Information processing apparatus, information processing method, information providing system, machine learning device, and inference device

An equipment introduction effect analyzer as an information processing apparatus includes: an equipment-related information storage unit that holds equipment data including information on equipment and information on a function and performance included in equipment, and equipment buyer data including information on an effect obtained when an equipment buyer introduces equipment, information on a use environment of the equipment, and information on the purchased equipment; and an evaluation engine unit that generates information on an effect to be provided to a potential equipment buyer based on the information on the effect and the information on the use environment of equipment that are included in the equipment buyer data, and on the basis of information on an environment in which the potential equipment buyer uses the equipment, the equipment buyer data being acquired from the equipment buyer already purchased equipment with a function and performance desired by the potential equipment buyer.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is based on PCT filing PCT/JP2020/009467, filed Mar. 5, 2020, which claims priority to JP PCT/JP2019/010343, filed Mar. 13, 2019, the entire contents of each are incorporated herein by reference.

FIELD

The present invention relates to an information processing apparatus, an information processing method, an information providing system, a machine learning device, and an inference device, for providing information to a potential buyer considering purchasing equipment or a device.

BACKGROUND

An information providing apparatus described in Patent Literature 1 is available as a conventional information processing apparatus that provides information to a potential buyer of equipment or the like, the information being necessary for considering the purchase thereof.

The information providing apparatus described in Patent Literature 1 provides a picture obtained by imaging a state of equipment that is already sold and in operation, to another user who is planning to purchase the equipment, thereby communicating effects when the equipment is introduced and promoting the purchase thereof.

CITATION LIST

Patent Literature

SUMMARY

Technical Problem

In the invention described in Patent Literature 1, a moving image acquired from a user who has purchased equipment, information on effects of introducing the equipment, and the like are combined and provided to a new user. However, in the invention described in Patent Literature 1, data such as the moving image acquired from the user and the information on the effects of introducing the equipment is provided as is, so that it is difficult for the new user to directly determine whether to purchase the equipment from the provided data. In order to obtain really necessary information, a potential equipment buyer himself or his supporter needs to perform work such as analyzing the provided data separately.

The present invention has been made in view of the above, and an object of the present invention is to provide an information processing apparatus that can provide information necessary for a potential buyer, who is considering purchasing equipment, in a form usable by the potential buyer.

Solution to Problem

An information processing apparatus according to an aspect of the present invention includes an equipment-related information storage unit that holds equipment data and equipment buyer data, the equipment data including information on a function included in equipment and performance of the equipment, and the equipment buyer data including information on an effect obtained when an equipment buyer already purchased the equipment introduces the purchased equipment, information on a use environment of the purchased equipment, and information on the purchased equipment. The information processing apparatus further includes an evaluation engine unit that generates information on an effect to be provided to a potential equipment buyer, who is considering purchasing equipment, on the basis of the information on the effect and the information on the use environment of equipment that are included in the equipment buyer data and on the basis of information on an environment in which the potential equipment buyer uses the equipment, the equipment buyer data being acquired from the equipment buyer already purchased equipment with a function and performance desired by the potential equipment buyer.

Advantageous Effects of Invention

The information processing apparatus according to the present invention can provide the information necessary for the potential buyer, who is considering purchasing equipment, in the form usable by the potential buyer.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an information processing apparatus, an information processing method, an information providing system, a machine learning device, and an inference device according to embodiments of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to the embodiments.

First Embodiment

FIG.1is a diagram illustrating an example of a configuration of an information providing system that includes an information processing apparatus according to a first embodiment.

An information providing system100according to the present embodiment includes an equipment introduction effect analyzer10that is an information processing apparatus, a terminal21and equipment22owned by an equipment buyer20as a user who has purchased the equipment, and a terminal31owned by a potential equipment buyer30who considers the purchase of equipment to be newly introduced. The equipment22is equipment that has been purchased and introduced by the equipment buyer20. The equipment introduction effect analyzer10, the terminal21, the equipment22, and the terminal31are connected to a network1such as the Internet. The equipment introduction effect analyzer10can communicate with the terminal21, the equipment22, and the terminal31. Note that although not illustrated inFIG.1, there is a plurality of the equipment buyers20and the potential equipment buyers30. There may also be a plurality of the equipment introduction effect analyzers10, in which case data related to an equipment introduction effect held in each of the equipment introduction effect analyzers10is shared.

In the information providing system100, the equipment introduction effect analyzer10collects information on an effect of the equipment22purchased by the equipment buyer20, and in response to a request from the potential equipment buyer30, generates information required by the potential equipment buyer30on the basis of the information being held and provides the generated information thereto.

Next, the equipment introduction effect analyzer10of the information providing system100will be described. The equipment introduction effect analyzer10includes a registration service unit11, a search service unit12, an evaluation engine unit13, a matching engine unit14, and an equipment-related information storage unit15.

The registration service unit11acquires information for creating an equipment buyer data table200illustrated inFIG.2from one or both of the terminal21and the equipment22of the equipment buyer20, and registers the information in the equipment buyer data table200.FIG.2is a table illustrating an example of the equipment buyer data table200held in the equipment introduction effect analyzer10according to the first embodiment. The equipment buyer data table200is held in the equipment-related information storage unit15. The equipment buyer data table200includes information such as user201, equipment manufacturer name202, equipment name203, price204, effect205, use environment206, and comment207. Details of each information included in the equipment buyer data table200will be described later.

Moreover, the registration service unit11acquires information for creating a potential equipment buyer data table300illustrated inFIG.3from the terminal31of the potential equipment buyer30, and registers the information in the potential equipment buyer data table300.FIG.3is a table illustrating an example of the potential equipment buyer data table300held in the equipment introduction effect analyzer10according to the first embodiment. The potential equipment buyer data table300is held in the equipment-related information storage unit15. The potential equipment buyer data table300includes information such as user301and use environment302. Details of each information included in the potential equipment buyer data table300will be described later.

Moreover, the registration service unit11acquires information for creating an equipment data table400illustrated inFIG.4from an equipment manufacturer or a person who has equivalent information not illustrated inFIG.1, and registers the information in the equipment data table400.FIG.4is a table illustrating an example of the equipment data table400held in the equipment introduction effect analyzer10according to the first embodiment. The equipment data table400is held in the equipment-related information storage unit15. The equipment data table400includes information such as equipment manufacturer name401, equipment name402, list price403, function404, and performance405. Details of each information included in the equipment data table400will be described later.

Upon receiving an inquiry from the terminal31of the potential equipment buyer30, the search service unit12generates data to be provided to the potential equipment buyer30on the basis of the information held in the equipment-related information storage unit15, and transmits the data to the terminal31from which the inquiry is made. A method by which the search service unit12generates the data will be described later. Note that in the present embodiment, the registration service unit11and the search service unit12are configured separately, but may be integrated as one service.

When receiving an inquiry about an effect of each equipment from the terminal31of the potential equipment buyer30, the evaluation engine unit13refers to the effect205, the use environment206of the equipment buyer20, and the use environment302of the potential equipment buyer30which are registered in the equipment-related information storage unit15, thereby adjusting the basic units or the like, for example, and evaluating how much effect each potential equipment buyer30can receive.

On the basis of the information of the use environments206and302registered in the equipment-related information storage unit15, the matching engine unit14extracts information of the equipment buyer20who is using the equipment in a situation similar to a situation in which the equipment is to be used by the potential equipment buyer30, and selects a case that is highly effective for the potential equipment buyer30.

The equipment-related information storage unit15holds the equipment buyer data table200illustrated inFIG.2, the potential equipment buyer data table300illustrated inFIG.3, and the equipment data table400illustrated inFIG.4.

The terminal21and the equipment22of the equipment buyer20are connected to the registration service unit11of the equipment introduction effect analyzer10via the network1, so that information such as the price and the effect such as staff reduction regarding the equipment already purchased and used is transmitted from the terminal21, for example, and effects such as power consumption and takt time that can be automatically collected and calculated by a sensor or the like are transmitted from the equipment22, for example.

The terminal31of the potential equipment buyer30is a potential equipment buyer terminal, and is connected to the search service unit12of the equipment introduction effect analyzer10via the network1, thereby transmitting information such as the function, price, and effect of desired equipment, for example, and acquiring information related to the equipment such as the equipment manufacturer name and equipment name corresponding to the transmitted information from the equipment introduction effect analyzer10.

Next, each information held in the equipment-related information storage unit15of the equipment introduction effect analyzer10will be described.

First, the information included in the equipment buyer data table200illustrated inFIG.2will be described. The user201is identification information of the equipment buyer20, and is a unique identification (ID) assigned to the equipment buyer20when he uses the information providing system100. The equipment manufacturer name202is basically information indicating the equipment manufacturer, but may be information indicating the seller of the equipment. The equipment manufacturer name202is second information used in a search performed by the search service unit12. The equipment name203is an identification name such as the name, designation, or model name of the equipment, and the equipment manufacturer name202and the equipment name203serve as information for identifying single equipment. Moreover, for equipment including a combination of a plurality of pieces of equipment, the plurality of pieces of equipment can be represented by one name by giving thereto another name indicating the plurality of pieces of equipment as a whole, that is, a name indicating a group of pairs of the equipment manufacturer name and the equipment name of the respective plurality of pieces of equipment combined. The plurality of pieces of equipment may be represented by giving the model name thereto instead of the name. The equipment name203is first information used in a search performed by the search service unit12. The price204is the cost when the equipment buyer20purchases the equipment, and is not a required item because it may be desired to not disclose the price. The effect205is an effect including both good and bad aspects when the equipment is used, and is basically a quantitative expression such as energy consumption or man-hours of work, or information representing a change thereof. However, the effect205is not limited to the quantitative description such as a numerical value because there may be a case where the effect cannot be expressed quantitatively such as when a qualitative expression with a subjective view of a user is to be included. The use environment206is information on the environment in which the equipment is used, and indicates parameters affecting the cost (negative aspect) and the effect (positive aspect) of the equipment such as information on a product used in combination with the equipment as well as electricity charges, the wage of a person operating the equipment, the climate of a place where the equipment is installed, and the frequency of maintenance that affect the gradient of the running cost of the equipment. The comment207is the impression, special notes, or the like when the user uses the equipment, and is information that can be input as information not included in the effect205but to be communicated to the potential equipment buyer30, and is referred to by the potential equipment buyer30.

Next, the information included in the potential equipment buyer data table300illustrated inFIG.3will be described. The user301is identification information of the potential equipment buyer30, and is a unique ID assigned to the potential equipment buyer30when he uses the information providing system100. The use environment302is information on the environment under which the potential equipment buyer30uses the equipment he is considering purchasing, and indicates, for example, a product that is planned to be used simultaneously in combination with the equipment, electric charges per usage when the potential equipment buyer uses the equipment after purchasing the equipment, the wage of a person in charge when the equipment is operated, and the like.

Next, the information included in the equipment data table400illustrated inFIG.4will be described. Each data included in the equipment data table400is registered by the equipment manufacturer that is omitted inFIG.1. For example, the equipment manufacturer connects to the equipment introduction effect analyzer10using a terminal similar to the terminals21and31, and registers each information included in the equipment data table400. The equipment manufacturer name401is information related to the equipment manufacturer such as the name of the equipment manufacturer or an equipment manufacturer ID. The equipment name402is an identification name such as a product name or a model name of the equipment. The list price403is information on the price of the equipment. The function404is information indicating the function included in the equipment. The performance405is information indicating the performance of the equipment. The equipment name402, the list price403, the function404, and the performance405are information related to the equipment provided by the equipment manufacturer.

Next, the operation of the equipment introduction effect analyzer10will be described. Specifically, a flowchart ofFIG.5will be used to describe the operation of the equipment introduction effect analyzer10when receiving information such as the cost and effect of equipment desired by the potential equipment buyer30from the terminal31of the potential equipment buyer30, and searching for information corresponding to the received information.FIG.5is the flowchart illustrating an example of the operation of the equipment introduction effect analyzer10according to the first embodiment. Note that the present embodiment will also describe the advance preparation required before the equipment introduction effect analyzer10performs the operation illustrated inFIG.5.

When performing the operation according to the flowchart illustrated inFIG.5, the equipment introduction effect analyzer10performs in advance the preparation described below and registers information in the equipment-related information storage unit15.

That is, a person in charge from the equipment manufacturer or a person who has information on the equipment to be registered connects a terminal to the registration service unit11, and inputs each information (the equipment manufacturer name401to the performance405) to be registered in the equipment data table400illustrated inFIG.4. Upon receiving the input information, the registration service unit11registers the received information as equipment data in the equipment data table400of the equipment-related information storage unit15.

Moreover, the equipment buyer20connects the terminal21being an equipment buyer terminal to the registration service unit11and inputs each information (the user201to the comment207) to be registered in the equipment buyer data table200to the extent possible. Upon receiving the information input by the equipment buyer20, the registration service unit11registers the received information as equipment buyer data in the equipment buyer data table200of the equipment-related information storage unit15. Also, if possible, the registration service unit11automatically acquires information related to the effect of the equipment22from the equipment22of the equipment buyer20to the extent permitted, and registers the information as the effect205in the equipment buyer data table200.

Moreover, the potential equipment buyer30connects the terminal31to the registration service unit11and inputs each information (the user301and the use environment302) to be registered in the potential equipment buyer data table300. Upon receiving the information input by the potential equipment buyer30, the registration service unit11registers the received information as potential buyer data in the potential equipment buyer data table300of the equipment-related information storage unit15.

When acquiring information such as the price of the equipment or the effect of introducing the equipment, the potential equipment buyer30connects the terminal31to the search service unit12of the equipment introduction effect analyzer10via the network1, and inputs information on the equipment to be considered for purchase, specifically, the equipment manufacturer name and the equipment name when the equipment desired for purchase has already been specified, or information such as a desired function or performance when the equipment has not been specified. The terminal31transmits each input information to the search service unit12and requests a search. Note that the terminal31also transmits the identification information of the potential equipment buyer30.

Upon acquiring each information (information on desired equipment) transmitted by the terminal31of the potential equipment buyer30(step S11), the equipment introduction effect analyzer10checks whether the equipment manufacturer name and the equipment name have been acquired, that is, whether the acquired information includes the equipment manufacturer name and the equipment name (step S12). The checking processing of step S12is performed by the search service unit12. If at least one of the equipment manufacturer name and the equipment name has not been acquired (No in step S12), the equipment introduction effect analyzer10executes a first search that searches the equipment data table400held in the equipment-related information storage unit15by using information related to the function or performance of the equipment included in the acquired information, and checks whether or not corresponding equipment data is available (step S13). Specifically, the search service unit12compares the equipment data table400held in the equipment-related information storage unit15with the information related to the function or performance of the equipment acquired in step S11, and checks whether or not the equipment data including the same information as the acquired information is in the equipment data table400. At this time, the search service unit12may use the list price403to narrow down the equipment in a price range desired by the potential equipment buyer30.

If the equipment data corresponding to the desire of the potential equipment buyer30is available (Yes in step S13), the equipment introduction effect analyzer10executes a second search that searches the equipment buyer data table200held in the equipment-related information storage unit15by using each information included in the equipment data found by the search in step S13, and checks whether or not corresponding equipment buyer data is available (step S14). Specifically, the search service unit12compares the equipment buyer data table200held in the equipment-related information storage unit15with the information included in the equipment data, and checks whether or not the equipment buyer data table200includes equipment buyer data including each information (the user201, the equipment manufacturer name202, the equipment name203, and the like illustrated inFIG.2) acquired from the equipment buyer20who has purchased the equipment desired by the potential equipment buyer30. More specifically, the search service unit12checks whether or not there is equipment buyer data whose equipment manufacturer name202and equipment name203match the equipment manufacturer name401and the equipment name402included in the equipment data found by the search in step S13. Moreover, the price204may be used as the actual purchase price to narrow down the equipment in a price range desired by the potential equipment buyer30.

On the other hand, if the equipment manufacturer name and the equipment name have been acquired in step S12above (Yes in step S12), the equipment introduction effect analyzer10searches the equipment buyer data table200held in the equipment-related information storage unit15by using the equipment manufacturer name and the equipment name acquired in step S12, and checks whether or not corresponding equipment buyer data is available (step S14). Note that the processing in step S14executed following step S13is the same as the processing in step S14executed following step S12.

If the equipment buyer data corresponding to the desire of the potential equipment buyer30is available (Yes in step S14), the equipment introduction effect analyzer10searches the equipment buyer data table200held in the equipment-related information storage unit15by using the equipment manufacturer name and the equipment name derived on the basis of each information acquired in step S11, and further searches the potential equipment buyer data table300using the identification information of the potential equipment buyer30to check whether or not corresponding use environment information is available (step S15). Specifically, the search service unit12checks whether use environment information (corresponding to the use environment206illustrated inFIG.2) is registered in the equipment buyer data found by the search using the equipment manufacturer name and the equipment name in step S14, and whether the potential equipment buyer data table300includes potential buyer data in which the identification information and use environment information (corresponding to the user301and the use environment302illustrated inFIG.3) of the potential equipment buyer30are registered.

If the use environment information is available, that is, if the equipment buyer data in which the corresponding use environment information is registered and the potential equipment buyer data are available (Yes in step S15), the equipment introduction effect analyzer10extracts the effect registered in the equipment buyer data found by the search in step S14(corresponding to the effect205illustrated inFIG.2), and converts the effect using the use environment information (step S16). In step S16, the evaluation engine unit13converts the information on the effect registered in the equipment buyer data into information that suits the situation of the potential equipment buyer30by using the use environment information registered in the equipment buyer data and the use environment information registered in the potential equipment buyer data. As an example, when the use environment information includes information such as electricity charges and wage, the evaluation engine unit13for example performs processing such as calculating, on the basis of a difference between the hourly wage listed on the side of the use environment206and the hourly wage listed on the side of the use environment302, the labor costs of a person involved in the operation of the equipment when the same equipment as that used on the side of the use environment206is used on the side of the use environment302(the side of the potential equipment buyer30), and calculating, from a difference in electricity charges between the two parties, the electricity charges on the side of the use environment302based on power consumption information of the equipment data, whereby the information on the effect (the material used for consideration by the potential equipment buyer30including the cost that is a negative effect) is converted.

A specific example of the processing performed by the evaluation engine unit13in step S16will be illustrated. For certain machining equipment, it is assumed that the wage of a worker is registered as 1000 yen per hour as the use environment206in the equipment buyer data table200, and “three hours are required for the operation of one standard machining cycle” is registered as the effect205or the performance405, whereas the wage of a worker is registered as 3000 yen per hour as the use environment302in the potential equipment buyer data table300. In this case, it can be seen that when the potential equipment buyer30purchases the machining equipment, the labor cost of 9000 yen is required for one standard machining cycle. At this time, when equipment that has been previously used by the potential equipment buyer30requires four hours to perform similar machining, it is calculated that the cost of 3000 yen can be reduced per operation by replacing the equipment. If the use environment302contains information such as the service life or the number of times of machining per day, the evaluation engine unit13also considers the information and calculates an amount of annual cost reduction or cost reduction in the life cycle of the equipment, thereby being able to evaluate the number of years of the expected period for recovering the cost of the price204or the list price403of the equipment, or by how much the period falls below the life cycle of the equipment. As described above, the evaluation engine unit13converts the information on the effect acquired from the equipment buyer20into the information on the effect obtained when the potential equipment buyer30introduces the equipment, on the basis of the use environment information acquired from the equipment buyer20and the use environment information acquired from the potential equipment buyer30.

Next, the equipment introduction effect analyzer10notifies the terminal31of the potential equipment buyer30of the effect obtained by executing the conversion processing in step S16(step S17), and ends the operation. The evaluation engine unit13performs the notification processing in step S17, that is, notifies the terminal31of the potential equipment buyer30of the effect.

If determining in step S15that the use environment information is not available (No in step S15), the equipment introduction effect analyzer10notifies the terminal31of the potential equipment buyer30of the effect registered in the equipment buyer data found by the search in step S14(step S17), and ends the operation.

Moreover, if determining that the corresponding equipment data is not available in step S13(No in step S13), or that the corresponding equipment buyer data is not available in step S14(No in step S14), the equipment introduction effect analyzer10notifies the terminal31of the potential equipment buyer30that the corresponding data is not available (step S18), and ends the operation. The search service unit12performs the notification processing in step S18, that is, the processing of notifying the terminal31that the corresponding data is not available.

Next, the effects of the equipment introduction effect analyzer10according to the present embodiment (effect of the present embodiment) will be described. As described above, the characteristic of the equipment introduction effect analyzer10according to the present embodiment is that the evaluation engine unit13uses the information related to the evaluation of the purchased equipment (the information of the user201to the comment207illustrated inFIG.2) acquired from the equipment buyer20in advance, and the information related to the use environment (the use environment302illustrated inFIG.3) acquired from the potential equipment buyer30, thereby calculating the information on cost-effectiveness such as the payout period of the equipment considered for purchase that the potential equipment buyer30really wishes to know. A conventional method has had an obstacle for collecting information scattered at individual equipment installation sites, such as the purchase price of the equipment (information obtained from the equipment buyer20) and the power consumption (information obtained from the equipment itself), into one and making it available to another potential equipment buyer30(due to the aspect that the information is not readily disclosed to others, for example). However, the use of the method of the present embodiment can expect not only the effect that a part of the work previously performed by each potential equipment buyer30or equipment salesperson for collecting and reusing information can be automated, but also the increase in the value of the equipment introduction effect analyzer10by collecting the information in one place so that the equipment buyer20, the potential equipment buyer30, and the equipment manufacturer wish to actively use the equipment introduction effect analyzer. This further allows information to be collected appropriately in the equipment introduction effect analyzer10so that a virtuous cycle is formed in which the potential equipment buyer30who is considering new purchase or replacement of equipment uses the information to be able to determine which equipment to introduce, perform appropriate asset management and equipment improvement, and provide them as information to the equipment introduction effect analyzer and the equipment buyer20.

Second Embodiment

Next, an information processing apparatus according to a second embodiment will be described. The configuration of the information providing system including the information processing apparatus according to the present embodiment and the configuration of the equipment introduction effect analyzer being the information processing apparatus are similar to those of the first embodiment (seeFIG.1).

The first embodiment describes the most basic method of use of the present invention in which the potential equipment buyer30transmits information on the equipment considered for purchase to the equipment introduction effect analyzer10, and obtains information such as the cost and effect of the desired equipment. However, if the potential equipment buyer30does not have a clear intention and not specify conditions such as the name of equipment specifically considered and the function or performance of the equipment, the search service unit12of the equipment introduction effect analyzer10receives an enormous number of types of inquiries so that it takes time to obtain a result convenient for the potential equipment buyer30, or such a convenient result cannot be obtained.

Thus, the equipment introduction effect analyzer10according to the present embodiment has a search function described below in addition to the search function described in the first embodiment. That is, the equipment introduction effect analyzer10according to the present embodiment has a function of deriving more effective equipment that matches conditions such as the current equipment configuration and use environment on the side of the potential equipment buyer30transmitted from the terminal31of the potential equipment buyer30. Hereinafter, the operation of the equipment introduction effect analyzer10according to the second embodiment will be described.

FIG.6is a flowchart illustrating an example of the operation of the equipment introduction effect analyzer10according to the second embodiment.

When acquiring information on equipment, the potential equipment buyer30connects the terminal31to the search service unit12of the equipment introduction effect analyzer10via the network1, and inputs information on the equipment in use, that is, information on the equipment currently in use, together with information on the use environment (corresponding to the use environment302illustrated inFIG.3) including information such as desired cost recovery period and investable amount. The information input at this time is assumed to be similar to the equipment manufacturer name401, the equipment name402, the list price403, the function404, and the performance405included in the equipment data table400illustrated inFIG.4. The terminal31transmits each of the input information to the search service unit12as equipment configuration information of the equipment currently in use, and requests a search.

Upon acquiring the equipment configuration information transmitted by the terminal31of the potential equipment buyer30(step S21), the equipment introduction effect analyzer10searches for equipment buyer data of equipment similar to the equipment currently in use by the potential equipment buyer30, on the basis of the equipment configuration information acquired (step S22). Specifically, the matching engine unit14first searches for equipment data of equipment having the function and performance similar to (compatible with) those of the equipment indicated by the equipment configuration information acquired in step S21, from among the equipment data registered in the equipment data table400. In this search, the matching engine unit14searches for equipment data in which the same equipment manufacturer name and equipment name as the equipment manufacturer name and equipment name included in the equipment configuration information are registered. Next, the matching engine unit14searches for equipment buyer data in which the function and performance that are similar to the function and performance registered in the equipment data obtained by the search or that can be replaced without any problem are registered, from among the equipment buyer data registered in the equipment buyer data table200. For example, the matching engine unit14searches for equipment buyer data in which all the functions registered in the equipment data obtained by the search are registered, and in which the performance equivalent to or higher than the performance registered in the equipment data obtained by the search is registered. Note that in step S21, the matching engine unit14may also acquire information indicating whether or not each of the functions included in the equipment in use by the potential equipment buyer30is an essential function. In this case, in step S22, the matching engine unit14may search for equipment buyer data in which all essential functions of the functions registered in the equipment data obtained by the search are registered, and in which the performance equivalent to or higher than the performance registered in the equipment data obtained by the search is registered.

Next, the equipment introduction effect analyzer10checks whether or not the equipment buyer data of similar equipment is available, that is, whether the corresponding equipment buyer data has been found by the search in step S22(step S23).

If the corresponding equipment buyer data is available (Yes in step S23), the equipment introduction effect analyzer10checks whether use environment information is available, that is, whether the equipment configuration information acquired in step S21and the equipment buyer data found by the search in step S22both include use environment information (step S24). The matching engine unit14performs the checking processing in each of steps S23and S24.

If the use environment information is available (Yes in step S24), the equipment introduction effect analyzer10extracts the effect registered in the equipment buyer data found by the search in step S22(corresponding to the effect205illustrated inFIG.2), and converts the effect using the use environment information (step S25). Step S25is the processing similar to step S16illustrated inFIG.5.

Next, the equipment introduction effect analyzer10checks whether the effect obtained by executing the conversion processing in step S25exceeds the effect of the current equipment (step S26). That is, the equipment introduction effect analyzer10checks whether the effect obtained by executing the conversion processing in step S25exceeds the effect included in the equipment configuration information acquired in step S21. Specifically, the evaluation engine unit13compares the cost recovery period and the investable amount indicated by the effect obtained in step S25with the cost recovery period and the investable amount acquired in step S21, thereby determining whether the effect obtained by executing the conversion processing in step S25exceeds the effect of the current equipment. In other words, it is determined whether the replacement of equipment generates benefits for the potential equipment buyer.

If the effect after conversion that is obtained by executing the conversion processing in step S25exceeds the effect of the current equipment (Yes in step S26), the equipment introduction effect analyzer10notifies the terminal31of the potential equipment buyer30of information about the equipment suggested to be introduced, specifically, the equipment data and the effect (effect after conversion) corresponding to the equipment buyer data found by the search in step S22(step S27), and ends the operation. The evaluation engine unit13performs the notification processing in step S27, that is, the processing of notifying the terminal31of the potential equipment buyer30of the information about the equipment suggested to be introduced.

Alternatively, if determining in step S24that the use environment information is not available (No in step S24), the equipment introduction effect analyzer10notifies the terminal31of the potential equipment buyer30of the equipment data corresponding to the equipment buyer data found by the search in step S22and the effect (the effect registered in the equipment buyer data found by the search in step S22) (step S27), and ends the operation. In this case, that is, when the notification is made in step S27without executing step S25, a notification is also made that the equipment data and effect are reference information. The notification processing in step S27in this case is also performed by the evaluation engine unit13.

Moreover, if determining that the corresponding equipment buyer data is not available in step S23(No in step S23), or that the effect after conversion does not exceed the effect of the current equipment in step S26(No in step S26), the equipment introduction effect analyzer10notifies the terminal31of the potential equipment buyer30that the corresponding data is not available (step S28), and ends the operation. The search service unit12or the evaluation engine unit13performs the notification processing in step S28, that is, the processing of notifying the terminal31that the corresponding data is not available. Specifically, the search service unit12performs the notification processing in step S28that is executed if the determination in step S23is “No”, and the evaluation engine unit13performs the notification processing in step S28that is executed if the determination in step S26is “No”.

Note that the equipment introduction effect analyzer10of the present embodiment acquires the equipment configuration information of the equipment currently in use from the potential equipment buyer30in step S21, and uses the equipment configuration information to search for the equipment that can be suggested to be introduced, that is, the equipment more effective than the equipment currently in use. However, the present invention is not limited to this. The equipment introduction effect analyzer10may also be adapted to search for equipment that can be suggested to the equipment buyer20who has already registered each information of the equipment buyer data, and suggest the introduction of new equipment to the equipment buyer20. An example of the equipment that can be suggested to the equipment buyer20is equipment that has the function similar to the function of the equipment already introduced by the equipment buyer20, has the performance similar to or higher than the performance thereof, and is more effective than the equipment already introduced.

Next, the effect of the equipment introduction effect analyzer10according to the present embodiment will be described. The equipment introduction effect analyzer10according to the present embodiment can automatically derive the equipment, which is more effective than the equipment currently used by the potential equipment buyer30and gives benefits when introduced, by the matching engine unit14using the information in the equipment-related information storage unit15without the potential equipment buyer30having to input the detailed information of the desired equipment and performing the search as in the first embodiment. Therefore, if conditions such as the current equipment configuration and investment limits are input, equipment that is relatively effective is recommended from the equipment introduction effect analyzer10so that the time and effort required for considering the replacement of equipment or the like can be greatly reduced. In addition, even for a person who starts with a survey or search for equipment as in the first embodiment, once he purchases equipment and registers information on the equipment buyer data (the user201to the comment207illustrated inFIG.2), the matching engine unit14treats the equipment buyer20as a next potential equipment buyer30from the next time and suggests next equipment replacement, so that this cycle naturally contributes to increasing the equipment buyer data and provides more useful information to the potential equipment buyer30and the equipment buyer20. In summary, the effect of the present embodiment is to contribute to labor saving in the implementation of the first embodiment, and to increase the number of implementations and enhance the effect thereof.

Finally, hardware for implementing the equipment introduction effect analyzer10will be described.FIG.7is a diagram illustrating an example of the hardware for implementing the equipment introduction effect analyzer10according to the first and second embodiments. The equipment introduction effect analyzer10can be implemented by a processor91, a memory92, and a communication interface93illustrated inFIG.7.

The processor91can be a central processing unit (CPU), a system large scale integration (LSI), or the like, the CPU being also referred to as a central processor, a processing unit, an arithmetic unit, a microprocessor, a microcomputer, or a digital signal processor (DSP). The memory92can be a random access memory (RAM), a read only memory (ROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM (registered trademark)), a hard disk drive, or the like. The communication interface93can be, for example, a network interface card.

The registration service unit11, the search service unit12, the evaluation engine unit13, and the matching engine unit14of the equipment introduction effect analyzer10are implemented by the processor91executing a program for operating as each of these units. The program for operating as each of the registration service unit11, the search service unit12, the evaluation engine unit13, and the matching engine unit14is stored in advance in the memory92. The processor91operates as the registration service unit11, the search service unit12, the evaluation engine unit13, and the matching engine unit14by reading the above program from the memory92and executing the program.

The equipment-related information storage unit15is implemented by the memory92. Note that the communication interface93is used when the equipment introduction effect analyzer10communicates with the terminal21, the equipment22, and the terminal31via the network1.

Note that the program for operating as each of the search service unit12, the evaluation engine unit13, and the matching engine unit14is stored beforehand in the memory92in the above description, but it is not limited thereto. The above program may be written on a recording medium such as a compact disc (CD)-ROM or a digital versatile disc (DVD)-ROM to be supplied to a user, and may be installed in the memory92by the user. In this case, the hardware that implements the equipment introduction effect analyzer10includes a reading device for reading the program from the recording medium or an interface circuit for connecting the reading device.

Third Embodiment

Next, an information processing apparatus according to a third embodiment will be described. The configuration of the information providing system including the information processing apparatus according to the present embodiment and the configuration of the equipment introduction effect analyzer being the information processing apparatus are similar to those of the first embodiment (seeFIG.1).

In the equipment introduction effect analyzer10of the information providing system100according to the first embodiment, the evaluation engine unit13calculates the information useful for the potential equipment buyer30to determine whether or not to purchase equipment, on the basis of the use environment information registered in the equipment buyer data, the use environment information registered in the potential equipment buyer data, and the information on the effect registered in the equipment buyer data. The present embodiment describes the equipment introduction effect analyzer in which the evaluation engine unit13is implemented by a machine learning device.

FIG.8is a diagram illustrating an example of a configuration of the evaluation engine unit13according to the third embodiment. The evaluation engine unit13according to the third embodiment includes a learning unit16, a trained model storage unit17, and a predictive effect value generating unit18.

The learning unit16learns an effect obtained when equipment is introduced on the basis of the information registered in the equipment buyer data table200(seeFIG.2) and the equipment data table400(seeFIG.4) held in the equipment-related information storage unit15, and generates a trained model.

The trained model storage unit17receives the trained model generated by the learning unit16from the learning unit16, and stores the trained model.

On the basis of the use environment information and information related to features of the equipment considered for purchase that are acquired from the potential equipment buyer30and the trained model that is held in the trained model storage unit17, the predictive effect value generating unit18generates a predictive effect value that is a result of prediction of the effect obtained when the potential equipment buyer30introduces the equipment, and outputs the generated predictive effect value as introduction effect information. Note that the trained model storage unit17may be placed inside the learning unit16or the predictive effect value generating unit18.

FIG.9is a diagram illustrating an example of a configuration of the learning unit16included in the evaluation engine unit13according to the third embodiment. The learning unit16includes a data acquisition unit61and a model generation unit62.

From the equipment buyer data table200and the equipment data table400held in the equipment-related information storage unit15, the data acquisition unit61acquires the equipment manufacturer name202and the equipment name203as identification information of the equipment, the function404and the performance405as feature information indicating the features of the equipment, the use environment206, and the effect205. Note that the data acquisition unit61acquires the function404and the performance405associated with the equipment manufacturer name401and the equipment name402that have the same values as the equipment manufacturer name202and the equipment name203.

The model generation unit62learns a predictive effect value on the basis of training data that is created on the basis of a combination of the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205acquired by the data acquisition unit61. That is, the model generation unit62generates a trained model that predicts the optimum predictive effect value from the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205. Here, the training data is data in which the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205are associated with one another. The equipment manufacturer name202and the equipment name203are information for identifying the equipment, and the use environment206is information indicating the environment and conditions under which the equipment is used (such as the presence or absence of an operator, power supply voltage, and air temperature). The effect205is information indicating the effect obtained when the equipment identified by the equipment manufacturer name202and the equipment name203is used under the environment and conditions indicated by the use environment206. The predictive effect value is a predictive value of the effect obtained when the equipment is introduced.

Note that although the evaluation engine unit13includes the learning unit16in the present embodiment, the learning unit16may be placed outside the evaluation engine unit13. Alternatively, the learning unit16may be implemented by a machine learning device external to the equipment introduction effect analyzer10. That is, the learning unit16may be implemented by, for example, a machine learning device connected to the equipment introduction effect analyzer10via the network1. In this case, the machine learning device that implements the learning unit16may be on a cloud server.

The learning algorithm used by the model generation unit62can be a known algorithm such as supervised learning, unsupervised learning, or reinforcement learning. As an example, a case where a neural network is applied will be described.

The model generation unit62learns a predictive effect value by, for example, so-called supervised learning according to a neural network model. Here, supervised learning refers to a model that gives a large number of sets of data of certain input and result (label) to the machine learning device, learns features in those pieces of training data, and estimates the result from the input.

The neural network includes an input layer including a plurality of neurons, a middle layer (hidden layer) including a plurality of neurons, and an output layer including a plurality of neurons. The middle layer may be one layer or two or more layers.

For example, in the case of a three-layer neural network as illustrated inFIG.10, when multiple pieces of data are input to the neurons (X1to X3) in the input layer, the neurons multiply the input data values by weights W1(w11to w16) and output them to the neurons (Y1and Y2) in the middle layer. That is, each neuron in the input layer outputs the value obtained by multiplying the input value by the weight W1toward all the neurons in the middle layer. Similarly, each neuron in the middle layer multiplies the input value by the weight W2(w21to w26) and outputs it to each neuron (Z1to Z3) in the output layer. Each neuron in the output layer outputs the value input from each neuron in the middle layer to the outside. The output result varies depending on the values of the weights W1and W2.

The neural network applied to the model generation unit62learns the predictive effect value by so-called supervised learning according to the training data input from the data acquisition unit61.

That is, the model generation unit62adjusts the weights W1and W2such that, when the equipment manufacturer name202, the equipment name203, the function404, the performance405, and the use environment206among the data included in the training data are input to the input layer of the neural network, the result output from the output layer approximates the effect205. Note that the training data acquired by the data acquisition unit61may be the equipment manufacturer name202, the equipment name203, and the use environment206. In this case, the model generation unit62acquires the function404and the performance405corresponding to the equipment manufacturer name202and the equipment name203from the equipment data table400, and inputs the function404and the performance405to the neural network together with the equipment manufacturer name202, the equipment name203, and the use environment206received from the data acquisition unit61.

The model generation unit62generates a trained model by executing the above learning, and outputs the trained model to the trained model storage unit17.

Next, the operation in which the learning unit16learns the predictive effect value will be described with reference toFIG.11.FIG.11is a flowchart illustrating a learning operation of the learning unit16included in the evaluation engine unit13according to the third embodiment.

In the learning unit16, first, the data acquisition unit61acquires data required for the learning processing, specifically, the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205, from the equipment-related information storage unit15(step S31). Note that the data acquisition unit61acquires all combinations of the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205held in the equipment-related information storage unit15.

Next, the model generation unit62performs the learning processing (step S32). That is, the model generation unit62learns a predictive effect value by so-called supervised learning according to training data created on the basis of the combination of the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205acquired by the data acquisition unit61, and generates a trained model. The model generation unit62learns the predictive effect value using all the combinations of the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205acquired by the data acquisition unit61.

Next, the model generation unit62outputs the trained model generated in step S32to the trained model storage unit17(step S33). The trained model storage unit17stores the trained model received from the model generation unit62.

Alternatively, in step S31above, although the data acquisition unit61simultaneously acquires the pieces of data required for the learning processing performed by the model generation unit62, the pieces of data may be acquired at different timings as long as the training data can be created by associating the pieces of data in the end.

FIG.12is a diagram illustrating an example of a configuration of the predictive effect value generating unit18included in the evaluation engine unit13according to the third embodiment. The predictive effect value generating unit18includes a data acquisition unit81, an inference unit82, and a notification unit83. Note that a trained model71illustrated inFIG.12is the trained model stored in the trained model storage unit17.

The data acquisition unit81acquires data related to equipment considered for purchase by the potential equipment buyer30, specifically, the equipment manufacturer name, the equipment name, the features of the equipment, and the use environment. The data acquisition unit81acquires these pieces of data from the terminal31owned by the potential equipment buyer30and the potential equipment buyer data table300held in the equipment-related information storage unit15. Note that the data acquisition unit81may also acquire data on the use environment from the terminal31owned by the potential equipment buyer30.

The inference unit82infers a predictive effect value by using the trained model71stored in the trained model storage unit17. That is, the inference unit82receives the equipment manufacturer name, the equipment name, the features of the equipment, and the use environment from the data acquisition unit81, inputs each received data into the trained model71stored in the trained model storage unit17, and acquires the predictive effect value that is inferred from the equipment manufacturer name, the equipment name, the features of the equipment, and the use environment and is output from the trained model71.

The notification unit83notifies the terminal31of the potential equipment buyer30of an inference result output from the inference unit82, that is, the predictive effect value.

Note that in the present embodiment, the predictive effect value generating unit18generates the predictive effect value using the trained model71generated by the learning unit16of the evaluation engine unit13, but the predictive effect value may be generated using the trained model71that is acquired from the outside of the equipment introduction effect analyzer10. For example, the trained model storage unit17receives and stores the trained model71generated by an evaluation engine unit of another equipment introduction effect analyzer, so that the predictive effect value generating unit18may generate the predictive effect value using the trained model71held in the trained model storage unit17.

Next, the operation in which the predictive effect value generating unit18generates the predictive effect value will be described with reference toFIG.13.FIG.13is a flowchart illustrating the predictive effect value generating operation of the predictive effect value generating unit18included in the evaluation engine unit13according to the third embodiment. Here, the equipment introduction effect analyzer10according to the third embodiment executes the operation in which the predictive effect value generating unit18generates the predictive effect value illustrated inFIG.13, in place of step S16of the flowchart illustrated inFIG.5described in the first embodiment, and step S25of the flowchart illustrated inFIG.6described in the second embodiment. That is, the evaluation engine unit13of the equipment introduction effect analyzer10according to the first and second embodiments converts, in steps S16and S25above, the information on the effect registered in the equipment buyer data on the basis of the use environment information registered in the equipment buyer data and the use environment information registered in the potential equipment buyer data, thereby finding the predictive effect value that is the effect obtained when the potential equipment buyer30introduces the equipment under consideration. On the other hand, in steps S16and S25above, the evaluation engine unit13of the equipment introduction effect analyzer10according to the third embodiment finds the predictive effect value on the basis of the trained model, the equipment manufacturer name, the equipment name, the features of the equipment, and the use environment.

When the predictive effect value generating unit18generates the predictive effect value, the data acquisition unit81first acquires data related to the equipment considered for purchase by the potential equipment buyer30, specifically, the equipment manufacturer name, the equipment name, the features of the equipment, and the use environment, from the terminal31owned by the potential equipment buyer30(step S41).

Next, the inference unit82inputs the data acquired by the data acquisition unit81, specifically, the equipment manufacturer name, the equipment name, the features of the equipment, and the use environment, to the trained model71stored in the trained model storage unit17(step S42), and acquires the predictive effect value output as a result from the trained model71(step S43). The inference unit82outputs the predictive effect value acquired from the trained model71to the notification unit83(step S44).

Upon receiving the predictive effect value from the inference unit82, the notification unit83makes a response with the received predictive effect value as the effect that is obtained when the equipment is used in the use environment of the potential equipment buyer30. The potential equipment buyer30can thus know a rough estimate of the effect obtained when the equipment is introduced without purchasing the equipment that has not been purchased, and can use the rough estimate in considering the purchase of the equipment in terms of cost-effectiveness information.

When the potential equipment buyer30actually purchases the equipment as a result of considering the purchase of the equipment, the information providing system100acquires information on an effect actually obtained after the purchase of the equipment, creates new equipment buyer data including the acquired information on the effect, and registers the data in the equipment buyer data table200in the equipment-related information storage unit15. When creating the new equipment buyer data, the information providing system100causes the learning unit16to perform re-learning using the new equipment buyer data, and updates the trained model.

Note that although the evaluation engine unit13includes the predictive effect value generating unit18in the present embodiment, the predictive effect value generating unit18may be placed outside the evaluation engine unit13. Moreover, for example, an inference device external to the equipment introduction effect analyzer10may implement the predictive effect value generating unit18. That is, the predictive effect value generating unit18may be implemented by, for example, an inference device connected to the equipment introduction effect analyzer10via the network1. In this case, the inference device that implements the predictive effect value generating unit18may be on a cloud server.

Note that the present embodiment has described the example in the case where supervised learning is applied as the learning algorithm used by the model generation unit62of the learning unit16, but the present invention is not limited to such an example. Besides supervised learning, it is also possible to apply reinforcement learning, unsupervised learning, semi-supervised learning, or the like as the learning algorithm.

Moreover, the model generation unit62may learn the predictive effect value according to the training data created for a plurality of the evaluation engine units13. In this case, in the learning unit16including the model generation unit62to be trained, the data acquisition unit61acquires the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205stored in the equipment-related information storage unit15from the equipment-related information storage unit15included in each of one or more of the equipment introduction effect analyzers10different from the equipment introduction effect analyzer10including the evaluation engine unit13that includes the data acquisition unit61itself. Note that the data acquisition unit61may collect training data from a plurality of the equipment introduction effect analyzers10used in the same area and learn the predictive effect value, or may collect training data from a plurality of the equipment introduction effect analyzers10operating independently in different areas and learn the predictive effect value. Moreover, the equipment introduction effect analyzer10as a target from which the training data is collected can be added or removed to or from the target along the way. Furthermore, a trained model in which the predictive effect value is learned for the evaluation engine unit13of a certain equipment introduction effect analyzer10may be applied to the evaluation engine unit13included in another equipment introduction effect analyzer10, so that the trained model may be updated by re-learning the predictive effect value for the evaluation engine unit13of the another equipment introduction effect analyzer.

Also, as the learning algorithm used in the model generation unit62of the learning unit16, deep learning that learns extraction of a feature value itself can be used, or machine learning may be executed according to another known method such as genetic programming, functional logic programming, or support vector machine.

As described above, the evaluation engine unit13of the equipment introduction effect analyzer10according to the present embodiment learns the predictive effect value, which is the result of prediction of the effect obtained when the equipment is purchased, by using the identification information of the equipment already purchased by the equipment buyer20, the feature information of the equipment, the information on the installation environment of the equipment, and the information on the effect obtained by introducing the equipment. The evaluation engine unit13further uses the trained model71that is a learning result of the predictive effect value, thereby predicting the effect obtained when the potential equipment buyer30introduces the equipment considered for purchase.

Note that the evaluation engine unit13according to the third embodiment performs the processing that predicts the effect (the effect obtained when the potential equipment buyer30introduces the equipment) using the trained model obtained by machine learning instead of the processing of the evaluation engine unit13according to the first embodiment (processing that generates the information on the effect obtained when the potential equipment buyer30introduces the equipment), but may be configured such that both of these processings can be selectively used. For example, the evaluation engine unit13can predict the effect by executing the processing described in the first embodiment when the number of pieces of the equipment buyer data registered in the equipment buyer data table200is under a predetermined number, or can predict the effect using the trained model in other cases. Also, the evaluation engine unit13can predict the effect by executing the processing described in the first embodiment when being able to predict the effect by executing the processing described in the first embodiment, specifically, when the determination in step S15illustrated inFIG.5is “Yes”, or can predict the effect using the trained model in other cases. Moreover, a user or administrator of the information providing system100may be allowed to select whether the evaluation engine unit13predicts the effect by executing the processing described in the first embodiment or by using the trained model.

Furthermore, in the third embodiment, the model generation unit62of the learning unit16uses the training data based on the combination of the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205to learn the relationship between the effect205and the combination of the equipment manufacturer name202, the equipment name203, the function404, the performance405, and the use environment206, but may perform the learning using different training data. For example, the learning may be performed using training data based on a combination of the equipment manufacturer name202, the equipment name203, the function404, the performance405, the use environment206, and the effect205, the use environment302included in the potential equipment buyer data, and the effect after conversion obtained by executing step S16ofFIG.5described in the first embodiment. That is, a method that converts the effect205into the effect provided to the potential equipment buyer30using the training data may be learned.

The configuration illustrated in the above embodiment merely illustrates an example of the content of the present invention, and can thus be combined with another known technique or partially omitted and/or modified without departing from the scope of the present invention.

REFERENCE SIGNS LIST