a message to be included in the second proposal 42 based on the items determined in S25 and the main factors for those items (S27). The processing unit 21 creates the second proposal 42 (S28). Specifically, the processing unit 21 creates the second proposal 42 by filling in the vehicle information column 421, the recommended vehicle inspection and maintenance column 424, and the message column 425. The processing unit 21 causes the printing device 243 to print the second proposal 42 (S29), and the processing ends.

[0053] According to the above process, a proposal for maintenance items can be created without an inspector inspecting the vehicle to be inspected, making it possible to quickly prepare estimates for vehicle inspections. Furthermore, the content of maintenance item proposals by vehicle inspection companies can be standardized, reducing missed proposals and variations. In this embodiment, the information processing device 1 outputs necessity information using the learning model M, and the store device 2 creates the proposal. However, this is not limited to this. The information processing device 1 and the store device 2 may be configured as an integrated device, or the functions of the information processing device 1 and the store device 2 may be realized by a single device.

[0054] (Embodiment 2) The present invention according to a second embodiment will be described below with reference to the drawings. The same components as those in the first embodiment are designated by the same reference numerals, and detailed description thereof will be omitted. The store device 2 according to the second embodiment describes required items in the second proposal 42 regardless of the necessity information. Furthermore, the store device 2 according to the second embodiment describes in the second proposal 42 items corresponding to free items for which no fee is charged for part replacement or inspection (maintenance), among the items for which necessity information has been output. FIG. 16 is a block diagram showing an example of the configuration of a store device according to the second embodiment. The storage unit 22 of the store device 2 according to the second embodiment stores a required item table 223 and a maintenance package table 224.

[0055] 17 is an explanatory diagram showing an example of a required item table. The required item table 223 includes, for example, a vehicle type field and a required item field. The vehicle type field stores the vehicle model name. The required item field stores items that must be included in the second proposal 42 for the vehicle model of the vehicle to be inspected. Note that the required item field may store multiple items, and the required item table 223 may include multiple required item fields.

[0056] FIG. 18 is an explanatory diagram showing an example of a store vehicle information table according to the second embodiment. The store vehicle information table 221 according to the second embodiment includes a maintenance pack field. A maintenance pack is a package product that a customer subscribes to when purchasing a vehicle, etc., and allows subsequent replacement or inspection of parts free of charge. There are multiple types of maintenance packs, and the maintenance pack field of the store vehicle information table 221 stores the type of maintenance pack.

[0057] 19 is an explanatory diagram showing an example of a maintenance pack table. The maintenance pack table 224 includes a maintenance pack field and a plurality of free item fields. The maintenance pack field stores the type of maintenance pack. The free item field stores items that are free of charge for parts replacement or inspection (maintenance) for each type of maintenance pack.

[0058] FIG. 20 is an explanatory diagram illustrating an example of a second proposal according to the second embodiment. The second proposal 42 according to the second embodiment includes a required item field 426 and a maintenance package field 427. The required item field 426 lists required items for the vehicle model of the vehicle to be inspected, identified by referring to the required item table. The required item field 426 also lists recommended required items for the vehicle model of the vehicle to be inspected. The maintenance package field 427 lists the type of maintenance package subscribed to by the customer who owns the vehicle to be inspected, identified by referring to the store vehicle information table 221, and free items. The processing unit 21 may highlight free items among the items listed in the recommended inspection maintenance field 424. The processing unit 21 may also enter an estimated amount in the second proposal 42 that is an estimate of the amount required for maintenance of only those items that are not free items among items with a maintenance recommendation level equal to or higher than a certain level. The processing unit 21 may also enter an estimated amount in the second proposal 42 that includes the amount required for maintenance of the required items.

[0059] FIG. 21 is a flowchart showing an example of processing of a store device according to the second embodiment. When a second proposal creation command 352 is selected on the output result display screen 3, the processing unit 21 of the store device 2 according to the second embodiment starts the following processing. The processing unit 21 reads the store vehicle information table 221 (S31) and acquires the vehicle model of the vehicle to be inspected (S32). The processing unit 21 reads the required item table 223 (S33) and identifies the required items for the vehicle model acquired in S32 (S34). The processing unit 21 reads the store vehicle information table 221 (S35) and acquires the type of maintenance package for the vehicle to be inspected (S36). The processing unit 21 reads the maintenance package table 224 (S37) and identifies free items for the type of maintenance package read in S32 (S38). The processing unit 21 enters the required items identified in S34 and the free items identified in S37 in the second proposal 42 (S39). The processing unit 21 causes the printing device 243 to print the second proposal 42 (S40), and ends the process. Note that the required items or free items may also be written in the first proposal 41.

[0060] According to the above process, recommended items and free items for which no maintenance fee is required are entered in the second proposal 42, which can increase the customer's motivation to undergo a vehicle inspection. Note that the processing unit 21 of the store device 2 may enter only one of the information related to the required items or the maintenance pack in the second proposal 42.

[0061] (Embodiment 3) Hereinafter, the present invention according to the third embodiment will be described with reference to the drawings. Among the components according to the third embodiment, the same components as those in the first embodiment will be denoted by the same reference numerals, and detailed description thereof will be omitted. In the third embodiment, the generation of the learning model M will be described.

[0062] 22 is a block diagram showing an example of the configuration of an information processing device according to embodiment 3. The storage unit 12 of the information processing device 1 according to embodiment 3 stores an item master table 123. The item master table 123 will be described in detail later.

[0063] 23 is a block diagram showing an example of the configuration of a store device according to embodiment 3. The storage unit 22 of the store device 2 according to embodiment 3 includes a vehicle inspection history table 225. Details of the vehicle inspection history table 225 will be described later.

[0064] FIG. 24 is an explanatory diagram showing an example of a vehicle inspection history table according to the third embodiment. The vehicle inspection history table 225 according to the third embodiment includes, for example, a customer name field, a customer number field, a registration number field, a vehicle inspection date field, and a plurality of performed maintenance code / operation item fields. The customer name field stores the name of the customer who owns the vehicle subject to vehicle inspection. The customer number field stores a number assigned to the customer. The registration number field stores the registration number printed on the license plate of the vehicle registered by the local government. The vehicle inspection date field stores the date on which the vehicle inspection was performed. The performed maintenance code / operation item field stores, in the upper row, a unique code assigned to each store for the maintenance performed at the time of the vehicle inspection, and in the lower row, the name of the maintenance performed at the time of the vehicle inspection. The vehicle inspection history table 225 stores data on the items of maintenance performed by the vehicle inspection company.

[0065] FIG. 25 is an explanatory diagram showing an example of an item master table. The item master table 123 includes a store field, a store control number field, a store maintenance name field, a common control number field, and a common management item field. The store field stores the store name. The store control number field stores a code for maintenance that is uniquely assigned to each store. The store maintenance name field stores the name of the maintenance that corresponds to the store control number. The common management number field stores the item number (common management number) of the item to which the maintenance at the store belongs. The common management item field stores the item name of the item to which the maintenance at the store belongs.

[0066] FIG. 26 is a flowchart showing an example of a processing procedure for generating a learning model M. The processing unit 11 of the information processing device 1 reads vehicle information from the in-store vehicle information table 221 stored in the storage unit 22 of the in-store device 2 (S51). The processing unit 11 reads the vehicle inspection history related to the vehicle for which the vehicle information has been read from the vehicle inspection history table 225 stored in the storage unit 22 of the in-store device 2 (S52). The vehicle inspection history read from the vehicle inspection history table 225 serves as the source data for training data. The processing unit 11 of the information processing device 1 refers to the item master table and converts the store management number and store maintenance name included in the read vehicle inspection history into item numbers and item names (S53). The processing unit 11 associates the vehicle information for the inspection date included in the vehicle inspection history read in S52 with the converted vehicle inspection history to create training data (S54). In S54, the processing unit 11 associates the large amount of vehicle information read from the in-store device 2 of each store with the vehicle inspection history to create a large amount of training data. The processing unit 11 creates a learning model M based on the generated training data (S55).

[0067] Specifically, the processing unit 11 uses vehicle information as input data and optimizes each learning model M so that 1 is output for the likelihood of items included in the vehicle inspection history and 0 is output for the likelihood of items not included in the vehicle inspection history. When the learning model M is constructed using LGBoost, the processing unit 11 adjusts parameters using, for example, gradient descent to optimize (minimize) the loss function in the learning model M, and sequentially learns the outcome. Note that, in order to prevent so-called overfitting (overlearning), when optimizing the loss function, the control unit 10 does not use all explanatory variables, but instead selects and uses a randomly determined number of explanatory variables. The processing unit 11 completes learning when the loss function satisfies a predetermined criterion.

[0068] The processing unit 11 of the information processing device stores the learned learning model M in the storage unit 12 (S56), and ends the processing. The processing unit 11 executes the above-described processing for each learning model M.

[0069] The above process makes it possible to generate a learning model M that has been trained to appropriately output necessity information (likelihood) for items requiring part replacement or inspection (maintenance) when vehicle information is input. Even if maintenance items are classified uniquely for each store, they can be included in the training data by converting them into common management numbers and common management items, i.e., by performing so-called biasing. This makes it possible to generate a learning model M using vehicle inspection histories from multiple stores, thereby increasing the amount of training data and generating a more accurate learning model M. After generating the learning model M, the processing unit 11 may retrain the learning model M using a method similar to the above-described process. In this embodiment, the storage unit 12 of the information processing device 1 stores the item master table 123, and the conversion from the store management number to the common management number is performed by the processing unit 11 of the information processing device 1. However, the storage unit 22 of the store device 2 may store the item master table, and the conversion from the store management number to the common management number may be performed by the processing unit 21 of the store device 2.

[0070] (Variation) In the above-described embodiments, the vehicle inspection is described as a statutory vehicle inspection (a vehicle inspection conducted in accordance with the Road Transport Vehicle Act), but this is not limited thereto. The configurations and processes described in each embodiment may be similarly applied to any vehicle inspection or vehicle maintenance conducted by a private company.

[0071] The embodiments disclosed herein are illustrative in all respects and should not be considered limiting. The technical features described in each embodiment may be combined with one another, and the scope of the present invention is intended to include all modifications within the scope of the claims and equivalents thereto. Furthermore, independent and dependent claims described in the claims may be combined with one another in any and all combinations, regardless of the reference format. Furthermore, while the claims use a format in which a claim references two or more other claims (multiple claim format), this is not limiting. Multiple claims (multiple multiple claims) that reference at least one other multiple claim may also be used. [Explanation of symbols]

[0072] 1. Information processing equipment 10 Control Unit 11 Processing section 12 Storage section 121 Vehicle Information Table 122 Item Table 123 Item Master Table 12a Storage medium 13 Communications Department 2 Store equipment 21 Processing section 22 Memory section 221 Store Vehicle Information Table 222 Message Table 223 Required Fields Table 224 Maintenance Pack Table 225 Vehicle Inspection History Table 23 Communications Department 24 Input / Output Interfaces 241 Display section 242 Input section 243 Printing device 3 Output result display screen 31 Vehicle information section 32 Last vehicle inspection and maintenance column 33 Latest maintenance column 34 Recommended vehicle inspection and maintenance column 35 Command column 351 First Proposal Creation Command 352 Second Proposal Creation Command 41 First proposal 411 Vehicle information section 412 Last vehicle inspection and maintenance column 413 Latest maintenance column 414 Recommended vehicle inspection and maintenance column 42 Second proposal 421 Vehicle information section 424 Recommended vehicle inspection and maintenance column 425 Message field 426 Required field 427 Maintenance Pack Column M Learning Model N Network P Program S Vehicle Inspection Proposal System

Claims

1. An information processing device acquires vehicle information including the number of days since inspection and the mileage since inspection of a vehicle subject to inspection transmitted from a store device, The information processing device outputs necessity information for each item by inputting the acquired vehicle information including the number of days since inspection and the mileage since inspection into a learning model that has been trained to output information on the necessity of part replacement or inspection for each of multiple items that require replacement or inspection when vehicle information including the number of days since inspection and the mileage since inspection of the vehicle subject to inspection is input, The information processing device transmits to the store device the necessity information for each of the items to be output and the main factors in the vehicle information for each of the items identified based on the degree of contribution of the items of the learning model to the output of the necessity information; The store device displays the necessary / unnecessary information and main factors for each transmitted item on the display unit. Information processing methods.

2. The learning model is trained using training data including vehicle information including the number of days since inspection and mileage since inspection of a vehicle subject to inspection, and a vehicle inspection history for each of a plurality of types of items. The information processing method according to claim 1 .

3. An information processing device acquires vehicle information including the number of days since inspection and the mileage since inspection of a vehicle subject to inspection transmitted from a store device, The information processing device outputs necessity information for each item by inputting the acquired vehicle information including the number of days since inspection and the mileage since inspection into a learning model that has been trained to output information on the necessity of part replacement or inspection for each of multiple items that require replacement or inspection when vehicle information including the number of days since inspection and the mileage since inspection of the vehicle subject to inspection is input, The necessity information is a maintenance recommendation level based on the likelihood of each item output from the learning model, The information processing device transmits the maintenance recommendation level for each item to the store device; When the store device receives a selection of a proposal for a vehicle inspection company, the display unit displays the vehicle information and the maintenance recommendation level for each transmitted item, and when the store device receives a selection of a proposal for a customer, the display unit displays the items and messages stored in association with the items in order of the highest maintenance recommendation level in addition to the vehicle information and the maintenance recommendation level for each item. Information processing methods.

4. An information processing device acquires vehicle information including the number of days since inspection and the mileage since inspection of a vehicle subject to inspection transmitted from a store device, The information processing device outputs necessity information for each item by inputting the acquired vehicle information including the number of days since inspection and the mileage since inspection into a learning model that has been trained to output information on the necessity of part replacement or inspection for each of multiple items that require replacement or inspection when vehicle information including the number of days since inspection and the mileage since inspection of the vehicle subject to inspection is input, The information processing device transmits information about whether or not each item is necessary to the store device; The store device refers to a table in which essential items are stored in association with vehicle types, and reads out the essential items corresponding to the vehicle type of the vehicle to be inspected; The store device displays the necessary information for each transmitted item and the read-out required items on the display unit. Information processing methods.

5. An information processing device acquires vehicle information including the number of days since inspection and the mileage since inspection of a vehicle subject to inspection transmitted from a store device, The information processing device outputs necessity information for each item by inputting the acquired vehicle information including the number of days since inspection and the mileage since inspection into a learning model that has been trained to output information on the necessity of part replacement or inspection for each of multiple items that require replacement or inspection when vehicle information including the number of days since inspection and the mileage since inspection of the vehicle subject to inspection is input, The information processing device transmits information about whether or not each item is necessary to the store device; The store device refers to a table that stores free items for which no fee is required for replacement or inspection, which are determined for each customer who is the owner of the vehicle to be inspected, and reads out the free items; The store device displays the transmitted information on whether each item is necessary and the read free items on the display unit. Information processing methods.

6. Equipped with an information processing device and a store device, The information processing device acquires vehicle information including the number of days since inspection and the mileage since inspection of the vehicle to be inspected, which is transmitted from the store device; The information processing device outputs necessity information for each item by inputting the acquired vehicle information including the number of days since inspection and the mileage since inspection into a learning model that has been trained to output information on the necessity of part replacement or inspection for each of multiple items that require replacement or inspection when vehicle information including the number of days since inspection and the mileage since inspection of the vehicle subject to inspection is input, The information processing device transmits to the store device the necessity information for each of the items to be output and the main factors in the vehicle information for each of the items identified based on the degree of contribution of the items of the learning model to the output of the necessity information; The store device displays the necessary / unnecessary information and main factors for each transmitted item on the display unit. system.

7. Acquire vehicle information including the number of days since inspection and the mileage since inspection of a vehicle subject to vehicle inspection, a learning model that has been trained to output information on the necessity of parts replacement or inspection for each of a plurality of items that require replacement or inspection when vehicle information including the number of days since inspection and the mileage since inspection of the vehicle subject to inspection is input, and by inputting the vehicle information including the acquired number of days since inspection and the mileage since inspection, the learning model outputs the necessity information for each item; The display unit displays the necessity information for each output item and the main factors in the vehicle information for each item identified based on the degree of contribution that contributes to the output of the necessity information for the item of the learning model. A program that causes a computer to perform a process.

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