Product recommendation system and recommendation method

Abstract

To grasp, before purchase of a commodity, which is a commodity whose attractiveness is not reduced as much as possible even after purchase.SOLUTION: There are provided a storage unit that stores a relationship between vital data and a recommended product, a vital data acquisition unit that acquires vital data which is a measurement result measured by a vital sensor from a person who has received an explanation about a product before purchase of the product, a recommendation unit that reads a recommended product according to the vital data acquired from the person from the storage unit, and an output unit that outputs data regarding the read recommended product.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a product recommendation system and a recommendation method. [Background technology]

[0002] When purchasing a product, a user carefully considers the products and selects and purchases them, taking into account preferences, purposes, etc. When purchasing a product, a user may look at or pick up a sample of the product displayed in a storefront to check the color, design, feel, etc. of the product and consider which product to purchase. A system has also been proposed that allows users to input keywords on an internet shopping site and then present recommended products (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 6066791 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when a user selects a product to purchase based on their favorite color or design, the user may find that a different color or design might have been better after actually purchasing and using the product at home, etc., and this may reduce the product's appeal. Therefore, it is desirable for users to be able to understand, before purchasing a product, which products will not lose their appeal even after purchase.

[0005] The present invention has been made in consideration of the above circumstances, and its purpose is to provide a product recommendation system and recommendation method that enable a user to determine, before purchasing a product, which products are unlikely to lose their appeal even after purchase. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems, one aspect of the present invention is a product recommendation system having a memory unit that stores the relationship between vital data and recommended products, a vital data acquisition unit that acquires vital data, which is the measurement result measured by a vital sensor, from a person who has been given an explanation about a product before purchasing the product, a recommendation unit that reads out recommended products from the memory unit according to the vital data acquired from the person, and an output unit that outputs data related to the read out recommended products.

[0007] Another aspect of the present invention is a product recommendation method executed by a computer, which includes acquiring vital data, which is a measurement result measured by a vital sensor, from a person who has received an explanation about a product before purchasing the product, reading out recommended products corresponding to the vital data acquired from the person from a memory unit that stores the relationship between the vital data and the recommended products, and outputting data related to the read recommended products. [Effects of the Invention]

[0008] As described above, according to the present invention, it is possible to know, before purchasing a product, which product's attractiveness is unlikely to decrease even after purchase. [Brief explanation of the drawings]

[0009] [Figure 1] 1 is a schematic block diagram showing the configuration of a product recommendation system S according to an embodiment of the present invention. [Figure 2] FIG. 2 is a functional block diagram showing an outline of functions of an information processing device 40. [Figure 3] FIG. 10 is a diagram illustrating an example of recommended product data. [Figure 4] 10 is a flowchart illustrating a flow of generating and using emotion estimation data. [Figure 5] 1 is a conceptual diagram illustrating the operational flow of a product recommendation system S. [Figure 6] FIG. 10 is a diagram showing an example of data stored in a recommended product storage unit 4021 based on the survey results. DETAILED DESCRIPTION OF THE INVENTION

[0010] A product recommendation system S according to one embodiment of the present invention will be described below with reference to the drawings. Fig. 1 is a schematic block diagram showing the configuration of a product recommendation system S according to one embodiment of the present invention. The product recommendation system S is used, for example, in situations where a salesperson in a store explains a product to a consumer (user) who is considering whether or not to purchase a product, or which product to purchase. The product may be either an interior product or wallpaper. The product explanation may involve a salesperson verbally explaining the product to the consumer, or may involve the consumer looking at or touching a sample of the product, such as an interior product or wallpaper. For example, in the case of wallpaper, a booklet (also called a sample book) contains cut-out portions of actual wallpaper for multiple products, allowing customers to see and touch the actual wallpaper. Wallpaper surfaces can be uneven, reflect light differently depending on the product, and the texture of the actual product can differ from that in photographs. Therefore, by including portions of the actual product rather than simply viewing it as a photograph, customers can see and touch the actual product, making it easier to consider the product.

[0011] The product recommendation system S includes a plurality of terminal devices 10 (terminal device 10a, terminal device 10b, ... terminal device 10n), a plurality of vital sensors 15 (vital sensor 15a, vital sensor 15b, ... vital sensor 15n), a learning device 30, an information processing device 40, a display device D, and a network NW.

[0012] The multiple terminal devices 10 (terminal device 10a, terminal device 10b, ... terminal device 10n) are used by different users, and may be, for example, any of smartphones, tablets, personal computers, etc. Each of the multiple terminal devices 10 is communicably connected to a network NW, and communicates with other devices connected to the network NW.

[0013] The multiple vital sensors 15 (vital sensor 15a, vital sensor 15b, ... vital sensor 15n) are used by different users, and perform biological sensing of the pulse, breathing, etc. from the movements of the body surface of the user, and generate vital data representing the sensing results. The vital data is data in which values ​​representing the measurement results are arranged in chronological order.

[0014] Vital sensor 15 may be a contact type that senses the user's biological activity by contacting a part of the user's body, or a non-contact type that senses the user's biological activity without contacting the user. If vital sensor 15 is a non-contact type, it senses the user's biological activity by, for example, irradiating the user with microwaves using a Doppler sensor and detecting the biological activity based on the reflected waves. The non-contact type vital sensor 15 can measure the user from any distance, but a model that can perform measurements from a distance of, for example, about 10 cm to several meters may be used. When the non-contact vital sensor is installed indoors, for example, it may be attached to a wall, ceiling, fixture, furniture, stand, etc., and may measure the person being measured. Non-contact vital signs sensors are increasingly being installed in homes and businesses, creating an environment where they can be easily used.

[0015] The contact-type vital sensor may be, for example, an existing electronic device that is built into a wristwatch and sold for sale. Such wristwatch-type vital sensors are widely available, and the number of users who already own them is increasing. Furthermore, even if the contact-type vital sensor is not built into a wristwatch, it may be a wearable type that can be worn on the wrist, arm, chest, abdomen, or the like. In this way, at least one of a contact type and a non-contact type may be used as the vital sensor.

[0016] The terminal device 10a and the vital sensor 15a may be used, for example, by a first user, the terminal device 10b and the vital sensor 15b may be used, for example, by a second user, and the terminal device 10n and the vital sensor 15n may be used, for example, in a store that sells products. When the vital sensor 15n is used in a store, a non-contact vital sensor is used and is installed, for example, in a booth for selling products, in a position (any position among a wall, ceiling, fixtures, furniture, stand, etc.) where vital data of a consumer (user) receiving an explanation about the product can be measured.

[0017] The number of combinations of terminal devices 10 and vital sensors 15 may be the same as or different from the number of users or stores. Also, a set of terminal devices 10 and vital sensors 15 may be used by multiple users in rotation. Vital sensor 15a is connected to terminal device 10a via wireless communication by performing a pairing process, and vital data, which is the measurement result, may be transmitted to terminal device 10a wirelessly or via a wired connection. Similarly, vital sensor 15b and terminal device 10b, and vital sensor 15n and terminal device 10n are connected to each other wirelessly or via a wired connection so that they can communicate with each other.

[0018] The contact-type vital sensor 15 may be attached to a person to be measured, and the terminal device 10 may be placed near the person wearing the sensor, and the vital data may be transmitted to the information processing device 40. The non-contact vital sensor 15 may acquire vital data of a person to be measured from a position away from the person, and transmit the vital data to the information processing device 40 from a terminal device 10 installed near the vital sensor 15. When the non-contact vital sensor 15 is used, the terminal device 10 that is the communication partner may be one that is personally owned by the user, or may be one that is installed in a facility such as a store where the non-contact vital sensor 15 is installed.

[0019] The vital sensor 15 may be communicably connected to the network NW without going through the terminal device 10, and may be configured to transmit vital data to other devices connected to the network NW (e.g., the learning device 30, the information processing device 40).

[0020] The display device D has a function of displaying various data and may also have a function of outputting sound. The display device D may be any of a liquid crystal display device, a projector, etc. The display device D displays output data on, for example, a liquid crystal display panel, etc. The display device D is installed in a booth where products are sold so that it can be seen by consumers and sales staff.

[0021] The learning device 30 performs learning using training data and generates a trained model. For example, the learning device 30 learns the relationship between vital data and emotions, and thereby generates an emotion estimation model (trained model) that estimates emotions from vital data.

[0022] The information processing device 40 is communicably connected to the terminal device 10, the vital sensor 15, the learning device 30, the display device D, etc. via the network NW. The information processing device 40 may also be communicably connected to the vital sensor 15 via the network NW. The information processing device 40 may be a physical server or a cloud server provided by a cloud computing service. The information processing device 40 outputs data related to recommended products based on the vital data.

[0023] The network NW may be the Internet, a LAN (Local Area Network), or a combination of these. The network NW may also include a communication network for communication using a television communication method.

[0024] FIG. 2 is a functional block diagram showing an outline of the functions of the information processing device 40. As shown in FIG. The information processing device 40 includes a communication unit 401 , a storage unit 402 , a control unit 403 , an acquisition unit 404 , a feeling estimation unit 405 , a recommendation unit 406 , and an output unit 407 . The communication unit 401 communicates with external devices via the network NW.

[0025] The storage unit 402 stores various types of data. The memory unit 402 is configured by a storage medium, such as a hard disk drive (HDD), flash memory, electrically erasable programmable read-only memory (EEPROM), random access read / write memory (RAM), read-only memory (ROM), or any combination of these storage media. The storage unit 402 may be, for example, a nonvolatile memory.

[0026] For example, the storage unit 402 includes a recommended product storage unit 4021 and an emotion estimation data storage unit 4022 . The recommended product storage unit 4021 stores recommended product data that indicates the relationship between vital data and recommended products. 3 is a diagram showing an example of recommended product data, which is data in which a described product, vital data, emotion data, and recommended product are associated with each other. The recommended product is a product that a salesperson explains to a consumer before purchasing the product. The vital data may be data in which measurements of a vital sensor taken over a certain period are arranged in chronological order. Furthermore, the vital data of the recommended product data may be a feature calculated from data in which measurements of a vital sensor taken over a certain period are arranged in chronological order.

[0027] The emotion data indicates the type of emotion estimated from the vital data. The recommended products are products that are recommended to consumers among the products handled in the store. In Figure 3, the explanatory product "Wallpaper Cross 0001" is associated with vital data, emotional data "calming," and the recommended product "Wallpaper Cross_0001," and the explanatory product "Wallpaper Cross 0001" is associated with vital data, emotional data "happy," and the recommended product "Wallpaper Cross_0010." For example, when a consumer is receiving a verbal explanation about Product A from a salesperson while looking at and touching a sample of Product A, if the emotional data according to the vital data obtained from the consumer is "calming," then "Wallpaper_0001" is the recommended product, and if the emotional data is "happy," then "Wallpaper_0010" is the recommended product. The relationship between such vital data, emotional data, and recommended products may be determined in advance based on past sales, impressions and evaluation comments from consumers who have purchased the products, and the like, and stored in the recommended product storage unit 4021. Furthermore, the recommended product storage unit 4021 may also take into consideration the past sales experience of salespeople with extensive product sales experience, the behavior of consumers when serving them, impressions heard from consumers after the sale, know-how, and the like.

[0028] The emotion estimation data storage unit 4022 stores emotion estimation data. The emotion estimation data is a trained model generated by the learning device 30.

[0029] The control unit 403 controls each unit of the information processing device 40 .

[0030] The acquisition unit 404 acquires various data from the outside. The acquisition unit 404 includes a vital data acquisition unit 4041. The vital data acquisition unit 4041 acquires vital data, which is the measurement results measured by a vital sensor, from a person who has received an explanation about a product before purchasing the product.

[0031] The emotion estimation unit 405 estimates emotion data representing the emotion of the person based on the vital data acquired by the acquisition unit 404.

[0032] The recommendation unit 406 generates data on products to be recommended to consumers (users). For example, the recommendation unit 406 reads out recommended products corresponding to the vital data acquired from the person from the recommended product storage unit 4021. Furthermore, the recommendation unit 406 reads out from the recommended product storage unit 4021 the recommended product corresponding to the vital data obtained from the person and the recommendation level of the recommended product.

[0033] The output unit 407 outputs the data generated by the recommendation unit 406 to the outside via the communication unit 401. The output destination of the output unit 407 may be the display device D or the terminal device 10. For example, the output unit 407 may output data relating to the read recommended products. For example, the output unit 407 may output the degree of recommendation, or may output the recommended product and the degree of recommendation.

[0034] The communication unit 401, control unit 403, acquisition unit 404, emotion estimation unit 405, recommendation unit 406, and output unit 407 of the information processing device 40 may be configured by a processing device such as a CPU (Central Processing Unit) or a dedicated electronic circuit.

[0035] <The relationship between vital data and emotions> FIG. 4 is a flow diagram illustrating the flow of generating and using emotion estimation data. Emotion estimation data is data that represents the correspondence between vital data and emotions. A user U1 who can provide vital data used to generate emotion estimation data is targeted. The user U1 is asked to perform a specific behavior, and vital data is acquired by having a vital sensor 15 that measures the user U1 perform biometric sensing for the period during which the behavior is being performed. This behavior may be related to receiving an explanation of a product available in a store. For example, the behavior may be any of the following, regarding a product that the user is considering whether to purchase: looking at a sample, touching a sample, or receiving a verbal explanation from a salesperson.

[0036] The user U1 may be a consumer who is considering purchasing a product before purchasing the product. Furthermore, a questionnaire about the emotions felt while performing the behavior is conducted on the person who performed the behavior, and the results of the questionnaire are input from the terminal device 10 or a separately provided management device, etc. Then, the vital data of the user U1 while performing the behavior specified as the measurement target and the emotions resulting from the questionnaire are associated and stored as emotion estimation data in the emotion estimation data storage unit 4022. Here, the user to be measured may be a single person, user U1, or measurements may be taken of multiple different users.

[0037] Furthermore, here, the learning device 30 uses as training data the relationship between the vital data of user U1 while performing the behavior specified as the measurement target and the emotions of user U1 ascertained through a questionnaire, thereby learning the relationship between the vital data and emotions, thereby generating a trained model for estimating emotions from the vital data, and transmits the trained model to the information processing device 40. The control unit 403 of the information processing device 40 may receive the emotion estimation model from the learning device 30 and store it in the storage unit 402. In this way, by using the emotion estimation model, the information processing device 40 can estimate emotion data from trends in changes in vital data such as heart rate and pulse rate.

[0038] After the emotion estimation data is stored in the emotion estimation data storage unit 4022 in this way, when vital data of a user U2 who is considering whether or not to purchase a product (for example, a consumer receiving an explanation from a salesperson in a store) is obtained, the emotion estimation unit 405 inputs the emotion data corresponding to this vital data into the learned model stored in the storage unit 402, thereby obtaining emotion data corresponding to the vital data.

[0039] Next, the operation of the product recommendation system S will be described. FIG. 5 is a conceptual diagram illustrating the flow of operations of the product recommendation system S. The operation of the product recommendation system S can be broadly divided into a "preparation phase" and an "execution phase."

[0040] Preparation Phase The preparation phase is a phase in which emotion estimation data (or emotion estimation model) is generated and stored in the emotion estimation data storage unit 4022. Here, multiple users (for example, users who are considering purchasing a product while receiving an explanation of the product before purchasing it) are targeted, and while the users perform actions determined to be the measurement targets (looking at a product sample, touching it, receiving a verbal explanation from a salesperson, etc.) (step S1), vital data is measured for each user using vital sensor 15. By performing measurements using vital sensor 15, vital data that is time-series data that changes according to emotions can be obtained.

[0041] When vital data is measured by the vital sensor 15, the terminal device 10 acquires the vital data from the vital sensor 15. The terminal device 10 asks the user to enter answers to a questionnaire about emotions during the measurement period by the vital sensor 15 along with the acquired vital data, and transmits the vital data and the emotions resulting from the questionnaire to the learning device 30. Learning device 30 acquires such data in which vital data and emotions are associated from each of a plurality of users (step S2) and stores the data in a storage device within learning device 30. This makes it possible to understand the relationship between vital data and emotions.

[0042] The learning device 30 generates an emotion estimation model (trained model) based on the relationship between the vital data and the emotion. Once the learning device 30 generates the emotion estimation model, it stores the generated emotion estimation model in a storage device (step S3). Then, the learning device 30 transmits the generated emotion estimation model to the information processing device 40. The information processing device 40 stores the emotion estimation model transmitted from the learning device 30 in emotion estimation data storage unit 4022. This makes it possible to obtain an emotion estimation model that can generate emotion data corresponding to vital data.

[0043] Execution Phase In the execution phase, a case will be described in which a consumer visits a store to consider a product to purchase and receives an explanation of the product from a salesperson before purchasing the product. In the execution phase, a vital sensor 15 installed in a store measures vital data of a consumer who is receiving a product explanation from a salesperson before purchasing the product. The acquisition unit 404 of the information processing device 40 acquires the vital data measured by the vital sensor 15 and data that can identify the product, such as which product was explained to the consumer (step S5). The data that can identify the product may be input by the salesperson via an input device or the like, transmitted to the information processing device 40, and acquired by the acquisition unit 404.

[0044] The emotion estimation unit 405 estimates emotion data corresponding to the vital data by inputting the acquired vital data into an emotion estimation model stored in the storage unit 402 (step S6). The recommendation unit 406 generates output data by reading recommended products corresponding to the estimated emotion data from the recommended product storage unit 4021 (step S7). The output unit 407 transmits the generated output data to an external device (step S8). The external device may be the display device D, or the output data may be transmitted to the terminal device 10 carried by the consumer receiving the product explanation. In this way, the recommendation unit 406 can read out recommended products according to the vital data by referring to the recommended product storage unit 4021, and the output unit 407 can output the recommended products. As a result, products according to the emotions that arose when looking at a product sample or when touching a product sample are output as recommended products, so that in addition to the preferences that the consumer himself recognizes, recommended products based on emotions that the consumer is not necessarily aware of can also be considered as candidates for purchase, and products can be purchased.

[0045] Furthermore, the recommended product is extracted and output by referring to the recommended product storage unit 4021, so that it is possible to extract the product that corresponds to the vital data when the consumer is receiving an explanation about the product, and the salesperson can recommend a product that corresponds to the consumer's feelings about the product.

[0046] For example, if emotional data according to vital data obtained from a consumer while explaining the product "Wallpaper_0001" is "calming," it is output that "Wallpaper_0001" is a recommended product. In this case, it is possible to convey to the consumer that "Wallpaper_0001" is a recommended product because consumers who purchase it in the hope of feeling calm are often satisfied with the product. On the other hand, if the emotional data corresponding to the vital data obtained from the consumer while explaining the product "Wallpaper_0001" is "happy," it is known from past experience and feedback from consumers who have actually purchased the product that consumers who have purchased "Wallpaper_0010" rather than "Wallpaper_0001" tend to be more satisfied with "Wallpaper_0010," and therefore it is possible to communicate to the consumer that "Wallpaper_0010" is a recommended product.

[0047] Furthermore, even if a salesperson has little experience or knowledge about what products to recommend to a consumer depending on what emotions the consumer is feeling, the information processing device 40 can present recommended products to the salesperson, allowing the salesperson to understand which products to recommend to the consumer. By presenting samples of the recommended products to the consumer and having the consumer look at or touch the samples, new vital data can be measured, and recommended products can be presented according to emotional data based on the measurement results. As a result, the recommended products are gradually narrowed down, allowing the consumer to decide which products to purchase from the recommended products presented.

[0048] <Application Examples> In the above-described embodiment, the recommended product storage unit 4021 has been described as storing the relationship between recommended products and emotional data based on the salesperson's past experience, knowledge, know-how, etc., but it may also be configured to acquire the impressions of people who have actually purchased the products when they used the products, and output the degree of recommendation based on the acquired impressions together with the recommended products. For example, in the preparation phase, the information processing device 40 may obtain vital data from a user who is considering purchasing a product, and then have the user actually purchase the product and input their impressions of using the product (level of satisfaction in terms of indicators such as "I'm glad I purchased it," "I want to continue using it for a long time," "A different color might have been better," or "A plain color instead of a pattern might have been better") through a questionnaire from the terminal device 10, etc. Then, the control unit 403 of the information processing device 40 may aggregate the input questionnaire results and store the aggregated results in the recommended product storage unit 4021 in association with the vital data (or emotional data) at the time when the user was receiving an explanation about the product that was the subject of the questionnaire. For example, FIG. 6 is a diagram showing an example of data stored in the recommended product storage unit 4021 based on the survey results.

[0049] In the data on recommended products shown in Figure 3, each recommended product is associated with a recommendation level. This recommendation level is based on the results of the survey mentioned above. The recommendation level is determined based on the impressions of consumers whose vital data was measured when they actually purchased and used the product, and represents the degree to which the product is recommended. The degree of recommendation may be expressed as a numerical value, with a higher numerical value indicating a higher degree of recommendation. The degree of recommendation can also be said to represent the degree of satisfaction when actually purchasing and using the product. A high degree of satisfaction can also be said to be a high degree of likelihood of recommending the same product to others as the one purchased by oneself. Here, emotional data corresponding to vital data measured when a consumer who actually purchased a product looked at or touched a sample of the product "Wallpaper_0001" before purchasing the product is stored in association with the degree of satisfaction when the consumer actually purchased and used the product when the emotional data was obtained. This allows the information processing device 40 to store, as data, the relationship between the consumer's feelings when considering whether to purchase a product and their impressions after actually purchasing and using the product. It is also possible to store the relationship between the consumer's feelings when receiving an explanation about a product and their impressions after actually purchasing and using the product.

[0050] By preparing such data in the preparation phase, in the execution phase, the information processing device 40 can extract and output recommended products and their recommendation levels corresponding to emotional data based on vital data when a consumer considering a purchase is receiving an explanation about the product. This allows the consumer to consider the level of recommendation of the recommended products while considering which products to purchase. In addition, emotional data based on the vital data of consumers who have actually made a purchase is associated with the degree of recommendation for the recommended product, so data can be used that reflects the emotions felt by those who have actually purchased the product.If the emotional data generated when receiving an explanation of the product is similar, the level of satisfaction after purchasing the product is likely to be similar as well, which has the advantage of making it easier to consider products based on the degree of recommendation.

[0051] In addition, the information processing device 40 acquires vital data and survey results (degree of recommendation) obtained from different consumers for the same product, estimates emotional data according to the vital data, and outputs the estimated emotional data and survey results to the learning device 30. The learning device 30 may learn the relationship between emotional data and questionnaire results, thereby generating a recommendation degree estimation model (trained model) that estimates the degree to which a product is recommended from emotional data based on vital data. The learning device 30 may then transmit the generated recommendation degree estimation model to the information processing device 40, where it may be stored in the storage unit 402 of the information processing device 40. Furthermore, such a recommendation degree estimation model may be generated for each product. This makes it possible to learn the relationship between emotional data obtained when a product is explained to a user (by looking at a sample, touching the sample, etc.) before purchasing the product, and the user's impressions and satisfaction level when using the product after actually purchasing it. The consumer who is the subject of the questionnaire and the consumer who is considering purchasing the product may be different consumers.

[0052] According to the embodiment described above, a consumer may experience a discrepancy between their perceived preferences for a product and the internal feelings, such as "delight," "comfort," or "happiness," that arise when they come into contact with the product, even though they may not necessarily be aware of it themselves. If a consumer purchases a product in this state, they may, in some cases, become aware after the purchase that a different color or a different product might have been better. In contrast, according to the above-described embodiment, product suggestions can be received based on emotional data derived from vital data, so that products can be recommended based on emotions that the user is not necessarily aware of, in addition to the user's perceived preferences regarding the product. This allows the user to select products using a new approach of selecting products based on psychology and emotions that the user is not necessarily aware of, rather than the approach of selecting products based on the user's perceived preferences. This allows the user to receive product suggestions that reduce the discrepancy between the user's perceived preferences and emotions that the user is not necessarily aware of, and makes it possible to identify product candidates that are unlikely to cause changes in the user's feelings toward the product after purchase. This also makes it possible to identify products whose appeal is least likely to decrease after purchase, thereby increasing the likelihood of purchasing a product whose appeal will not decrease even after purchase.

[0053] In the above-described embodiment, the recommended products may be any product. For example, there are products that require consumer interaction after purchase and products that do not require consumer interaction. Examples of products that require interaction include televisions, refrigerators, lighting devices, and storage shelves. Examples of products that do not require interaction include wallpaper. Products that require interaction can be purchased by actually operating a sample in a store and checking the operation feel, but products that do not require interaction do not require the consumer to actually try operating the product, as this prevents the consumer from checking the operation feel and thus prevents the consumer from actually operating the product as a clue to deciding whether to purchase it. Therefore, consumers must select products based on factors other than the operation feel, such as appearance and texture, and may make careful selections. Even in such cases, the information processing device 40 can recommend recommended products based on emotional data, thereby providing more clues for product selection.

[0054] Furthermore, in the above-described embodiment, the case where the product to be recommended is a specific product has been described, but it is also possible to recommend product attributes rather than specifying the product itself. Product attributes may be the color, pattern, material, etc. of the product. For example, recommendation content such as "I recommend a white product for you" or "I recommend a floral pattern for you" may be output in accordance with emotional data based on vital data when the consumer is receiving an explanation about product A. This allows the consumer to narrow down the products by their attributes and then select a product from the list.

[0055] Furthermore, according to the above-described embodiment, when a consumer is receiving a product explanation, if the store acquires and uses their vital data, there is no benefit to the user, and the consumer may become uneasy about how the vital data will be used. However, if the salesperson explains to the consumer in advance that by measuring their vital data, they will be able to enjoy the benefit of being introduced to products that are more closely aligned with their own psychology, the consumer will be able to understand the measurement of their vital data. In this way, consumers can gain benefits from providing their vital data.

[0056] The information processing device 40 in the above-described embodiment may be implemented by a computer. In this case, a program for implementing the functions may be recorded on a computer-readable recording medium, and the program may be loaded into a computer system and executed. The term "computer system" as used herein includes hardware such as an OS and peripheral devices. The term "computer-readable recording medium" refers to portable media such as flexible disks, optical magnetic disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into a computer system. The term "computer-readable recording medium" may also include media that dynamically store programs for a short period of time, such as communication lines used when transmitting programs over a network such as the Internet or a telephone line, or media that store programs for a fixed period of time, such as volatile memory within a computer system serving as a server or client. The program may be a program that implements only a portion of the functions described above, or may be a program that can implement the functions described above in combination with a program already stored in the computer system, or may be implemented using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0057] Although an embodiment of the present invention has been described above in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and includes designs within the scope of the gist of the present invention. [Explanation of symbols]

[0058] 10, 10a, 10b, 10n terminal equipment 15, 15a, 15b, 15n Vital Sensors 30 Learning Device 40 Information processing equipment 401 Communications Department 402 Storage section 403 Control Unit 404 Acquisition Department 405 Emotion estimation part 406 Recommendation Department 407 Output Section 4021 Product storage section 4022 Emotion estimation data storage unit 4041 Vital Data Acquisition Unit NW Network D Display device S Product Recommendation System U1 User U2 users

Claims

1. a storage unit that stores the relationship between vital data and recommended products; a vital data acquisition unit that acquires vital data, which is a measurement result measured by a vital sensor, from a person who has received an explanation about the product before purchasing the product; a recommendation unit that reads out from the storage unit recommended products according to the vital data acquired from the person; an output unit that outputs the read data regarding the recommended products; A product recommendation system with

2. the storage unit stores a relationship between vital data acquired from a user who previously purchased a product and a recommendation degree based on an impression of the product by the user who previously purchased the product, and the recommendation unit reads out from the storage unit the recommended product and a recommendation degree of the recommended product corresponding to the vital data obtained from the person; The output unit outputs the recommended product and the degree of recommendation. The product recommendation system according to claim 1 .

3. The product is either an interior product or wallpaper, The explanation of the product is to see or touch a sample of the product. The product recommendation system according to claim 1 or 2.

4. A computer-implemented method for recommending products, comprising: acquiring vital data, which is a measurement result measured by a vital sensor, from a person who has received an explanation about the product before purchasing the product; reading out recommended products corresponding to the vital data acquired from the person from a storage unit that stores the relationship between the vital data and the recommended products; Outputting the read data on the recommended products A method for recommending products that includes:

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