WO2023105951A1

WO2023105951A1 - Information processing device, information processing method, and information processing program

Info

Publication number: WO2023105951A1
Application number: PCT/JP2022/039384
Authority: WO
Inventors: 豪久高原; 章子高橋
Original assignee: ユニ・チャーム株式会社
Priority date: 2021-12-09
Filing date: 2022-10-21
Publication date: 2023-06-15
Also published as: JP2023086017A

Abstract

An information processing device (100) according to the present application is characterized by comprising: a first extraction unit (132) that extracts, from among posted information that has been posted on a network by users, posted information containing information related to a predetermined nonwoven fabric product; a second extraction unit (133) that extracts, from among the posted information that is extracted by the first extraction unit (132), posted information related to the use of the nonwoven fabric product; and an estimation unit (134) that estimates, on the basis of the posted information related to the use of the nonwoven fabric product, information related to the form of use when the users use the nonwoven fabric product.

Description

Information processing device, information processing method and information processing program

The present invention relates to an information processing device, an information processing method, and an information processing program.

Conventionally, a demand prediction system uses past information on SNS (Social Networking Service) related to similar concerts as performance information to estimate audience demand such as wanting beer, wanting tea, and wanting to eat ice cream. Techniques for prediction are known.

JP 2018-92267 A

However, the conventional technology described above merely predicts product demand based on information posted on SNS by users. For example, if the product is a non-woven fabric product that is used only a limited number of times per day, depending on how the non-woven fabric product is used, there may be cases where the user wants to use the non-woven fabric product and who refrains from using the non-woven product. In such a case, it is not always possible to make an appropriate demand forecast for the product.

This application has been made in view of the above, and aims to grasp the demand of users for each usage mode of nonwoven products.

An information processing apparatus according to the present application includes: a first extracting unit for extracting posted information including information about a predetermined nonwoven fabric product from posted information posted on a network by a user; a second extraction unit for extracting posted information relating to the use of the non-woven fabric product from the posted information; and an estimating unit for estimating.

According to one aspect of the embodiment, it is possible to grasp the demand of users for each usage mode of nonwoven fabric products.

FIG. 1 is a diagram illustrating an example of information processing executed by an information processing apparatus according to an embodiment; FIG. 2 is a diagram illustrating an example of a configuration of an information processing system according to the embodiment; 3 is a diagram illustrating an example of a posted information storage unit according to the embodiment; FIG. 4 is a diagram illustrating an example of a product information storage unit according to the embodiment; FIG. FIG. 5 is a diagram illustrating an example of an estimation result information storage unit according to the embodiment; FIG. 6 is a flowchart illustrating an example of the flow of delivery processing executed by the information processing apparatus according to the embodiment; FIG. 7 is a diagram illustrating an example of a hardware configuration;

At least the following matters become clear from the description of this specification and the attached drawings.

a first extracting unit for extracting posted information containing information about a predetermined nonwoven fabric product from posted information posted on a network by a user; and an estimating unit for estimating information on the manner in which the nonwoven fabric product is used by the user based on the posted information on the use of the nonwoven fabric product. An information processing device characterized by:

According to such an information processing device, for example, among the posted information posted to the SNS by the user, the posted information including the information related to the predetermined non-woven fabric product such as wet wipes is extracted. Here, examples of posted information include text, still images, moving images, and the like, but may not be limited to these.

Here, the posted information including information about the predetermined nonwoven fabric product includes not only posted information such as how the nonwoven product is used and in what manner, but also information simply indicating the nonwoven product. It is considered to be mixed. If the user's needs are estimated using such posted information, there is a risk that the user's demand cannot be grasped appropriately.

For example, in the case of products such as wet wipes, which are used only a limited number of times per day, there are times when there is a product form of wet wipes that users want to use and a product form of wet wipes that they refrain from using. For example, when users go outdoors, they tend to desire small-capacity packs that are easy to carry. On the other hand, when users want to use wet tissues indoors, they tend to want large-capacity packs.

In addition, it is expected that there will be demand for wiping down items such as tables and chairs when using wet wipes indoors. Therefore, in anticipation of an increase in the use of wet wipes indoors, it is desirable to manufacture wet wipes having components and sizes suitable for wiping articles. On the other hand, it is predicted that there will be demand for wiping hands for disinfection when using wet wipes outdoors. Therefore, when it is expected that the use of wet wipes outdoors will increase, it is desirable to manufacture wet wipes having components and sizes suitable for wiping hands.

Under these trends, for example, when it is predicted that the demand for wet wipes will increase indoors such as at home, etc., when various infectious diseases are spreading, wet wipes in large-capacity packs and suitable for wiping items It is desirable to manufacture many wet wipes with different ingredients and sizes. However, during the spread of various infectious diseases, as a result of an increase in remote work, there are cases where behavior such as taking a walk in a nearby park for the purpose of relieving lack of exercise increases. In this case, there may be an increase in demand for wet wipes in small-capacity packs that are easy for users to carry, and wet wipes with ingredients and sizes suitable for wiping hands, instead of large-capacity packs.

As mentioned above, it is difficult to predict demand properly for non-woven products such as wet wipes, and it may not be possible to realize an appropriate manufacturing plan. Therefore, the information processing device extracts the posted information regarding the use of the nonwoven fabric product from the posted information posted by the user. Then, the information processing device estimates information about the manner of use of the nonwoven fabric product by the user based on the posted information about the use of the nonwoven fabric product. That is, the information processing device extracts posted information regarding the use of the nonwoven fabric product based on posted information with high immediacy such as SNS, and uses the extracted posted information to timely estimate information regarding the manner of use. . As a result, the information processing device can, for example, estimate a product form that meets demand, such as an appropriate ratio of small-volume wet tissue packs and large-volume wet tissue packs, or ingredients or sizes.

Further, the information processing device extracts posted information including a keyword indicating the nonwoven fabric product as posted information including information about the predetermined nonwoven fabric product.

According to such an information processing device, for example, posted information containing wet wipes as a keyword is extracted from the posted information searched from the storage unit of the SNS server that provides the SNS. Thereby, the information processing device can appropriately extract the posted information including the information about the predetermined nonwoven fabric product.

Further, the information processing device extracts posted information including a keyword indicating the type of the nonwoven fabric product as posted information including information about the predetermined nonwoven fabric product.

According to such an information processing apparatus, for example, among the posted information retrieved from the storage unit of the SNS server that provides the SNS, the posted information including the product name of wet wipes and the keyword indicating the series name of the product is extracted. do. Thereby, the information processing device can comprehensively extract posted information including information about a predetermined nonwoven fabric product.

Further, the information processing device extracts, as the posted information on the use of the nonwoven fabric product, posted information including information on behavior of the user who uses the nonwoven fabric product, and based on the posted information including information on the behavior of the user who uses the nonwoven fabric product Then, information about the manner of use of the nonwoven fabric product by the user is estimated.

According to such an information processing device, for example, out of posted information including wet wipes as a keyword, posted information including the user's behavior and the user's current location as keywords is extracted. Subsequently, the information processing device estimates information about the manner in which the user uses the nonwoven fabric product based on the behavior of the user and posted information including the current location of the user. As a result, the information processing device can grasp the demand of the user for each usage mode of the nonwoven fabric product.

Further, the information processing device extracts posted information including a keyword indicating the behavior of the user as posted information including information about the behavior of the user.

According to such an information processing device, for example, posted information including user behavior as a keyword is extracted from posted information including wet wipes as a keyword. Thereby, the information processing device can appropriately extract the posted information including the behavior of the user.

In addition, the information processing device extracts posted information including a keyword indicating a date and time related to the user's behavior as posted information including information about the user's behavior.

According to such an information processing device, for example, posted information including a keyword indicating a date and time related to the user's behavior is extracted from posted information including wet wipes as a keyword. Thereby, the information processing device can appropriately extract the posted information including the keyword indicating the date and time related to the behavior of the user.

Further, the information processing device extracts posted information including a keyword indicating the position of the user as posted information including information about the behavior of the user.

According to such an information processing device, for example, posted information including the user's current location is extracted from posted information including wet wipes as a keyword. Thereby, the information processing device can appropriately extract the posted information including the user's current position.

In addition, the information processing device estimates, as the information on the manner of use, information on the place of use where the nonwoven fabric product is used by the user.

According to such an information processing device, for example, if the posted information includes keywords such as "picnic" and "cafe", it is assumed that the location where the user uses the wet tissue is outdoors. As a result, the information processing device can accurately estimate the place of use.

In addition, the information processing device further estimates whether or not the user will carry the nonwoven fabric product to the place of use based on the information on the place of use.

According to such an information processing device, for example, when it is assumed that the place of use is outdoors, it is assumed that the user will carry wet wipes to the place of use. As a result, the information processing device can accurately estimate whether or not the nonwoven fabric product will be carried to the place of use.

In addition, the information processing device further estimates information on the demand for each product form of the nonwoven fabric product based on the information on the usage mode.

According to such an information processing device, for example, when it is assumed that the place of use is outdoors and the user carries wet wipes to the place of use, information regarding the demand for small-capacity packs and large-capacity packs is estimated. As a result, the information processing device can accurately estimate the information regarding the demand for each product form of the nonwoven fabric product.

In addition, the information processing device further estimates information on the demand for each of the product forms with different numbers of the nonwoven fabric products included in the product form.

According to such an information processing device, for example, it is estimated that the demand for small-capacity packs that are easy to use outdoors and easy to carry is higher than the demand for large-capacity packs. As a result, the information processing device can accurately estimate the information regarding the demand for each product form with different numbers of non-woven fabric products.

In addition, the information processing device further estimates information on the demand for each provision mode of the nonwoven fabric product based on the information on the usage mode.

According to such an information processing device, for example, based on information that the demand for small-capacity packs is high, it is estimated that the number of small-capacity packs sold at convenience stores will be increased as a mode of providing small-capacity packs. Thereby, the information processing device can encourage the user to purchase the nonwoven fabric product at an appropriate timing.

In addition, the information processing device further estimates information on the demand for each product form of the nonwoven fabric product based on the variation in the usage mode.

According to such an information processing device, for example, it may be estimated that there is a high demand for large-capacity packs based on information such as the location where users use wet tissues has changed from outdoors to indoors. As a result, the information processing device can timely and accurately estimate the demand for the nonwoven fabric product.

In addition, the information processing device extracts posted information including information about the nonwoven fabric product from posted information posted on a predetermined SNS.

According to such an information processing device, for example, posted information containing wet wipes as a keyword is extracted from the posted information searched from the storage unit of the SNS server. Thereby, the information processing device can appropriately extract the posted information including the information about the nonwoven fabric product.

In addition, the information processing device executes delivery processing for delivering the nonwoven fabric product in the product form according to the estimated usage mode.

According to such an information processing device, for example, when it is estimated that there is a high demand for small-volume packs of wet tissues, a delivery company issues a delivery request for delivering small-volume packs of wet tissues to retail stores. send to. Thereby, the information processing device can provide appropriate nonwoven fabric products to retail stores.

An example of a form (hereinafter referred to as "embodiment") for implementing an information processing device, an information processing method, and an information processing program will be described in detail below with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program are not limited by this embodiment. Also, in the following embodiments, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

[Embodiment]
[1. Example of information processing indicated by information processing device]
An example of information processing executed by the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of information processing executed by an information processing apparatus 100 according to an embodiment. For convenience, the steps shown in FIG. 1 include actions of natural persons.

In the following, the SNS server 10 provides a service (which may be referred to as "service A" hereinafter) for disclosing posted information posted by a user to other users. An example of service A is microblogging, in which posted information in a relatively short text format is made available to each user.

In this case, the information processing device 100 extracts posted information including information on wet wipes (an example of a nonwoven fabric product) from the posted information. Next, from among the posted information extracted by the information processing apparatus 100, posted information including information about the behavior of the user is extracted. Then, an example of estimating information about the usage mode when the user uses the wet tissue based on the posted information including the information about the behavior of the user extracted by the information processing apparatus 100 will be described.

First, as shown in FIG. 1, the user makes a post about wet wipes (step S1). In this case, it is assumed that the user is registered with the service A. For example, it is assumed that a user registers user information and the like in service A and has an account for service A. FIG.

In the example of FIG. 1, user U1 posts posted information PO1 on service A stating that "wet wipes are essential for a picnic". Further, the user U2 posts the posted information PO2 on the service A saying that "wet wipes are convenient". User U3 also posts posted information PO3 on service A stating that "wet wipes are essential in cafes". In this way, the SNS server 10 receives information on the posted date and time together with posted information posted by each user.

Subsequently, the information processing device 100 searches for posted information from the storage unit of the SNS server 10, and extracts posted information including information on wet wipes (step S2).

For example, the information processing device 100 searches for posted information from the storage unit of the SNS server 10 . Subsequently, information-processing device 100 extracts posted information containing wet wipes as a keyword from the searched posted information. Here, a keyword is a predetermined word. For example, if the keyword is wet tissue, the keyword may be another name for wet tissue such as alcohol tissue, disinfecting and antibacterial tissue, or another type of wet tissue (for example, wet tissue containing alcohol, wet tissue not containing alcohol etc.).

To give a more specific example, the information processing apparatus 100 uses conventional techniques such as morphological analysis and semantic analysis to determine whether or not the text indicated by the posted information includes "wet tissue" as a keyword. Then, when the posted information includes wet tissues, information processing apparatus 100 extracts the posted information.

In the example of FIG. 1, the information processing device 100 extracts the posted information PO1 to PO3 containing wet wipes as a keyword from the storage unit of the SNS server 10. In the example shown in FIG. Note that such processing may be implemented using, for example, a search function of the SNS server 10 . For example, the SNS server 10 may extract posted information including text including a keyword, and use a conventional technique such as image analysis to extract a post including a still image or a moving image of an object related to the keyword. Information may be extracted.

Then, the information processing apparatus 100 extracts posted information including information about the behavior of the user using the wet tissue from the extracted posted information (step S3). That is, the information processing apparatus 100 filters posted information including content indicating use of wet wipes from search results based on keywords indicating wet wipes such as product names of wet wipes. For example, the information processing apparatus 100 extracts posted information including the user's behavior and the current location of the user as keywords from the posted information including wet wipes as the keyword.

To give a more specific example, the information processing apparatus 100 uses conventional techniques such as morphological analysis, semantic analysis, and the like to extract the user's behavior and the current position of the user as keywords from the text indicated by the posted information. Determine whether or not Then, when the posted information includes the user's behavior or current location, the information processing apparatus 100 extracts the posted information.

In the example of FIG. 1, it is assumed that "picnic" is the keyword indicating the behavior of the user in the posted information PO1 "Wet wipes are essential for picnics". Further, it is assumed that "cafe" in the posted information PO3 "Wet tissues are essential in cafes" is a keyword indicating the current position of the user. In this case, the information processing apparatus 100 extracts the posted information PO1 and PO3 including the user's behavior or the user's current location as a keyword from the posted information PO1 to PO3 including wet wipes.

Note that the information processing apparatus 100 may use a predetermined keyword as a keyword indicating the behavior of the user. A learning model that estimates whether or not information is included (for example, when inputting post information extracted in advance as post information that includes information indicating the use of wet wipes, information indicating the use of wet wipes is posted) Posted information indicating user behavior may be extracted using a learning model that has been trained to output information indicating that the information is included in the information.

Subsequently, the information processing device 100 estimates information regarding the manner of use of the wet tissue based on the posted information including information regarding the behavior of the user (step S4). In the example of FIG. 1, the information processing apparatus 100 estimates that the location where the user uses the wet tissue is outdoors, based on the extracted posted information PO1 and PO3.

To give a more specific example, the information processing apparatus 100 extracts the text "picnic" by morphologically analyzing the text of the posted information PO1 "Wet wipes are essential for picnics". Further, the information processing apparatus 100 extracts the text "Cafe" by morphologically analyzing the text of the posted information PO2 "Wet wipes are essential in the cafe". Next, based on the extracted text, the information processing apparatus 100 estimates how the user uses the wet tissue.

For example, the information processing apparatus 100 holds in advance a table in which usage modes and keywords are associated with each other (hereinafter sometimes referred to as a "usage mode table") in a predetermined storage unit. Based on this, the mode of use associated with the keyword that matches the text extracted from each piece of posted information is identified. Then, the information processing apparatus 100 estimates in what manner the user uses the wet tissue based on the identified tendency of the manner of use.

To give a more specific example, the predetermined storage unit holds a usage mode table in which usage modes such as "outdoors", "eating and drinking", and "eating out" are associated with the keyword "cafe". and Further, a predetermined storage unit holds a usage mode table in which usage modes such as "outdoors", "going out", "exercise", and "walking" are associated with the keyword "picnic". In this case, the information processing apparatus 100 estimates "outdoors" as a usage mode with high commonality among the usage modes associated with "café" and "picnic".

It should be noted that the information processing apparatus 100 may consider usage patterns associated with similar keywords in addition to the keywords that match the text extracted from each piece of posted information. Further, the information processing apparatus 100 may estimate the usage mode in consideration of weighting according to the degree of similarity.

Further, the information processing apparatus 100 may estimate the usage pattern using a learning model that has been trained to output the degree of wet tissue usage for each predetermined usage pattern, for example. good. For example, the information processing apparatus 100 outputs a higher score when the posted information that wet wipes are used outdoors is input than when the posted information that wet wipes are used indoors is input. Posted information PO1 and posted information PO2 are input to a learning model that has been trained to do so, and if the learning model outputs a score equal to or greater than a predetermined value, "outdoors" may be estimated as the mode of use. .

Next, the information processing device 100 estimates whether or not the user will carry the wet tissue to the place of use based on the information about the place of use. In the example of FIG. 1, the information processing apparatus 100 estimates that the user will carry the wet tissue to the place of use based on the information that the place of use is outdoors. It should be noted that such an estimation process may be realized, for example, based on a table in which usage patterns are associated with whether or not the device will be carried around. In addition, the estimation process may be realized by a learning model that has learned the characteristics of the mode of use in which the mobile phone is carried.

Next, the information processing device 100 estimates information on the demand for each product form of wet wipes based on the information on the mode of use. In the example of FIG. 1, the product form of wet wipes is assumed to be a small-capacity pack containing less than a predetermined number of wet tissues and a large-capacity pack containing a predetermined number or more of wet tissues. In this case, the information processing apparatus 100 estimates information about demand for small-capacity packs and large-capacity packs based on information that the place of use is outdoors and the user carries wet tissues to the place of use. Here, the information processing apparatus 100 estimates that the demand for small-capacity packs that are easy to use outdoors and easy to carry is higher than the demand for large-capacity packs. It should be noted that such an estimation process can be realized, for example, based on a table or the like in which behaviors at the time of use, such as carrying, are associated with products suitable for such behaviors.

Then, the information processing device 100 executes delivery processing for delivering wet wipes in the product form corresponding to the estimated usage mode (step S5). For example, the information processing apparatus 100 transmits a delivery request to the delivery company server 20 to deliver small-volume packs of wet tissues to the retail store SH1 based on information that small-volume packs of wet tissues are in high demand. do. Subsequently, the delivery company LO1 managing the delivery company server 20 arranges delivery based on the delivery request.

Subsequently, the delivery company LO1 that manages the delivery company server 20 delivers the small pack of wet tissues to the retail store SH1 (step S6). For example, the delivery company LO1 delivers the small-capacity pack of wet tissues to be delivered to the retail store SH1, which is the delivery destination corresponding to the received delivery request. As a result of such processing, the retail store SH1 can prepare wet wipes in small-capacity packs, which are in greater demand than in large-capacity packs. Therefore, the user can purchase small-capacity packs of wet tissues when visiting the retail store SH1.

In the past, the demand for products was only predicted based on the information posted on SNS by users, so there were cases where the targets for which product demand could be predicted were limited. For example, if the product is a wet tissue that is used only a limited number of times per day, depending on how the wet tissue is used, the product form of the wet tissue that the user wants to use and the product form of the wet tissue that the user refrains from using. sometimes occurs. In such cases, it may not be possible to make an appropriate demand forecast for wet wipes.

Therefore, the information processing apparatus 100 extracts posted information including information about a predetermined nonwoven fabric product from the posted information posted on the network by the user. Subsequently, the information processing apparatus 100 extracts posted information regarding the use of the nonwoven fabric product from the extracted posted information. Then, the information processing apparatus 100 estimates information about the manner of use of the nonwoven fabric product by the user based on the extracted posted information about the use of the nonwoven fabric product. In this way, the information processing apparatus 100 can grasp the demand of the user for each usage mode of the nonwoven fabric product. As a result, the information processing apparatus 100 can estimate the demand for the nonwoven fabric product desired by the user at the timing when the user wants to use the nonwoven fabric product. can be provided for

[2. Configuration of information processing system]
Next, the configuration of the information processing system 1 according to the embodiment will be described using FIG. FIG. 2 is a diagram showing a configuration example of the information processing system 1 according to the embodiment. As shown in FIG. 2 , the information processing system 1 includes an SNS server 10 , a delivery company server 20 and an information processing device 100 . The SNS server 10, the delivery company server 20, and the information processing device 100 are communicatively connected via a network N by wire or wirelessly. The information processing system 1 shown in FIG. 2 may include a plurality of SNS servers 10, a plurality of delivery company servers 20, and a plurality of information processing apparatuses 100. FIG.

The SNS server 10 is an information processing device that provides various SNS services, and is realized by, for example, a server device or a cloud system. For example, the SNS server 10 accepts posts of posted information such as microblogs, blogs, articles, messages, still images, and moving images from users. Subsequently, the SNS server 10 stores the posted information in the storage unit of the SNS server 10 . Then, the SNS server 10 discloses the posted information stored in the storage unit to users other than the user who posted the posted information.

The delivery company server 20 is an information processing device that executes information processing related to delivery in response to various delivery requests, and is realized by, for example, a server device or a cloud system. For example, the delivery company server 20 receives various delivery requests and makes delivery arrangements based on the received delivery requests. Then, the delivery company managing the delivery company server 20 delivers the nonwoven fabric product to be delivered to the delivery destination corresponding to the received delivery request.

The information processing device 100 is an information processing device that can communicate with various devices via a network N such as the Internet, and is realized by, for example, a server device or a cloud system. For example, the information processing device 100 is communicably connected to various other devices via a network N. FIG.

[3. Configuration of Information Processing Device]
Next, the configuration of the information processing apparatus 100 according to the embodiment will be described using FIG. FIG. 2 shows a configuration example of the information processing apparatus 100 according to the embodiment. As shown in FIG. 2, the information processing apparatus 100 has a communication section 110, a storage section 120, and a control section .

(Regarding communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from other various devices.

(Regarding storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or an optical disk. For example, storage unit 120 has posted information storage unit 121 , estimation result information storage unit 122 , and product information storage unit 123 .

In addition, the storage unit 120 relates to various tables, such as a usage pattern table, a table in which whether or not carrying occurs is associated, and a table in which actions during use are associated with products suitable for such actions. Store information. In addition, the storage unit 120 stores a learning model for estimating whether or not the information indicating the use of the nonwoven fabric product by the user is included in the posted information, a learning model for learning the characteristics of the usage mode in which the product is carried, etc. do.

(Regarding Posted Information Storage Unit 121)
Posted information storage unit 121 stores posted information and information related to the posted information in association with each other. Here, FIG. 3 shows an example of the posted information storage unit 121 according to the embodiment. In the example shown in FIG. 3, the posted information storage unit 121 has items such as “posted information ID (Identifier)” and “posted information”. For example, "posted information" has items such as "user ID", "user information", "date and time", and "posted information".

"Posted information ID" is an identifier that identifies posted information. The “user ID” is an identifier that identifies the user who posted the posted information associated with the “posted information ID”. “User information” is information about the user associated with the “posted information ID”. For example, the user information includes the user's age, gender, address and location information, and the like.

"Date and time" is information related to the date and time when the posted information associated with the "posted information ID" was posted. “Posted information” is posted information associated with “posted information ID”. For example, the posted information is information in text format.

For example, in FIG. 3, "P1" identified by the posted information ID has a user ID of "UI1", user information of "IU1", date and time of "DT1", and posted information of " PO1”.

In the example shown in FIG. 3, the user information, etc. is represented by an abstract code such as "IU1", but the user information, etc. can be represented by numerical values, information on character strings, user information, etc. The file format of the containing file may be used.

(Regarding the estimation result information storage unit 122)
The estimation result information storage unit 122 stores information about estimated estimation results. Here, FIG. 4 shows an example of the estimation result information storage unit 122 according to the embodiment. In the example shown in FIG. 4, the estimation result information storage unit 122 has items such as "estimation result ID", "date and time", and "estimation result".

"Estimation result ID" is an identifier that identifies an estimation result. “Date and time” is information related to the date and time corresponding to the estimation result associated with the “estimation result ID”. "Estimation result" is information about the estimation result associated with the "estimation result ID".

For example, in FIG. 4, "R1" identified by the estimation result ID has a date and time of "DT1" and an estimation result of "LI1". In the example shown in FIG. 5, the date and time are represented by abstract codes such as "DT1". may be

(Regarding product information storage unit 123)
The product information storage unit 123 stores various information regarding products. Here, FIG. 5 shows an example of the product information storage unit 123 according to the embodiment. In the example shown in FIG. 5, the product information storage unit 123 has items such as "product ID" and "product information". For example, "merchandise information" has items such as "type", "merchandise", and "quantity".

"Product ID" is an identifier that identifies the product. “Type” is information about the type of product associated with the “product ID”. For example, the type is information regarding product forms such as small-capacity packs and large-capacity packs. For example, a small-capacity pack is a product form in which less than a predetermined number of wet wipes are packaged. Here, the small-capacity pack indicates a product form in which the product is subdivided. A large-capacity pack is a product in which a predetermined number or more of wet tissues are packaged. "Quantity" is information about the number of products associated with the "product ID".

For example, in FIG. 5, "M1" identified by the product ID has a type of "MT1", a product of "MA1", and a quantity of "MN1". In the example shown in FIG. 4, the types and the like are represented by abstract codes such as "MT1", but the types and the like are numerical values, information about character strings, file formats including information about the types and the like. may be

(Regarding the control unit 130)
The control unit 130 is a controller, and for example, various programs (information processing programs) stored in a storage device inside the information processing apparatus 100 are controlled by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like. example) is implemented by using RAM as a work area. Also, the control unit 130 is a controller, and is implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 2, the control unit 130 includes a search unit 131, a first extraction unit 132, a second extraction unit 133, an estimation unit 134, and a delivery processing unit 135. Realize or perform the function or action of a process. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 2, and may be another configuration as long as it performs the information processing described later. Moreover, the connection relationship between the processing units of the control unit 130 is not limited to the connection relationship shown in FIG. 2, and may be another connection relationship.

(Regarding the search unit 131)
The search unit 131 searches for various types of information. For example, the search unit 131 searches the storage unit of the SNS server 10 for posted information.

(Regarding the first extraction unit 132)
The first extraction unit 132 extracts various information. Specifically, the first extraction unit 132 extracts posted information including information about a predetermined nonwoven fabric product from the posted information posted on the network by the user. For example, the first extraction unit 132 extracts posted information including wet wipes (an example of a predetermined nonwoven fabric product) as a keyword from the posted information searched by the search unit 131 .

To give a more specific example, the first extraction unit 132 uses conventional techniques such as morphological analysis and semantic analysis to determine whether or not the text indicated by the posted information includes "wet tissue" as a keyword. . Then, when the posted information includes wet tissues, the first extraction unit 132 extracts the posted information.

In this case, the first extraction unit 132 uses, as information related to the extracted posted information, an ID for identifying the user who posted the posted information, user information related to the user, information related to the date and time when the posted information was posted, and the like. Extract information. Then, the first extraction unit 132 stores the extracted posted information and information related to the posted information in the posted information storage unit 121 .

(Regarding the second extraction unit 133)
The second extractor 133 extracts various information from the information extracted by the first extractor 132 . Specifically, second extraction unit 133 extracts, from among the posted information extracted by first extraction unit 132, the posted information regarding the use of the nonwoven fabric product.

For example, the second extracting unit 133 extracts, from the posted information including wet wipes stored by the posted information storage unit 121, posted information including the user's behavior and the user's current location as keywords (related to the use of non-woven fabric products). An example of posted information) is extracted.

To give a more specific example, the second extracting unit 133 uses conventional techniques such as morphological analysis and semantic analysis to extract the user's behavior and the user's current location as keywords in the text indicated by the posted information. Determine whether it is included. Then, when the posted information includes the user's behavior or current location, the second extraction unit 133 extracts the posted information. Second extraction unit 133 then stores the extracted posted information in storage unit 120 .

In the example of FIG. 1, it is assumed that "picnic" is the keyword indicating the behavior of the user in the posted information PO1 "Wet wipes are essential for picnics". Further, it is assumed that "cafe" in the posted information PO3 "Wet tissues are essential in cafes" is a keyword indicating the current position of the user. In this case, the second extraction unit 133 extracts the posted information PO1 and PO3 including the user's behavior or the user's current location as a keyword from the posted information PO1 to PO3 including wet wipes.

Note that the second extraction unit 133 may use a predetermined keyword as a keyword indicating the user's behavior. A learning model that estimates whether or not information indicating the use of wet wipes is included (for example, when inputting post information extracted in advance as post information that includes information indicating the use of wet wipes, the use of wet wipes is included in the post information) Posted information indicating a user's behavior may be extracted using a learning model that has been trained to output information indicating that it is included.

(Regarding the estimation unit 134)
The estimation unit 134 estimates various types of information. Specifically, the estimating unit 134 estimates information related to the manner in which the user uses the nonwoven fabric product based on the posted information related to the use of the nonwoven fabric product. In the example of FIG. 1, the estimation unit 134 estimates that the location where the user uses the wet tissue is outdoors, based on the extracted posted information PO1 and PO3.

To give a more specific example, the estimation unit 134 extracts the text "picnic" by morphologically analyzing the text of the posted information PO1 "wet wipes are essential for picnics". In addition, the estimation unit 134 extracts the text "Cafe" by morphologically analyzing the text of the posted information PO2 "Wet tissues are essential in the cafe". Based on the extracted text, the estimation unit 134 estimates how the user uses the wet tissue.

For example, it is assumed that the storage unit 120 holds in advance a usage mode table in which usage modes and keywords are associated with each other. In this case, the estimating unit 134 identifies the usage mode associated with the keyword that matches the text extracted from each piece of posted information based on the usage mode table. Subsequently, the estimating unit 134 estimates in what mode of use the user uses the wet tissue based on the identified tendency of the mode of use.

To give a more specific example, it is assumed that the storage unit 120 holds a usage mode table in which usage modes such as "outdoors", "eating and drinking", and "eating out" are associated with the keyword "cafe". It is also assumed that the storage unit 120 holds a utilization mode table in which utilization modes such as "outdoors", "going out", and "exercise" are associated with the keyword "picnic". In this case, the estimating unit 134 estimates “outdoors” as a highly common usage mode among the usage modes linked to “café” and “picnic”.

Note that the estimating unit 134 may consider usage patterns associated with similar keywords other than the keywords that match the text extracted from each piece of posted information. In addition, the estimation unit 134 may estimate the usage mode in consideration of weighting according to the degree of similarity.

In addition, the estimation unit 134 may estimate the mode of use using a learning model that has been trained to output the degree of wet tissue usage for each predetermined mode of use. For example, the estimating unit 134 outputs a score with a higher value when posted information that wet wipes are used outdoors is input than when posted information that wet wipes are used indoors is input. Posted information PO1 and posted information PO2 are input to the learning model trained in the above manner, and if the learning model outputs a score equal to or greater than a predetermined value, "outdoors" may be estimated as the mode of use.

Next, the estimation unit 134 estimates whether or not the user will carry the wet tissue to the place of use based on the information about the place of use. In the example of FIG. 1, the estimation unit 134 estimates that the user will carry the wet tissue to the place of use based on the information that the place of use is outdoors. Note that such estimation processing may be realized, for example, based on a table or the like that associates usage patterns with whether or not the device will be carried around. Also, such an estimation process may be realized by a learning model that has learned the characteristics of the mode of use in which the device is carried.

Next, the estimating unit 134 estimates information on the demand for each product type of wet wipes based on the information on the mode of use. The estimation unit 134 then stores the estimation result in the estimation result information storage unit 122 .

In the example of FIG. 1, the product form of wet wipes is assumed to be a small-capacity pack containing less than a predetermined number of wet tissues and a large-capacity pack containing a predetermined number or more of wet tissues. In this case, the estimating unit 134 estimates information about demand for small-capacity packs and large-capacity packs based on information that the place of use is outdoors and the user carries wet tissues to the place of use. For example, the estimation unit 134 estimates that the demand for small-capacity packs that are easy to use outdoors and easy to carry is higher than the demand for large-capacity packs. It should be noted that such an estimation process can be realized, for example, based on a table or the like in which behaviors at the time of use, such as carrying, are associated with products suitable for such behaviors.

(Regarding the delivery processing unit 135)
The delivery processing unit 135 executes a delivery process for delivering the nonwoven fabric product having the product form according to the usage mode estimated by the estimation unit 134 . Specifically, the delivery processing unit 135 selects the type (product type) corresponding to the ID for identifying the product stored in the product information storage unit 123, based on the information that the demand for small-capacity wet wipes is high. Choose a small pack of wet wipes. Subsequently, the delivery processing unit 135 transmits to the delivery company server 20 a delivery request for delivering the small-capacity pack of wet tissues to the retail store. Note that the delivery processing unit 135 is not limited to the above example, and may transmit various information to various servers, for example.

[4. Processing procedure]
Next, the procedure of delivery processing executed by the information processing apparatus 100 according to the embodiment will be described with reference to FIG. FIG. 6 is a flowchart showing an example of the flow of delivery processing executed by the information processing apparatus 100 according to the embodiment.

As shown in FIG. 6, the search unit 131 determines whether it is a predetermined timing (step S101). The predetermined timing here is, for example, timing when the information processing apparatus 100 is operated by an administrator who manages the information processing apparatus 100 .

Specifically, when the search unit 131 determines that it is not the predetermined timing (step S101; No), it waits until the predetermined timing is determined.

On the other hand, when the search unit 131 determines that it is the predetermined timing (step S101; Yes), it searches for posted information stored in the storage unit of the SNS server 10 (step S102).

Next, the first extraction unit 132 extracts posted information including information on nonwoven fabric products from the posted information searched by the search unit 131 (step S103). Then, the second extraction unit 133 extracts the posted information including the information about the behavior of the user who uses the nonwoven product from the posted information including the information about the nonwoven product extracted by the first extraction unit 132 (step S104). ).

Next, the estimating unit 134 estimates information on the manner of use of the non-woven fabric product based on the posted information including the information on the behavior of the user extracted by the second extracting unit 133 (step S105). Then, the delivery processing unit 135 executes a delivery process for delivering the nonwoven fabric product of the type corresponding to the usage mode estimated by the estimation unit 134 (step S106).

[5. Modification]
The information processing apparatus 100 described above may be embodied in various forms other than the above embodiment. Therefore, other embodiments of the information processing apparatus 100 will be described below.

[5-1. Nonwoven products]
In the above embodiments, an example in which the non-woven fabric product is a wet tissue has been described, but the non-woven fabric product is not limited to this. For example, the nonwoven product may be other sanitary products than sanitary products such as wet wipes. In addition, non-woven fabric products include tissues, diapers, baby wipes, sanitary products, pads for light incontinence, incontinence pads, bed sheets, masks, breastfeeding pads, cleaning products, and cosmetic puffs. etc. As another example, the nonwoven product may be diapers used by pets, pet sheets, and the like.

[5-2. Target for estimating demand]
In the above embodiment, an example in which demand is estimated for a small-capacity pack with a different number of wet tissues and a large-capacity pack has been described, but the present invention is not limited to this. For example, the target for estimating the demand may be products with wet wipes of different sizes, products with different chemical components added to the wet wipes, and the like. Here, the components of the chemical solution added to the wet tissue are components of the chemical solution that are gentle on the skin, components of the chemical solution for sterilization or antibacterial purposes, and the like.

If the non-woven fabric product is diapers for children, the target for estimating demand is products with different numbers of diapers, products with different sizes of diapers, and types of diapers (for example, pants-type diapers, tape-type diapers, etc.). diapers, etc.) may be different products. Further, the target of demand estimation may be products with different uses such as diapers for training, diapers for sleeping, diapers for playing in the water, diapers for summer, and diapers for winter. Further, the targets for which the demand is estimated may be products with different genders, products with different grades of diapers, and the like.

In addition, if the nonwoven fabric product is adult diapers, demand is estimated for products with different numbers of diapers, products with different sizes of diapers, and types of diapers (for example, pants-type diapers, tape-type diapers, etc.). diapers, pants with a urine absorption pad, etc.) may be different products. Further, the object for which the demand is estimated may be products having different uses, such as sleeping diapers and daytime diapers. In addition, the target for estimating the demand may be products with different genders, products with different absorption amounts of diapers, and the like.

Also, if the non-woven fabric product is pet diapers, the target for estimating demand may be products with different numbers of diapers or products with different diaper sizes. In addition, the target for estimating the demand may be products that differ according to gender, products that differ according to the presence or absence of castration, and the like. Further, the target for estimating the demand may be a product or the like in which the perfume added to the diaper is different.

Also, if the non-woven fabric product is a sanitary product, the target for estimating demand may be products with different numbers of sanitary products or products with different absorption amounts of sanitary products. Further, the objects for which the demand is estimated may be different types of products such as napkins and shorts-type napkins. In addition, the target for estimating the demand may be a sanitary product that differs depending on the presence or absence of wings. In addition, the target of demand estimation may be products with different uses, such as sleeping sanitary products, daytime sanitary products, and daily sanitary products with a large amount of menstrual blood. Further, the target for estimating the demand may be a sanitary product with a different fragrance added thereto.

In addition, if the target for estimating demand is pet food instead of non-woven fabric products, the target for estimating demand may be products of different animal species or products with different amounts of pet food. Moreover, the object for which demand is estimated may be a product such as a staple food or a side dish, or a product such as a wet or dry product. In addition, the target for estimating the demand may be products that differ according to gender, products that differ according to the presence or absence of castration, products that differ according to body weight, and the like.

　In addition, the target for estimating demand may be products other than the above examples. For example, the product may be a pet sheet, a pet litter box, a litter for a cat, or the like.

[5-3. Posted information]
In the above embodiment, the target is the posted information posted by the user on the SNS, but the target is not limited to this. For example, posted information may be a blog, an article, a message, or the like posted by a user.

Also, the posted information may be a still image or a moving image. For example, the first extraction unit 132 may specify the motion of a person captured in a still image, a moving image, or the like, based on posted information such as the still image or the moving image. For example, the first extraction unit 132 may specify the child's standing posture or the like based on a still image or a moving image in which the child is captured. In addition, the first extraction unit 132 extracts images of the child based on still images or moving images in which the child is imaged. You may specify the action|movement etc. which touches and walks.

[5-4. Extraction processing by the first extraction unit]
In the above-described embodiment, an example has been described in which the first extraction unit 132 extracts posted information including information about a predetermined nonwoven fabric product from posted information posted on the network by the user, but the present invention is not limited to this. . For example, the first extraction unit 132 may extract posted information including a keyword indicating the type of nonwoven fabric product. In addition, the first extraction unit 132 may extract posted information including keywords indicating product names of non-woven fabric products sold by a predetermined manufacturer and product series names. Thereby, the first extraction unit 132 can exhaustively extract the posted information related to the nonwoven fabric product.

[5-5. Extraction processing by the second extraction unit]
In the above-described embodiment, an example has been described in which the second extraction unit 133 extracts the posted information regarding the use of the nonwoven fabric product from the posted information extracted by the first extraction unit 132, but the present invention is not limited to this. For example, the second extraction unit 133 may extract posted information including a keyword indicating a date and time related to the user's behavior as the posted information including information about the user's behavior.

To give a more specific example, posted information including wet wipes includes posted information PO4. Here, the posted information PO4 is "Let's have a picnic by 9:00. Wet tissues are essential for the picnic." Further, in the posted information PO4, it is assumed that "picnic" is a keyword indicating the behavior of the user, and "9:00" is a keyword indicating the date and time related to the behavior of the user. In this case, the second extraction unit 133 extracts the posted information PO4 including the date and time related to the user's behavior from the posted information including wet wipes. Thereby, the second extraction unit 133 can comprehensively extract the posted information including various information related to the user's behavior. Note that the second extraction unit 133 is not limited to the above example, and may extract posted information including a keyword indicating a season instead of the date and time.

In addition, the second extraction unit 133 may extract posted information including information on a caregiver such as a child or elderly person who is taken care of by the user, instead of the information on the behavior of the user. For example, the second extraction unit 133 may extract posted information that includes a keyword indicating a caregiver that the user takes care of. As a result, the second extraction unit 133 can accurately extract the posted information related to the nonwoven fabric products that the caregiver is presumed to use. Note that the caregiver may be someone other than a human, and may be a pet or the like taken care of by the user.

[5-6. Estimation process (1)]
In the above embodiment, the estimating unit 134 estimates information on the manner in which the user uses the nonwoven product based on the posted information on the use of the nonwoven product, but the present invention is not limited to this. . For example, the estimation unit 134 may further estimate information on demand for each provision mode of the nonwoven fabric product based on the information on the usage mode.

For example, based on the posted information extracted by the estimating unit 134, an example will be described in which it is estimated that the location where the user uses the wet tissue is outdoors. In this case, the estimation unit 134 may estimate that the demand for small-capacity packs that are easy to carry will increase based on the information that the place of use is outdoors. Subsequently, based on the information that the demand for small-capacity packs is high, the estimation unit 134 may estimate that the number of small-capacity packs to be sold at convenience stores will be increased as a mode of providing small-capacity packs. For example, when a user goes outdoors, he or she may forget to prepare wet tissues in advance. In such a case, it is desirable for users to be able to easily purchase wet wipes. Therefore, the estimation unit 134 estimates that the number of small-capacity packs sold at convenience stores will be increased.

On the other hand, an example will be described in which, based on the posted information extracted by the estimation unit 134, it is estimated that the location where the user uses the wet tissue is indoors. In this case, the estimation unit 134 may estimate that the demand for large-capacity packs will increase based on the information that the place of use is indoors. Subsequently, based on the information that the demand for large-capacity packs is high, the estimating unit 134 increases the number of large-capacity packs to be sold at supermarkets, drug stores, and electronic commerce services as a mode of providing large-capacity packs. can be estimated. For example, when a user spends time indoors such as at home, it is desirable to be able to purchase large-capacity packs of wet wipes that are suitable for wiping items placed in the home. Therefore, the estimation unit 134 estimates that the number of large-capacity packs sold at supermarkets, drug stores, and electronic commerce services will be increased. Thereby, the estimating unit 134 may further estimate information about the demand for each provision mode of the nonwoven fabric product based on the information about the usage mode.

In this way, the estimating unit 134 further estimates information on the demand for each provision mode of the non-woven fabric product based on the information on the usage mode, so that the user can be encouraged to purchase the non-woven fabric product at an appropriate timing. can be done.

[5-7. Estimation process (2)]
In the above embodiment, the estimating unit 134 estimates information on the manner in which the user uses the nonwoven product based on the posted information on the use of the nonwoven product, but the present invention is not limited to this. . For example, the estimating unit 134 may further estimate information regarding the demand for each product form of the nonwoven fabric product based on changes in usage patterns.

For example, it is assumed that the total number of posted information items extracted by the second extraction unit 133 is stored in the storage unit 120 . In this case, the estimation unit 134 estimates whether the usage mode has changed based on the total number of pieces of posted information extracted by the second extraction unit 133 and the corresponding date and time. Here, it is assumed that the mode of use has changed. Specifically, it is assumed that the location where the user uses the wet tissue changes from outdoors to indoors.

In this case, the estimating unit 134 may estimate that there is a high demand for large-capacity packs based on information that the location where the user uses wet tissues has changed from outdoors to indoors. In this manner, the estimation unit 134 further estimates information on the demand for each product form of the nonwoven fabric product based on the change in the usage mode, so it is possible to accurately estimate the demand for the nonwoven fabric product in a timely manner.

[5-8. Other estimation processing]
In addition, the estimation unit 134 may further estimate information regarding demand for various products. For example, the estimating unit 134 may estimate information related to demand for products with different chemical components added to wet wipes.

To give a more specific example, an example will be described in which the estimation unit 134 estimates that the place of use is outdoors and the user carries wet wipes to the place of use. In this case, wet wipes with a standard amount of alcohol added, wet wipes with a larger amount of alcohol than the standard amount, and wet wipes with no alcohol added. Assume that there is At this time, the estimation unit 134 determines that the demand for wet tissue products to which a standard amount of alcohol component is added and wet tissue product to which a larger amount of alcohol component than the standard amount is added is It may be estimated that the demand for wet wipes products is higher than that.

Also, an example in which the estimation unit 134 estimates that the place of use is outdoors and the user carries wet wipes to the place of use will be described. In this case, it is assumed that various infectious diseases are spreading. At this time, the estimating unit 134 estimates that the demand for the wet tissue product to which the standard amount of the alcohol component is added is higher than the demand for the wet tissue product to which the standard amount of the alcohol component is added. may In addition, the estimation unit 134 may estimate that the demand for wet tissue products to which a standard amount of alcohol component is added is higher than the demand for wet tissue products to which no alcohol component is added.

Also, an example in which the estimation unit 134 estimates that the place of use is outdoors and the user carries wet wipes to the place of use will be described. In this case, it is assumed that various infectious diseases are spreading. In addition, it is assumed that there are wet tissue products to which a skin-protecting component and a standard amount of alcohol component are added, and wet tissue product to which a standard amount of alcohol component is added. At this time, the estimating unit 134 determines that the demand for the wet tissue product to which the skin protecting component and the standard amount of the alcohol component are added is higher than the demand for the wet tissue product to which the standard amount of the alcohol component is added. can be estimated. It should be noted that such an estimation process can be realized, for example, based on a table or the like in which behaviors at the time of use, such as carrying, are associated with products suitable for such behaviors.

As another example, the estimating unit 134 may estimate information regarding the demand for products with different outlets for wet tissue packs. As a more specific example, an example in which the estimation unit 134 estimates that the place of use is outdoors and the user carries wet tissues to the place of use will be described. In this case, it is assumed that there are products in which wet wipes are wrapped in a pack with a lid made of plastic and products in which wet wipes are wrapped in a pack with a lid with a sealing lid. At this time, the estimating unit 134 estimates that the demand for products in which wet tissue packs have a seal-type lid is higher than the demand for products in which a pack has a plastic lid. may For example, when the user goes outdoors, it is desirable for the user to be able to easily open the cover of the wet tissue. Therefore, the estimating unit 134 estimates that the number of commodities having a pack outlet with a seal-type lid will increase. It should be noted that such estimation processing can be realized, for example, based on a table or the like that associates actions during use, such as carrying, with products with different outlets that are suitable for such actions.

As another example, the estimating unit 134 may estimate information related to demand for products with wet wipes of different sizes. As a more specific example, an example in which the estimation unit 134 estimates that the location of use is indoors will be described. In this case, it is assumed that there are products with wet wipes of large size and products with wet wipes of small size. At this time, the estimation unit 134 may estimate that the demand for products with wet wipes of large size is higher than the demand for products with wet wipes of small size. For example, when a user spends time indoors, there is demand for wiping articles such as tables and chairs. In such a case, it is desirable to use a wet tissue with a size that can effectively wipe the article. Therefore, the estimation unit 134 estimates that the number of products with large wet wipes will be increased. It should be noted that such an estimation process can be realized, for example, based on a table or the like in which behaviors during use, such as spending time indoors, are associated with products suitable for such behaviors.

Note that the estimation process does not have to be limited to the above example. For example, the estimating unit 134 may estimate whether the place of use is outdoors, indoors, or the like, such as inside a vehicle of various moving bodies. In addition, the estimation unit 134 may estimate whether the wet tissue is used by a caregiver of the user, other than the user. To give a more specific example, the estimating unit 134 can detect arbitrary behavior such as a behavior of wiping the buttocks of a child that the user takes care of, a behavior of the user carrying a wet tissue to the place of use, and a child using the wet tissue. You may estimate the usage of Such usage mode estimation processing is performed by determining whether wet wipes are used in a predetermined usage mode in a table in which a predetermined usage mode and features of posted information are associated with each other, or in input posted information. It can be realized by a learning model trained to estimate whether or not there is.

[5-9. Cooperation with external services]
In addition, the estimation unit 134 may further estimate information related to the manner in which the user uses the nonwoven fabric product based on various information acquired from an external service. Here, the external service is, for example, a service that provides information on temperature, atmospheric pressure, weather, amount of pollen, degree of air pollution, and the like. For example, such services are provided by an external server managed by an external operator or the like. It should be noted that acquisition processing for acquiring information from an external server is assumed to be implemented by an API (Application Programming Interface) or the like.

For example, the estimating unit 134 acquires information about temperature, atmospheric pressure, weather, amount of pollen, degree of air pollution (for example, amount of PM2.5, etc.) from an external server. Subsequently, the estimating unit 134 determines whether the user uses the nonwoven fabric product based on the information on the temperature, the atmospheric pressure, the weather, the amount of pollen, the degree of air pollution, etc., and the posted information on the use of the nonwoven product. You may estimate the information about the utilization mode at the time of doing.

To give a more specific example, it is assumed that the usage location is assumed to be "outdoors". In this case, the estimating unit 134 determines whether or not the user will carry the wet tissue to the place of use based on the information that the place of use is outdoors and the information on temperature, atmospheric pressure, weather, etc. acquired from the external server. can be estimated. For example, assume that the weather is fine. In this case, the estimation unit 134 may estimate that the user will carry the wet tissue to the place of use based on the information that the place of use is outdoors and the information that the weather is fine.

In this case, the estimating unit 134 may estimate information on the demand for each product form of wet wipes based on the information on the mode of use. To give a more specific example, the product form of wet wipes is assumed to be a small-capacity pack containing less than a predetermined number of wet tissues and a large-capacity pack containing a predetermined number or more of wet tissues. . In this case, the estimating unit 134 estimates information about demand for small-capacity packs and large-capacity packs based on information that the place of use is outdoors, the weather is fine, and the user carries wet wipes to the place of use. You may For example, the estimation unit 134 may estimate that demand for small-capacity packs that are easy to use outdoors and easy to carry is higher than demand for large-capacity packs.

Also, it is assumed that the weather is sunny and then heavy rain. In this case, the estimating unit 134 estimates that the user does not carry the wet tissue to the place of use based on the information that the place of use is outdoors and the information that the weather is sunny and then raining heavily. good.

In this case, the estimating unit 134 determines that the demand for large-capacity packs is small based on information that the place of use is outdoors, the weather is sunny and then heavy rain, and the user does not carry wet wipes to the place of use. It may be estimated to be higher than the demand for capacity packs.

As another example, assume that the usage location is estimated to be "outdoors". In this case, the estimating unit 134 determines whether the user is using a predetermined product such as wet tissue based on the information that the place of use is outdoors and the information on the amount of pollen and the degree of air pollution obtained from the external server. It may be estimated whether or not to use a mask when carrying to the place of use.

For example, assume that the amount of pollen and the degree of air pollution are each less than a predetermined threshold. In this case, the estimating unit 134, based on the information that the place of use is outdoors and the information that the amount of pollen and the degree of air pollution are each less than a predetermined threshold, determines whether the user is using wet wipes or the like. It may be assumed that masks will not be used when carrying these products to the place of use.

In this case, the estimating unit 134, based on the information that the place of use is outdoors, the information that the amount of pollen and the degree of air pollution are each less than a predetermined threshold, and the information that the mask is not used, It may be presumed that the demand for masks will not increase.

Also, it is assumed that either the amount of pollen or the degree of air pollution is greater than or equal to a predetermined threshold. In this case, the estimating unit 134, based on the information that the place of use is outdoors and the information that either the amount of pollen or the degree of air pollution is equal to or greater than a predetermined threshold, determines whether the user is using a wet tissue or the like. It may be presumed that the mask is used when carrying the predetermined product to the place of use.

In this case, the estimation unit 134 is based on information that the place of use is outdoors, information that either the amount of pollen or the degree of air pollution is equal to or greater than a predetermined threshold, and information that a mask is used. It may be inferred that the demand for masks will increase.

In this way, the estimation unit 134 estimates information on the manner in which the nonwoven fabric product is used by the user based on the various information acquired from the external service. can be done.

[5-10. Instructions to Sales]
Further, the business operator managing the information processing device 100 may use the information regarding the demand for various nonwoven fabric products estimated by the estimation unit 134 in the business activities of the business operator. For example, the delivery processing unit 135 may transmit information regarding demand for various nonwoven fabric products to a server managed by a person in charge of sales.

To give a more specific example, it is assumed that the demand for small-volume wet tissue packs is estimated to be higher than the demand for large-volume wet tissue packs. In this case, the delivery processing unit 135 transmits information that the demand for small-capacity packs is higher than the demand for large-capacity packs to the server managed by the person in charge of sales. Next, based on the information that the demand for small-capacity packs is higher than the demand for large-capacity packs, the person in charge of sales selects the products to be sold, and the product development space provided at the retail store (for example, You may expand or reduce the number of shelves in the sales floor, etc.). For example, the person in charge of sales may select best-selling products from among the small-volume packs, expand the product development space of the small-volume packs, and the like.

In this way, the person in charge of sales can conduct appropriate sales activities according to the demand for various nonwoven fabric products. As a result, the delivery processing unit 135 can encourage the person in charge of sales to promote efficient sales activities.

It should be noted that it is not necessary to be limited to the above example, and the target for which demand is estimated may be sanitary products. For example, it is assumed that the location of use is assumed to be indoors based on the behavior of the user. In this case, the delivery processing unit 135 may transmit information to the server managed by the person in charge of sales, such as information that there is a high demand for shorts-type napkins that prevent menstrual blood leakage or long-sized napkins. Also, it is assumed that the place of use is estimated to be outdoors based on the behavior of the user. In this case, the delivery processing unit 135 stores a product such as a thin sanitary product that is difficult for a third party to see that the user is using sanitary products in a server managed by a person in charge of sales. You may send information that the demand is high.

Also, the target for which the demand is estimated may be diapers for children. For example, it is assumed that the place of use is estimated to be outdoors based on the behavior of the user. It is also assumed that the date and time are assumed to be in the summer based on the date and time associated with the user's behavior. In this case, the delivery processing unit 135 may transmit information that there is a high demand for diapers for swimming to the server managed by the person in charge of sales.

[5-11. Instructions to production technology]
Further, the business operator managing the information processing device 100 may use the information regarding the demand for various nonwoven fabric products estimated by the estimation unit 134 for the business operator's production technology and other manufacturing operations. For example, the delivery processing unit 135 may transmit information regarding demand for various nonwoven fabric products to a server managed by a person in charge of production engineering.

To give a more specific example, it is assumed that the demand for small-volume wet tissue packs is estimated to be higher than the demand for large-volume wet tissue packs. In this case, the delivery processing unit 135 transmits information that the demand for small-capacity packs is higher than the demand for large-capacity packs to the server managed by the person in charge of production engineering. Subsequently, the person in charge of production technology adjusted the production of wet wipes to increase the production of small packs based on information that the demand for small packs was higher than the demand for large packs. may

In this way, the person in charge of production technology can make appropriate production adjustments according to the demand for various nonwoven fabric products. As a result, the delivery processing unit 135 can allow the person in charge of production technology to realize efficient production adjustment.

[5-12. others〕
Of the above processes, all or part of the processes described as being performed automatically may be performed manually. Also, all or part of the processes described as being performed manually may be performed automatically by known methods. In addition, information including processing procedures, specific names, various data and parameters shown in the above documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

Also, each component of each device illustrated is functionally conceptual and does not necessarily have to be physically configured as illustrated. That is, the specific form of distribution/integration of each device is not limited to the illustrated one. Further, all or part of each component may be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions.

Also, each of the processes described above may be executed in combination as appropriate within a non-inconsistent range.

Also, the above "section, module, unit" can be read as "means" or "circuit". For example, the estimating unit can be read as estimating means or an estimating circuit.

[6. Hardware configuration]
Also, the SNS server 10, the delivery company server 20, and the information processing device 100 according to the above-described embodiments are implemented by, for example, a computer 1000 configured as shown in FIG. FIG. 7 is a diagram illustrating an example of a hardware configuration; The computer 1000 is connected to an output device 1010 and an input device 1020 , and a bus 1090 connects an arithmetic device 1030 , a cache 1040 , a memory 1050 , an output IF (Interface) 1060 , an input IF 1070 and a network IF 1080 .

The arithmetic device 1030 operates based on programs stored in the cache 1040 and memory 1050, programs read from the input device 1020, and the like, and executes various processes. The cache 1040 is a cache such as a RAM that temporarily stores data used by the arithmetic device 1030 for various arithmetic operations. In addition, the memory 1050 is a storage device in which data used for various calculations by the arithmetic unit 1030 and various databases are registered, and is realized by ROM (Read Only Memory), HDD (Hard Disk Drive), flash memory, etc. memory.

The output IF 1060 is an interface for transmitting information to be output to the output device 1010 that outputs various information such as a monitor and a printer. It may be realized by a standard connector such as HDMI (registered trademark) (High Definition Multimedia Interface). On the other hand, the input IF 1070 is an interface for receiving information from various input devices 1020 such as a mouse, keyboard, scanner, etc., and is implemented by, for example, USB.

For example, the input device 1020 includes optical recording media such as CD (Compact Disc), DVD (Digital Versatile Disc), PD (Phase change rewritable disk), magneto-optical recording media such as MO (Magneto-Optical disk), tape media, It may be implemented by a device that reads information from a magnetic recording medium, a semiconductor memory, or the like. Also, the input device 1020 may be realized by an external storage medium such as a USB memory.

The network IF 1080 has a function of receiving data from another device via the network N and sending it to the arithmetic device 1030, and transmitting data generated by the arithmetic device 1030 via the network N to other devices.

Here, the arithmetic device 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070. For example, the arithmetic device 1030 loads a program from the input device 1020 or the memory 1050 onto the cache 1040 and executes the loaded program. For example, when the computer 1000 functions as the information processing device 100 , the arithmetic device 1030 of the computer 1000 implements the functions of the control unit 130 by executing the program loaded on the cache 1040 .

The embodiments of the present application have been described in detail above based on the drawings. However, these are examples, and the embodiments of the present application can be implemented in other forms with various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the disclosure of the invention. is possible.

N network 1 information processing system 10 SNS server 20 delivery company server 100 information processing device 110 communication unit 120 storage unit 121 posted information storage unit 122 estimation result information storage unit 123 product information storage unit 130 control unit 131 search unit 132 first extraction unit 133 second extraction unit 134 estimation unit 135 delivery processing unit

Claims

a first extraction unit for extracting posted information including information about a predetermined nonwoven fabric product from posted information posted on a network by a user;
a second extraction unit for extracting, from among the posted information extracted by the first extraction unit, posted information relating to the use of the nonwoven fabric product;
An information processing apparatus, comprising: an estimation unit for estimating information related to a usage mode in which a user uses the nonwoven fabric product based on posted information related to usage of the nonwoven fabric product.
The first extraction unit is
The information processing apparatus according to claim 1, wherein posted information including a keyword indicating the nonwoven fabric product is extracted as the posted information including information about the predetermined nonwoven fabric product.
The first extraction unit is
3. The information processing apparatus according to claim 1, wherein posted information including a keyword indicating a type of the nonwoven fabric product is extracted as the posted information including information about the predetermined nonwoven fabric product.
The second extractor is
Extracting posted information including information on the behavior of the user who uses the nonwoven fabric product as the posted information on the use of the nonwoven fabric product,
The estimation unit
The method according to any one of claims 1 to 3, characterized in that, based on the posted information including information on the behavior of the user, information on the manner in which the nonwoven fabric product is used by the user is estimated. The information processing device described.
The second extractor is
5. The information processing apparatus according to claim 4, wherein posted information including a keyword indicating the behavior of the user is extracted as the posted information including information about the behavior of the user.
The second extractor is
6. The information processing apparatus according to claim 5, wherein posted information including a keyword indicating a date and time related to said user's behavior is extracted as said posted information including information about said user's behavior.
The second extractor is
The information processing apparatus according to any one of claims 4 to 6, wherein posted information including a keyword indicating the location of the user is extracted as the posted information including information about the behavior of the user.
The estimation unit
The information processing apparatus according to any one of claims 1 to 7, wherein, as the information on the manner of use, information on a place of use where the user uses the nonwoven fabric product is estimated.
The estimation unit
9. The information processing apparatus according to claim 8, further estimating whether the user will carry the nonwoven fabric product to the place of use based on the information about the place of use.
The estimation unit
10. The information processing apparatus according to any one of claims 1 to 9, wherein the information on the demand for each product type of the nonwoven fabric product is further estimated based on the information on the usage mode.
The estimation unit
11. The information processing apparatus according to claim 10, further estimating information on demand for each of the product forms having different numbers of the non-woven fabric products included in the product form.
The estimation unit
The information processing apparatus according to any one of claims 1 to 11, wherein information on demand for each provision mode of said non-woven fabric product is further estimated based on said information on said usage mode.
The estimation unit
13. The information processing apparatus according to any one of claims 1 to 12, further comprising information relating to demand for each product type of said non-woven fabric product, which is further estimated based on said variation in usage pattern.
The first extraction unit is
14. The information processing apparatus according to any one of claims 1 to 13, wherein posted information including information on said non-woven fabric product is extracted from posted information posted on a predetermined SNS (Social Networking Service). .
15. The method according to any one of claims 1 to 14, further comprising a delivery processing unit that performs a delivery process for delivering the nonwoven fabric product in the product form according to the usage pattern estimated by the estimation unit. information processing equipment.
A computer-executed information processing method comprising:
a first extracting step of extracting posted information containing information about a predetermined nonwoven fabric product from posted information posted on a network by a user;
a second extracting step of extracting posted information related to the use of the nonwoven fabric product from the posted information extracted by the first extracting step;
and an estimating step of estimating information related to the manner in which the nonwoven fabric product is used by the user, based on the posted information related to the use of the nonwoven fabric product.
a first extraction procedure for extracting posted information containing information about a predetermined nonwoven fabric product from posted information posted on a network by a user;
a second extracting step for extracting posted information relating to the use of the nonwoven fabric product from the posted information extracted by the first extracting step;
An information processing program for causing a computer to execute: an estimation procedure for estimating information related to the manner in which the user uses the nonwoven fabric product based on the posted information related to the use of the nonwoven fabric product.