Product information processing system, product information processing method, product information processing program

Abstract

To enable, according to one of the objectives of the present disclosure, a consumer to further easily or further appropriately select a sensitivity commodity like a flagrance commodity at a market place.SOLUTION: One of the present disclosure may be a commodity information processing system 101. The commodity information processing system 101 according to the present disclosure may include, as a functional unit, a commodity explanation creating unit that creates scientific data 110 on a sensitivity commodity based on scientific expected efficacy data 112 on such a sensitivity commodity.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure generally relates to technologies related to the evaluation of products or services that affect human sensibilities and emotions (for example, fragrances, music, paintings, plays, design patterns, literature, clothing, automobiles, houses, etc., hereinafter collectively referred to as "sensory products"). The present disclosure relates to, for example, a method and system for processing information on fragrance products such as air fresheners, perfumes, and eau de cologne.

Background Art

[0002] Generally, when consumers consider purchasing or the like of a sensory product, they select and purchase or the like of a sensory product by actually trying a sample of the sensory product, referring to the description of the text explaining the sensory product, or referring to the evaluation of the sensory product made by others. For example, when a consumer selects a fragrance product (a product containing a fragrance), the selection is made based on the actual feeling of smelling the fragrance product, the description in text about the fragrance (for example, materials such as jasmine, musk, sandalwood, etc.) used in the fragrance product, and the tone of the fragrance (fragrance note. For example, fragrance types such as oriental, floral, citrus, etc.) provided by the fragrance product, or the evaluation of the fragrance product written by others.

[0003] In addition, the following prior art documents related to fragrances exist. The fragrance information providing device disclosed in Patent Document 1 (Japanese Patent Application Laid-Open No. 2021-108918) acquires electroencephalogram information of a plurality of users, performs sensory analysis on the electroencephalogram information of those plurality of users, generates sensory information in which the sensibilities of the users are quantified, and outputs fragrance information indicating one fragrance information suitable for all users using the sensory information. The fragrance spraying device disclosed in Patent Document 2 (Japanese Patent Publication No. 2002-282231) releases fragrance to the practitioner, measures brain waves, analyzes the central nervous system's biological rhythm, evaluates the degree of comfort and relaxation from the central rhythm, and controls the amount of fragrance released so that the fragrance concentration is appropriate for the person receiving the treatment. The mobile terminal disclosed in Patent Document 3 (International Publication No. 2018 / 163361) acquires the user's physical and mental state from physiological indicators, inputs the user's future information, determines a recipe including the type and blending ratio of fragrances based on the physical and mental state and future information, and transmits the recipe to a fragrance generating device. [Prior art documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2021-108918 [Patent Document 2] Japanese Patent Publication No. 2002-282231 [Patent Document 3] International Publication No. 2018 / 163361 [Overview of the project] [Problems that the invention aims to solve]

[0005] When consumers consider purchasing emotional products, they may try samples of the products, refer to descriptions in texts, or read reviews of emotional products by others. Even if they select and purchase emotional products based on these factors, there is no guarantee that the purchased emotional products will actually meet the consumer's needs. For example, when consumers choose a fragrance product, they may base their selection on their actual smell, textual descriptions of the fragrances used and the scent profile, or reviews written by others. However, whether the chosen fragrance product actually meets their needs remains speculative. In reality, consumers only truly know whether a purchased fragrance product meets their needs after they have actually bought it.

[0006] Furthermore, regarding fragrance products, it might seem at first glance that consumers can easily choose a product that suits their preferences by visiting a store and actually smelling the fragrances. However, in reality, consumers' sense of smell can become numb after smelling many products, and it often becomes more difficult to make a choice. Thus, choosing the right fragrance product is not easy for consumers.

[0007] Furthermore, consumers' criteria for selecting fragrance products may include not only subjective preferences such as whether they like or dislike a scent, but also the objective effects or benefits of the scent on human psychology and physiology. For example, in aromatherapy, consumers seek fragrance products expecting objective psychological or physiological effects such as relaxation, alertness, improved concentration, increased motivation, and a brighter mood. However, the average consumer lacks knowledge about what effects can be expected from different fragrance products.

[0008] Patent documents 1, 2, and 3, mentioned above, disclose technologies that utilize the results of measuring human brainwaves in relation to the selection and blending of fragrances. However, these prior arts only involve spraying or otherwise applying fragrance-containing substances to people. In other words, these prior arts are insufficient to help individual consumers choose products that are suitable for them in a market where many different fragrance products are sold.

[0009] The circumstances described above exist not only in fragrance products but also in other areas of consumer goods.

[0010] Based on the above, one of the purposes of this disclosure may be to enable consumers to more easily or accurately select sensory products such as fragrances in the market.

[0011] If consumers can more easily and accurately select sensory products such as fragrances in the market, it will enhance the value of consumers' lives through sensory products and contribute to the development of the sensory products industry. [Means for solving the problem]

[0012] To achieve at least one of the above objectives, the features that this disclosure may have include, for example, the following: One of the disclosed items is a product information processing system. The product information processing system may have a product description creation unit. The product description creation unit creates scientific descriptive data for a product based on expected efficacy data for the product. [Effects of the Invention]

[0013] The product information processing system disclosed herein generates scientific descriptive data for sensory products. This scientific descriptive data is based on expected efficacy data for sensory products. Therefore, by referring to the scientific descriptive data, consumers can obtain information about sensory products that is based on objective scientific evidence. Furthermore, even if the description content and methodology of the expected efficacy data for sensory products itself are specialized and difficult for consumers to understand, the scientific descriptive data itself is easy for consumers to understand.

[0014] As described above, this disclosure will enable consumers to more easily and accurately select sensory products such as fragrances in the market.

[0015] A product information processing method and a product information processing program that achieve the same as the processing realized by the above product information processing system can also obtain the same operational effects as the above product information processing system. In many cases, costs are reduced in the form of a program. In a program, design changes related to processing are also easily made. Features that the present disclosure other than the above may have, and the corresponding operational effects thereof are disclosed in this specification, the claims or the drawings.

Brief Description of the Drawings

[0016] [Figure 1] Shows the overall system configuration. [Figure 2] Shows the functional configuration of the product information processing system. [Figure 3] Shows the computer architecture of the product information processing system. [Figure 4] Shows the flowchart of the expected efficacy data creation unit. [Figure 5] Shows an example of the sensory survey result data. [Figure 6] Shows an example of the sensory survey result data. [Figure 7] Shows an example of the sensory survey result data. [Figure 8] Shows an example of the sensory survey result data. [Figure 9] Shows an example of the electroencephalogram survey result data. [Figure 10] Shows an example of the electroencephalogram survey result data. [Figure 11] Shows an example of the electrocardiogram survey result data. [Figure 12] Shows an example of the electrocardiogram survey result data. [Figure 13] Shows an example of the component survey result data. [Figure 14] Shows the flowchart of the product description creation unit. [Figure 15] Shows an example of the process of creating a product description. [Figure 16] Shows the flowchart of the product classification unit. [Figure 17] Shows an example of the product classification data. [Figure 18] A flowchart for the guide creation section is shown. [Figure 19] A flowchart for the guide provision section is shown. [Figure 20] A flowchart for the product proposal department is shown. [Figure 21] A flowchart for the designated product information provision department is shown. [Figure 22] A flowchart for the Market Support Department is shown. [Figure 23] A flowchart for the Development Support Department is shown. [Modes for carrying out the invention]

[0017] Embodiments of this disclosure will be described in detail below with reference to the drawings. The embodiments described below are not intended to limit the disclosures in the claims, and not all elements and combinations thereof described in the embodiments are necessarily essential to the solutions of this disclosure. The following descriptions and drawings are illustrative for illustrating this disclosure and have been omitted and simplified as appropriate for clarity. This disclosure can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural. The positions, sizes, shapes, and ranges of the components shown in the drawings may not represent the actual positions, sizes, shapes, and ranges in order to facilitate understanding of the invention. Therefore, this disclosure is not necessarily limited to the positions, sizes, shapes, and ranges disclosed in the drawings. Each of the systems, devices, or functional units of this disclosure may be a single, integrated hardware unit, or it may be divided into multiple parts that work together to perform their respective functions. Several systems, devices, or functional units may be integrated hardware-wise. Each system, device, or functional unit may be implemented by having a computer execute software (programs) (as shown in Figure 3). Some of the functions of the system, device, or functional unit may be implemented in hardware (e.g., hardwired logic or field-programmable gate array (FPGA)), and the remaining functions may be implemented by executing software (programs). All of the functions of each system, device, or functional unit may be implemented in hardware. Some or all of the steps shown in the flowcharts etc. described in this disclosure may be implemented in hardware. One or more systems, devices, or functional units of the Disclosure may be implemented from one or more hardware resources. For this purpose, each of the systems, devices, or functional units of the Disclosure may be implemented virtually. For example, virtual machine or container techniques may be used. Furthermore, some of the functions of the systems, devices, or functional parts of this disclosure may be realized not only by artificial systems such as computers, artificial intelligence, or hardware devices, but also by a combination of artificial systems and human involvement. The programs disclosed herein are included in the general concept of software, where software and hardware resources work together to construct a specific information processing system (system) or method of operation suited to a particular purpose. In other words, the programs disclosed herein are not limited to any particular type or form of program. Furthermore, the programs may initially be recorded in a compressed format. Reference numbers used in multiple drawings indicate that they are equivalent. In flowcharts, rectangular boxes represent processing steps, and hexagonal boxes represent conditional branching steps. In flowcharts, "step" is abbreviated as "S".

[0018] 1. Overall system configuration (Figure 1) Figure 1 shows the overall system configuration (and the information handled) encompassing the product information processing system that is an embodiment of this disclosure. Note that not all configurations (and the information handled) shown in Figure 1 are mandatory. Furthermore, the existence of configurations (and the information handled) other than those shown in Figure 1 is not prohibited. In the overall system configuration 100, the product information processing system 101, the data pool 102, the consumer terminal 103, and the product information output device 104, which are embodiments of the present disclosure, may be able to communicate with each other via the communication system 105. The product information processing system 101 may have a functional configuration consisting of the functional units shown in Figure 2, described later. Furthermore, the product information processing system 101 may be implemented using the computer architecture shown in Figure 3, described later. The product information processing system 101 may be, for example, a server or personal computer owned by a company that provides product information about emotional products. The product information processing system 101 may also be capable of running applications for document editing. Moreover, the product information processing system 101 may be capable of utilizing a learning model trained by machine learning (e.g., a generative AI). The data pool 102 can be any device capable of recording the various types of data (information) shown in Figure 1. The data pool 102 may be, for example, a server or data storage owned by a company that provides product information about emotional products, or it may be a web server or data storage on the cloud used by a company that provides product information about emotional products. The consumer terminal 103 is a terminal used by consumers to obtain product information about sensory products. The consumer terminal 103 may be, for example, a smartphone, a personal digital assistant (PDA), or a personal computer handled by the consumer. The product information output device 104 displays or outputs product information about emotional products acquired by consumers. The product information output device 104 may also record product information about emotional products acquired by consumers. The product information output device 104 may be, for example, a display, a printer, or a web server. Furthermore, product information about the sensory product acquired by the consumer may be displayed or output (and even recorded) on the consumer terminal 103. In this case, the product information output device 104 does not necessarily have to be used. The communication system 105 enables mutual communication between the product information processing system 101, the data pool 102, the consumer terminal 103, and the product information output device 104. However, the communication system 105 does not need to enable mutual communication for all combinations of the product information processing system 101, the data pool 102, the consumer terminal 103, and the product information output device 104. The communication system 105 only needs to enable the communication used to realize this disclosure. The communication system 105 may be one or more combinations of, for example, a local area network (LAN), a world area network (WAN), a communication cable, a data bus, etc.

[0019] 2. Overview of the data (information) handled in this disclosure (Figure 1) Figure 1 shows the data (information) used in this disclosure as data (information) recorded in data pool 102. In the data pool 102, product data 106 is recorded for each sensory product. That is, for each sensory product, product identification data 107, product description data 108, scientific expected efficacy data 112, and scientific survey results data 117 are recorded. Product description data 108 may include general description data 109, scientific description data 110, and integrated description data 111. Scientific expected efficacy data 112 may include sensory-based efficacy data 113, electroencephalogram-based efficacy data 114, electrocardiogram-based efficacy data 115, and ingredient-based efficacy data 116. Scientific survey results data 117 may include sensory survey results data 118, electroencephalogram survey results data 119, electrocardiogram survey results data 120, and ingredient survey results data 121. Furthermore, the data pool 102 records the following data (information) common to all sensory products: analysis results-efficacy correlation data 122 (first correlation data), product classification data 123, product selection guide data 124, consumer trend data 128, classification-outcome correlation data 129 (second correlation data), and business support data 130. The product selection guide data 124 may include category selection questionnaire data 125, product list data 126, and link information 127. The business support data 130 may include sales support data 131 and development support data 132. Note that in Figure 1, the word "data" at the end of reference numbers 109 to 111, 113 to 116, 118 to 121, and 125 has been omitted.

[0020] The details and usage of each piece of data (information) recorded in the data pool 102 in Figure 1 will be explained later, along with the description of the processing performed by the product information processing system 101. Here, an overview of the data (information) recorded in the data pool 102 is provided.

[0021] Product identification data 107 is information that identifies the emotional product. Product identification data 107 may be, for example, the name of the emotional product or an identifier (identification number) attached to the emotional product.

[0022] Product description data 108 is data primarily intended to provide consumers with information describing emotional products. Product description data 108 may be used as digital data. Product description data 108 may also be information displayed on a display device of a consumer terminal 103 or on a display or output device 307 (see Figure 3) within the product information processing system 101 (for example, information used to be displayed as a web page). Product description data 108 may also be information output to the display or output device 307 within the product information processing system 101 or to a product information output device 104 (for example, information printed on paper). The printed material on which product description data 108 is printed may be in the form of a brochure or report. Product description data 108 provides consumers with easily usable information when they select and purchase emotional products, and also provides consumers with scientifically or objectively valid information.

[0023] The general descriptive data 109 (which may be included in the product description data 108) is information that describes the sensory product and is information that has been traditionally used to explain sensory products to consumers, etc. For example, if the sensory product is a fragrance product, the general descriptive data 109 may describe the fragrances and tones (fragrance notes, for example, those that indicate how the scent changes over time) used in the fragrance product. For example, an example of general descriptive data 109 for a white musk product is shown in Figure 15.

[0024] Scientific descriptive data 110 (which may be included in product description data 108) is information that describes an emotional product and is a description of the emotional product based on scientific information. Scientific descriptive data 110 may be based on, for example, one or both of the scientific survey results data 117 and the scientific expected efficacy data 112. Scientific descriptive data 110 may be, for example, an electroencephalogram (EEG) distribution map, which is an example of EEG survey results data 119, and a set of text that describes the expected efficacy derived from EEG-based efficacy data 114. For example, an example of scientific descriptive data 110 for a white musk product is shown in Figure 15.

[0025] Integrated descriptive data 111 (which may be included in product description data 108) is information that describes an emotional product and is created by integrating general descriptive data 109 and scientific descriptive data 110. The integrated descriptive data 111 may have a layout designed for displaying or outputting general descriptive data 109 and scientific descriptive data 110 in a way that is easy for consumers to view.

[0026] Scientific survey results data 117 is data obtained as a result of conducting some kind of scientific survey regarding the emotional product. Scientific survey results data 117 may be data (information) concerning events that can be scientifically or objectively investigated (for example, physical (physiological) events or psychological (emotional) events) that occur in people who use the emotional product, or it may be data (information) obtained by analyzing the emotional product itself.

[0027] The emotional survey data 118 (which may be included in the scientific survey data 117) is data (information) obtained as a result of using scientific survey methods on the emotional perception of those who used the emotional product. The emotional survey data 118 may, for example, be data showing the results of an emotional test. The emotional survey data 118 may, for example, be data (information) obtained by statistically processing the results of an emotional questionnaire given to those who used the emotional product for a group of people who used the emotional product. Examples of emotional survey data 118 are the combination of Figures 5 and 6 described below, and the combination of Figures 7 and 8 described below.

[0028] The electroencephalogram (EEG) survey data 119 (which may be included in scientific survey data 117) is data (information) obtained as a result of scientific survey methods being used on the brainwaves of people who used the sensory product. The EEG survey data 119 may be, for example, an EEG distribution (a distribution map of EEG frequencies) as shown in Figure 9 below. Alternatively, the EEG survey data 119 may be, for example, a spectrum of EEG frequencies (information on 1 / f fluctuations in EEG) as shown in Figure 10 below. The electrocardiogram (ECG) survey data 120 (which may be included in the scientific survey data 117) is data (information) obtained as a result of using scientific survey methods on the electrocardiograms of people who used the emotional product. The ECG survey data 120 may be data (information) obtained by statistically processing the sympathetic nervous system activity index (LF) and autonomic nervous system stress index (LF / HF), which are derived from the electrocardiograms of people who used the emotional product, as shown in the combination of Figures 11 and 12, for a group of people who used the emotional product. Generally, analytical data related to electroencephalograms (EEGs) and electrocardiograms (ECGs) represent physiological phenomena that have a high correlation with psychological and emotional states. Therefore, if EEG survey data 119 and ECG survey data 120 for each emotional product are utilized, it is expected that each emotional product can be associated with one of the various psychological and emotional states. In other words, when consumers desire to be in a specific psychological or emotional state, it is expected that it will be possible to identify emotional products that are associated with that specific state.

[0029] The component analysis data 121 (which may be included in the scientific analysis data 117) is data (information) obtained as a result of using scientific research methods on the components contained in the consumer goods. The component analysis data 121 may be, for example, component analysis data (component table) obtained by chromatography, as shown in Figure 13.

[0030] When scientific expected efficacy data 112 is created based on scientific survey results data 117, analysis results-efficacy correlation data 122 (first correlation data) may be used. Analysis results - Efficacy correlation data 122 (first correlation data) may be data (information) that shows the relationship between the content of scientific survey results on a sensory product and the expected efficacy of that sensory product. Analysis results - Efficacy correlation data 122 (first correlation data) may be data (information) that shows knowledge or theories with a certain degree of reliability obtained from past academic research, for example.

[0031] Scientific expected efficacy data 112 is data that indicates some expected efficacy of a sensory product. Scientific expected efficacy data 112 may be data (information) relating to scientifically or objectively observable efficacy (e.g., physical (physiological) efficacy or psychological (emotional) efficacy) caused by using the sensory product, or it may be data (information) relating to efficacy (e.g., physical (physiological) efficacy or psychological (emotional) efficacy) that can be inferred by analyzing the sensory product itself. Scientific expected efficacy data 112 may be obtained by estimating efficacy (e.g., physical (physiological) efficacy or psychological (emotional) efficacy) based on scientific survey results data 117. The sensory-based efficacy data 113 (which may be included in the scientific expected efficacy data 112) is data (information) that shows the expected efficacy of a sensory product in relation to the sensory perception of the user of the sensory product. The sensory-based efficacy data 113 may be generated, for example, based on the sensory survey results data 118 and the analysis results-efficacy correlation data 122 (first correlation data). The electroencephalogram-based efficacy data 114 (which may be included in the scientific expected efficacy data 112) is data (information) that shows the expected efficacy of the sensory product in relation to the brainwaves of the person who used the sensory product. The electroencephalogram-based efficacy data 114 may be generated, for example, based on the electroencephalogram survey results data 119 and the analysis results-efficacy correlation data 122 (first correlation data). ECG-based efficacy data 115 (which may be included in scientific expected efficacy data 112) is data (information) that shows the expected efficacy of the sensory product in relation to the electrocardiogram of a person who has used the sensory product. ECG-based efficacy data 115 may be generated, for example, based on electrocardiogram survey results data 120 and analysis results-efficacy correlation data 122 (first correlation data). The ingredient-based efficacy data 116 (which may be included in the scientific expected efficacy data 112) is data (information) that indicates the expected efficacy of the product, associated with the ingredients contained in the product. The ingredient-based efficacy data 116 may be generated, for example, based on ingredient survey results data 121 and analysis results-efficacy correlation data 122 (first correlation data).

[0032] Product classification data 123 is data (information) that shows the results of classifying emotional products so that they form a set of emotional products that have the same or similar expected effects. Product classification data 123 may be data (information) that shows the results of assigning each emotional product to an emotional product classification (category) from multiple perspectives, for example, as shown in Figure 17 below.

[0033] The category selection questionnaire data 125 is designed so that when consumers provide their answers (or selections made by choosing from the answer options) to the questionnaire shown in the data, the classification (category) of the emotional products that best suits the consumer's needs can be determined. Product list data 126 is data (information) that shows a list of emotional products included in each classification (category) of emotional products, which is determined according to the consumer's answers or selections to the questionnaire shown in category selection questionnaire data 125. Link information 127 is information about links that identify the location of product description data 108 (for example, scientific description data 110 or integrated description data 111) for each of the sensory products listed in product list data 126.

[0034] Consumer trend data 128 is data (information) that shows the trends in demand for each consumer of sensory products. Consumer trend data 128 may include sales information for each sensory product. Classification-Outcome Correlation Data 129 (Second Correlation Data) is data (information) that shows the level and degree of demand among consumers for each classification (category) of sensory products. Classification-Outcome Correlation Data 129 (Second Correlation Data) may be created, for example, based on consumer trend data 128 and product classification data 123.

[0035] Sales support data 131 is data (information) that provides reference information for companies selling emotional products to formulate sales policies (sales promotion activity policies). Sales support data 131 may include, for example, classifications (categories) of emotional products for which consumer demand is expected (relatively high) or information that identifies emotional products. Development support data 132 is data (information) that provides reference information for companies developing sensory products to formulate development policies when developing new sensory products. Development support data 132 may include, for example, classifications (categories) of sensory products for which consumer demand is expected (relatively high) or information that identifies sensory products. Furthermore, development support data 132 may include, for example, information showing the profile of sensory products for which consumer demand is expected (relatively high) (e.g., information showing expected effects, classification, contained fragrances, tone, characteristic ingredients).

[0036] 3. Functional configuration of the product information processing system (Figure 2) Figure 2 shows the functional configuration 200 of the product information processing system 101. Note that not all configurations (or the information handled) shown in Figure 2 are mandatory. Furthermore, the existence of functional configurations other than those shown in Figure 2 is not prohibited.

[0037] The product information processing system 101 may have each of the functional units shown as "units" in Figure 2. As shown in Figure 2, the product information processing system 101 may implement some or all of the following functional units: the expected efficacy data creation unit 202, the product description creation unit 203, the product classification unit 204, the guide creation unit 205, the guide provision unit 207, the product proposal unit 208, the designated product information provision unit 209, the market support unit 211, or the development support unit 212. The guide provision unit 207 and the product proposal unit 208 may be collectively referred to as the product selection support unit 206. The market support unit 211 and the development support unit 212 may be collectively referred to as the business support processing unit 210. As has already been pointed out, these functional components may be implemented through the execution of a program (software). Alternatively, these functional components may be implemented more in hardware.

[0038] The functions and processes performed by each of the functional units shown in Figure 2 will be explained later, along with the description of the processes performed by the product information processing system 101. Here, an overview of each function of the functional unit is provided.

[0039] The expected efficacy data creation unit 202 creates scientific expected efficacy data 112 for each of the sensory products. The expected efficacy data creation unit 202 may, for example, create scientific expected efficacy data 112 for each of the sensory products using scientific survey results data 117 and analysis result-efficacy correlation data 122 (first correlation data) for each of the sensory products, based on the flowchart in Figure 4 described later.

[0040] The product description creation unit 203 creates scientific descriptive data 110 or integrated descriptive data 111 for each of the emotional products. For example, the product description creation unit 203 may create scientific descriptive data 110 for each of the emotional products using scientific expected efficacy data 112 and scientific survey results data 117 for each of the emotional products, based on the flowchart in Figure 14 described later. Alternatively, the product description creation unit 203 may create integrated descriptive data 111 for each of the emotional products using general descriptive data 109 and scientific descriptive data 110 for each of the emotional products, based on the flowchart in Figure 14 described later.

[0041] The product classification unit 204 assigns each emotional product to an emotional product classification (category) and creates product classification data 123 that reflects the assignment results. The product classification unit 204 may, for example, assign each emotional product to an emotional product classification (category) based on the flowchart in Figure 16, using general descriptive data 109, scientific expected efficacy data 112, and scientific survey results data 117 for each emotional product. Alternatively, the product classification unit 204 may create product classification data 123 as shown in Figure 17, for example.

[0042] The guide creation unit 205 creates product selection guide data 124 to assist consumers in selecting emotional products. For example, based on the flowchart in Figure 18, the guide creation unit 205 may use product classification data 123 to create category selection questionnaire data 125, product list data 126, and link information 127, and combine these category selection questionnaire data 125, product list data 126, and link information 127 to create the product selection guide data 124.

[0043] The guide provision unit 207 uses product selection guide data 124 to assist consumers in selecting emotional products and outputs information that identifies an emotional product or the classification (category) to which the emotional product belongs, or product description data 108 about the emotional product, based on the information entered by the consumer. For example, the guide provision unit 207 may display or output based on category selection questionnaire data 125 based on the flowchart in Figure 19, identify a list of emotional products (product list data 126) corresponding to the consumer's response to the category selection questionnaire data 125 or the selection result of the answer choices, display or output the identified list of emotional products, receive information on the selection of an emotional product from the list of emotional products, obtain product description data 108 about the emotional product using link information 127 about the emotional product indicated by the selection information, and display or output the obtained product description data 108.

[0044] The product suggestion unit 208 identifies consumer needs, identifies categories of emotional products that meet those needs, selects emotional products belonging to the identified categories, and displays or outputs information identifying the selected emotional products or product description data 108 for those emotional products. The product suggestion unit 208 may, for example, make suggestions to consumers regarding emotional products based on the flowchart in Figure 20. Here, consumer needs may include expected effects, which are the effects that consumers expect from emotional products.

[0045] The designated product information provision unit 209, upon receiving information identifying the emotional product, acquires product description data 108 for the identified emotional product and displays or outputs the acquired product description data 108. The designated product information provision unit 209 may, for example, provide the consumer with product description data 108 for the identified emotional product based on the flowchart in Figure 21.

[0046] The Market Support Department 211 creates sales support data 131, which serves as reference information for companies selling emotional products to formulate sales policies (promotional activity policies) for those products. For example, the Market Support Department 211 may create sales support data 131 that includes information on emotional products or categories to which emotional products belong that are expected to have high demand, using consumer trend data 128 and product classification data 123, based on the flowchart in Figure 22. The Market Support Department 211 may also create classification-performance correlation data 129 (second correlation data) that shows the correlation between the categories to which emotional products belong and the sales performance of emotional products belonging to those categories, based on the flowchart in Figure 22.

[0047] The Development Support Department 212 creates development support data 132, which serves as reference information for companies developing emotional products to formulate development policies indicating what kind of emotional products they will develop. For example, the Development Support Department 212 may create development support data 132 that includes information on emotional products or categories to which emotional products belong, based on the flowchart in Figure 23, using consumer trend data 128 and product classification data 123. The Development Support Department 212 may also create classification-performance correlation data 129 (second correlation data) that shows the correlation between the categories to which emotional products belong and the sales performance of emotional products belonging to that category, based on the flowchart in Figure 23. Furthermore, the Development Support Department 212 may identify categories to which emotional products with expected sales performance belong and include information on emotional products belonging to the identified categories in the Development Support Data 132. Here, information regarding the sensory product may include one or more of the following: expected efficacy data for the sensory product, the classification (category) to which the sensory product belongs, the fragrances contained in the sensory product, the tone of the sensory product, or the components that characterize the sensory product.

[0048] Since the product information processing system 101 in the embodiment of this disclosure has the functional configuration described above, it can have the effects shown in the [Effects of the Invention] section above.

[0049] 4. Computer architecture for realizing embodiments of the disclosure (Figure 3) Figure 3 shows a computer architecture 300 for realizing the product information processing system 101 of the embodiment of this disclosure. To realize the product information processing system 101, the information processing device 301, the storage device 302, the non-volatile recording medium (recording device) 303, the external recording medium drive 304, the input device 306, the display or output device 307, the communication device 308, and some or all of the external input / output ports 309 may be interconnected at the interconnection unit 311. (Note that some or all of the interconnection unit 311 may be a network. In that case, the product information processing system 101 will be realized by multiple devices connected via the network.) The information processing device 301 may be, for example, a processor. Examples of such processors include a CPU, MPU, or GPU. Alternatively, the processor referred to herein may be any other semiconductor device that performs a predetermined process. The information processing device 301 may also be one or more (micro)processors. The storage device 302 may be, for example, memory. The non-volatile recording medium (recording device) 303 may be, for example, non-volatile memory (e.g., flash memory) or a non-volatile disk device. The external recording medium drive 304 may be, for example, a disk drive. The input device 306 may be, for example, a mouse, keyboard, imaging device, sensor, touch panel, or pointing device. The display or output device 307 may be, for example, a display, printer, or speaker. The communication device 308 may be, for example, a wired communication device or a wireless communication device. The communication device 308 may be a network interface device (NIC) that controls communication with other systems, devices, terminals, or servers according to a predetermined protocol. The interconnection unit 311 may be, for example, a bus or crossbar switch. (As mentioned above, part or all of the interconnection section 311 may be a network.)

[0050] The non-volatile recording medium (recording device) 303 may record various programs included in the program group 331 (for example, programs for realizing the functional configuration related to this disclosure; for example, various programs for implementing each of the functional units realized in the product information processing system 101), various data groups included in the data group 332, or various information 333. The program group 331 may include various programs for realizing each of the functional units designated as "units" in the functional configuration diagram of Figure 2. Some of the above programs may be integrated into a single program. Alternatively, any of the above programs may be split into multiple programs. Data group 332 may include information (data, etc.) handled by the above-mentioned functional unit. Alternatively, some or all of the various programs included in the program group 331, the various data groups included in the data group 332, or the various information 333 may be obtained from outside the configuration shown in Figure 3.

[0051] The external storage media drive 304 can connect to an external storage media 305. The external storage media 305 may be, for example, a portable recording disc (DVD, etc.), an IC card, an SD card, a non-volatile memory (e.g., flash memory), or a portable hard disk. Alternatively, information similar to the information in various information 333, such as various programs included in the program group 331, various data included in the data group 332, or information in various information 333, may be transferred and stored from the external storage media 305 to the non-volatile storage media (recording device) 303 or the storage device 302. The external storage media 305 may be used to record programs and data handled by the product information processing system 101. The external storage media drive 304 and the external storage media 305 may be connected to the product information processing system 101 shown in Figure 3 via a network. Various programs included in the program group 331, various data included in the data group 332, or information from various information 333 may be provided via the communication device 308, the external input / output port 309, and the input device 306, and recorded or stored in the non-volatile recording medium (recording device) 303 or the storage device 302.

[0052] In order for the architecture in Figure 3 to function as the product information processing system 101, each functional unit within the product information processing system 101, or a part of each functional unit (execute one or a series of processes (steps)), the various programs included in the program group 331 may be loaded into the storage device 302 (for example, from the non-volatile recording medium (recording device) 303). The loaded program is shown as 321 in Figure 3. The information processing device 301 may then execute program 321 (using, if necessary, various data included in the data group 332 present in the non-volatile recording medium (recording device) 303, etc., or information from various information 333). The execution of program 321 realizes the function of the product information processing system 101, each functional unit within the product information processing system 101, or a part of each functional unit (one or a series of processes (steps) are executed). Various buffers 323 temporarily formed in the storage device 302 may also be used as appropriate at this time.

[0053] 5. Processing performed by embodiments of the present disclosure The following describes the processes performed by embodiments of this disclosure. It is not mandatory to implement all of the functional configurations and perform all of the processes described below. Furthermore, it is not prohibited to implement functional configurations and perform processes other than those described below. In the following, the processes (and the data (information) used in the processing) performed by each functional unit that the product information processing system 101 shown in Figure 2 may have will be explained in order.

[0054] 5.1. Creation of expected efficacy data (Figures 4-13) Figure 4 shows a flowchart 400 of the processes executed by the expected efficacy data creation unit 202, which is a functional unit that the product information processing system 101 may have. Flowchart 400 shows a series of processes that the expected efficacy data creation unit 202 executes when creating scientific expected efficacy data 112 for any of the emotional products. If there are multiple emotional products and the expected efficacy data creation unit 202 creates scientific expected efficacy data 112 for each of the emotional products, the processes shown in the flowchart of Figure 4 will be executed for each of the emotional products.

[0055] 5-1-1. Processing of the Expected Efficacy Data Creation Unit In step 401 of Figure 4, the expected efficacy data creation unit 202 acquires scientific survey results data 117 for the sensory product for which scientific expected efficacy data 112 is to be created. As shown in Figure 1, if the scientific survey results data 117 is recorded in the data pool 102, the expected efficacy data creation unit 202 may request the scientific survey results data 117 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the scientific survey results data 117 to the product information processing system 101 via the communication system 105. As mentioned above, the scientific survey results data 117 may be any of the sensory survey results data 118, electroencephalogram (EEG) survey results data 119, electrocardiogram (ECG) survey results data 120, or component survey results data 121. The scientific survey results data 117 may also be other types of survey results data. The acquisition of scientific survey results data 117 in step 401 may be for one type of survey results data or for multiple types of survey results data.

[0056] In step 402 of Figure 4, the expected efficacy data creation unit 202 acquires the analysis result-efficacy correlation data 122 (first correlation data). As shown in Figure 1, if the analysis result-efficacy correlation data 122 (first correlation data) is recorded in the data pool 102, the expected efficacy data creation unit 202 may request the analysis result-efficacy correlation data 122 (first correlation data) from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the analysis result-efficacy correlation data 122 (first correlation data) to the product information processing system 101 via the communication system 105. As described above, the analysis result-efficacy correlation data 122 (first correlation data) may be data (information) that shows the relationship between the content of the scientific survey results for the sensory product and the expected efficacy for that sensory product. Analysis results - Efficacy correlation data 122 (first correlation data) may be data (information) that shows knowledge or theories with a certain degree of reliability, such as those obtained from past academic research. The analysis results-efficacy correlation data 122 (first correlation data) may be data (information) common to multiple emotional products. Therefore, when the flowchart shown in Figure 4 is executed for a certain emotional product, the analysis results-efficacy correlation data 122 (first correlation data) may be recorded (cached) in the non-volatile recording medium (recording device) 303 or storage device (memory) 302 within the product information processing system 101. When the flowchart shown in Figure 4 is executed for another emotional product, the expected efficacy data creation unit 202 may acquire the analysis results-efficacy correlation data 122 (first correlation data) within the product information processing system 101.

[0057] In step 403 (expected efficacy data creation step) in Figure 4, the expected efficacy data creation unit 202 creates scientific expected efficacy data 112. The expected efficacy data creation unit 202 creates scientific expected efficacy data 112 based on the scientific survey results data 117 obtained in step 401 and the analysis results-efficacy correlation data 122 (first correlation data) obtained in step 402. The expected efficacy data creation unit 202 may, if the scientific survey results data 117 is the same as the emotional survey results data 118, create the emotional-based efficacy data 113, which is the scientific expected efficacy data 112, based on the emotional survey results data 118 and the analysis results-efficacy correlation data 122 (first correlation data). The expected efficacy data creation unit 202 may, if the scientific survey results data 117 is electroencephalogram (EEG) survey results data 119, create EEG-based efficacy data 114, which is the scientific expected efficacy data 112, based on the EEG survey results data 119 and the analysis results-efficacy correlation data 122 (first correlation data). The expected efficacy data creation unit 202 may, if the scientific survey results data 117 is electrocardiogram survey results data 120, create electrocardiogram-based efficacy data 115, which is the scientific expected efficacy data 112, based on the electrocardiogram survey results data 120 and the analysis results-efficacy correlation data 122 (first correlation data). The expected efficacy data creation unit 202 may, if the scientific survey results data 117 is the component survey results data 121, create component-based efficacy data 116, which is the scientific expected efficacy data 112, based on the component survey results data 121 and the analysis results-efficacy correlation data 122 (first correlation data). Alternatively, the expected efficacy data creation unit 202 may create scientific expected efficacy data 112 based on multiple types of research result data, which constitute the scientific research result data 117. Alternatively, the expected efficacy data creation unit 202 may input the analysis result-efficacy correlation data 122 (first correlation data) and the scientific survey results data 117 for the emotional product into a learning model trained by machine learning, and then use the output data from the learning model to create scientific expected efficacy data 112 for the emotional product. (The output data from the learning model itself may also be used as the scientific expected efficacy data 112 for the emotional product.) The expected efficacy data creation unit 202 may transmit the created scientific expected efficacy data 112 to the data pool 102 via the communication system 105. The data pool 102, upon receiving the scientific expected efficacy data 112, records the scientific expected efficacy data 112.

[0058] 5-1-2. Examples of survey results data, first correlation data, and expected efficacy data. As shown in Figure 1, both the scientific survey results data 117 and the scientific expected efficacy data 112 can be of four types (related to the sensibilities of those who used the sensory product, related to the brainwaves of those who used the sensory product, related to the electrocardiograms of those who used the sensory product, and related to the ingredients of the sensory product) (there may be other types of scientific survey results data 117 and scientific expected efficacy data 112 besides those shown in Figure 1, and none of the above four types may be present). Below, examples of several types of scientific survey results data 117 and scientific expected efficacy data 112 are shown, as well as an example of how scientific expected efficacy data 112 is created based on the scientific survey results data 117 and the analysis results-efficacy correlation data 122 (first correlation data) in step 403 of Figure 4.

[0059] 5-1-2-1. Matters relating to the sensibilities of those who use the products. The combination of Figures 5 and 6 shows an example of sensory survey data 118. The combination of Figures 5 and 6 shows an example of sensory (psychological) survey data 118 for a white musk product, which is an example of a sensory product (fragrance product). The sensory (psychological) survey data 118 shown in the combination of Figures 5 and 6 is based on the VAS method (Visual Analogue Scale method). Here, Figure 5 shows the sensory (psychological) survey results 500 obtained by statistically processing the results of a sensory questionnaire answered by those who smelled the comparison product (CONTROL) on the group of people who smelled the comparison product (CONTROL). On the other hand, Figure 6 shows the sensory (psychological) survey results 600 obtained by statistically processing the results of a sensory questionnaire answered by those who smelled the white musk product on the group of people who smelled the white musk product.

[0060] Figures 5 and 6 provide an example of the 118 data points from the sensory survey results for white musk products, showing that "significant differences were observed between the control group (CONTROL) and the white musk products in the three categories of 'gorgeous,' 'like,' and 'sweet.'" (Note that the pie charts shown in Figures 5 and 6 are also examples of the 118 data points from the sensory survey results for white musk products.) Here, if the analysis results - efficacy correlation data 122 (first correlation data) contains data (information) stating that "when the brain is activated, positive emotions such as 'gorgeous' and 'liking' arise," then the expected efficacy data creation unit 202 may generate data (information) stating "White musk products activate psychology and the brain" as emotion-based efficacy data 113 for white musk products, based on the above-mentioned sensory survey results data 118 for white musk products and the analysis results - efficacy correlation data 122 (first correlation data).

[0061] The combination of Figures 7 and 8 shows another example of the sensory survey results data 118. The combination of Figures 7 and 8 shows another example of the sensory (psychological) survey results data 118 for a white musk product, which is an example of a sensory product (fragrance product). The sensory (psychological) survey results data 118 shown in the combination of Figures 7 and 8 is based on a mood profile test called POMS (Profile of Mood States). Here, Figure 7 shows the sensory (psychological) survey results 700 obtained by statistically processing the results of a sensory questionnaire answered by those who smelled a control product (CONTROL) on the group of people who smelled the control product (CONTROL). On the other hand, Figure 8 shows the sensory (psychological) survey results 800 obtained by statistically processing the results of a sensory questionnaire answered by those who smelled the white musk product on the group of people who smelled the white musk product.

[0062] Figures 7 and 8 provide another example of the 118 sensory survey results regarding white musk products, showing that "most participants highly valued the effect of white musk products on 'fatigue-lethargy'." (Note that the pie charts shown in Figures 7 and 8 are also another example of the 118 sensory survey results regarding white musk products.) Here, if the analysis results-efficacy correlation data 122 (first correlation data) contains data (information) stating that "the effect on 'fatigue-lethargy' is to reduce negative emotions and stress," then the expected efficacy data creation unit 202 may generate the following data (information) as emotion-based efficacy data 113 for the white musk product, based on the above-mentioned sensory survey results data 118 for the white musk product and the analysis results-efficacy correlation data 122 (first correlation data): "White musk products reduce negative emotions and stress."

[0063] 5-1-2-2. Regarding the brainwaves of persons who use sensory products. Figure 9 shows an example of electroencephalogram (EEG) survey data 119. Figure 9 shows an example of EEG analysis data (EEG frequency distribution data, EEG survey data) 900 for a white musk product, which is an example of a fragrance product. The EEG analysis data (EEG frequency distribution data, EEG survey data) 900 shown in Figure 9 is information on the intensity of brain waves for each frequency band of brain waves (for example, frequency bands expressed as alpha waves (α waves), beta waves (β waves), gamma waves (γ waves), theta waves (θ waves), etc.). The "Pre" section on the left of Figure 9 shows the electroencephalogram (EEG) analysis data (EEG frequency distribution data, EEG survey results data) before smelling the comparison product (CONTROL) or the white musk product. The "Task" section in the center of Figure 9 shows the EEG analysis data (EEG frequency distribution data, EEG survey results data) while smelling the comparison product (CONTROL) or the white musk product. The "Post" section on the right of Figure 9 shows the EEG analysis data (EEG frequency distribution data, EEG survey results data) after smelling the comparison product (CONTROL) or the white musk product. In Figure 9, each of the "Pre," "Task," and "Post" groups has three columns of electroencephalogram (EEG) analysis data (EEG frequency distribution data and EEG survey results data). Of these three columns, the leftmost column shows information about the EEG intensity relative to the control group (CONTROL), the middle column shows information about the EEG intensity relative to the white musk product, and the rightmost column shows information about the p-value based on the EEG intensity relative to the control group (CONTROL) and the EEG intensity relative to the white musk product. Furthermore, the electroencephalogram (EEG) analysis data (EEG frequency distribution data, EEG survey results data) in Figure 9 has four rows. From top to bottom, the rows show information about theta waves (θ waves), alpha waves (α waves), beta waves (β waves), and gamma waves (γ waves).

[0064] Figure 9 shows an example of electroencephalogram (EEG) survey data 119 for white musk products, which indicates that "beta waves (β waves) were significantly suppressed in the left hemisphere only when smelling the white musk product (during the Task)." (Note that the individual EEG analysis data (EEG frequency distribution data, EEG survey data) 900 shown in Figure 9 are also examples of EEG survey data 119 for white musk products.) Here, if the analysis results-efficacy correlation data 122 (first correlation data) contains the data (information) that "the left brain controls pleasant emotions. Beta waves (β waves) in the left brain indicate tension in the left brain (decreased pleasant emotions)," then the expected efficacy data creation unit 202 may generate the following data (information) as brainwave-based efficacy data 114 for the white musk product, based on the brainwave survey results data 119 for the white musk product and the analysis results-efficacy correlation data 122 (first correlation data): "White musk products have the potential to relax the left brain and enhance pleasant emotions."

[0065] Figure 10 shows another example of electroencephalogram (EEG) survey data 119. Figure 10 shows an example of EEG frequency spectrum information (1 / f fluctuation information, EEG survey data) 1000 for a white musk product, an example of a fragrance product. The EEG frequency spectrum information (1 / f fluctuation information, EEG survey data) 1000 shown in Figure 10 shows a graph of the EEG intensity of those who smelled the control product (CONTROL) and those who smelled the white musk product, with the horizontal axis representing EEG frequency and the vertical axis representing the logarithm of EEG intensity. Note that in the graph in Figure 10, the regression line is shown as a dotted line. (The regression line for the control product (CONTROL) is labeled "1 / f control," and the regression line for the white musk product is labeled "1 / f Musk.") Figure 10 contains three graphs. The graph on the left shows the brainwave intensity before smelling the control product (CONTROL) or the white musk product. The graph in the center shows the brainwave intensity while smelling the control product (CONTROL) or the white musk product. The graph on the right shows the brainwave intensity after smelling the control product (CONTROL) or the white musk product.

[0066] Figure 10 shows another example of EEG survey data 119 for white musk products: "The slope of the 1 / f fluctuation in the brainwaves (slope of the regression line) decreased only when smelling the white musk product (during the Task)." (Note that the individual frequency spectrum information (1 / f fluctuation information, EEG survey data) 1000 shown in Figure 10 are also another example of EEG survey data 119 for white musk products.) Here, if the analysis results - efficacy correlation data 122 (first correlation data) contains data (information) stating that "when a person smells a scent they like, dopamine is secreted, and the slope of the 1 / f fluctuation of the brainwave (slope of the regression line) tends to become gentler," then the expected efficacy data creation unit 202 may generate brainwave-based efficacy data 114 for the white musk product, based on the brainwave survey results data 119 for the white musk product and the analysis results - efficacy correlation data 122 (first correlation data), stating that "most people (the statistical majority) like the scent of the white musk product."

[0067] 5-1-2-3. Regarding electrocardiograms of persons who have used sensory products. The combination of Figures 11 and 12 shows an example of electrocardiogram (ECG) survey data 120. The combination of Figures 11 and 12 shows an example of ECG survey data 120 for a white musk product, which is an example of a sensory product (fragrance product). Figures 11 and 12 are obtained by applying statistical processing to the group of people who smell either the comparison product (odorless) or the white musk product, using indices obtained from ECGs acquired from people who smell either the comparison product (odorless) or the white musk product. The index 1100 shown in Figure 11 is the sympathetic nervous system activity index (LF). The index 1200 shown in Figure 12 is the autonomic nervous system stress index (LF / HF). In both Figure 11 and Figure 12, the two box plots above the "Pre-treatment rest" label on the left represent indicators before smelling the comparison sample (odorless) or the white musk product. The two box plots above the "Odor inhalation" label in the center represent indicators during the smelling of the comparison sample (odorless) or the white musk product. The two box plots above the "Post-treatment rest" label on the right represent indicators after smelling the comparison sample (odorless) or the white musk product. In both combinations of box plots, the box plot on the left represents the comparison target (odorless), and the box plot on the right represents the white musk product.

[0068] From the combination of Figures 11 and 12, one example of electrocardiogram (ECG) survey data 120 for white musk products is obtained: "The fragrance and lingering scent of white musk products suppressed the increase in sympathetic nervous system activity (stress)." (Note that the sympathetic nervous system activity index (LF) 1100 shown in Figure 11 and the autonomic nervous system stress index (LF / HF) 1200 shown in Figure 12 are also examples of ECG survey data 120 for white musk products.) Here, if the analysis results-efficacy correlation data 122 (first correlation data) contains data (information) stating that "sympathetic nervous system stress may cause negative emotions and adverse effects on the digestive system and immune function," then the expected efficacy data creation unit 202 may generate the following data (information) as electrocardiogram-based efficacy data 115 for the white musk product, based on the electrocardiogram survey results data 120 for the white musk product and the analysis results-efficacy correlation data 122 (first correlation data): "White musk products reduce negative emotions and stress."

[0069] 5-1-2-4. Regarding the ingredients of sensory products. Figure 13 shows an example of component analysis data 121. Figure 13 shows an example of component analysis data 121 for a white musk product, which is an example of a sensory product (fragrance product). Figure 13 shows the results of analyzing the concentration of collected fragrance components for the white musk product using gas chromatography-mass spectrometry (GC-MS) with a heated desorption unit (TDU) (TDU-GCMS analysis results 1300).

[0070] Figure 13 shows an example of ingredient analysis data 121 for white musk products, which states that "white musk products contain galaxolide and tonalid as characteristic ingredients (synthetic musk ingredients)." (Note that the TDU-GCMS analysis results 1300 shown in Figure 13 are also an example of ingredient analysis data 121 for white musk products.) The component analysis data 121 described above may, for example, be included in the development support data 132 by the development support unit 212, as described later.

[0071] 5-2. Creating a product description (Figures 14-15) The following section primarily describes the functions and processes performed by the product description creation unit 203.

[0072] 5-2-1. Processing of the product description creation section (Figure 14) Figure 14 shows a flowchart 1400 of the processes performed by the product description creation unit 203, which is a functional unit that the product information processing system 101 may have. The flowchart 1400 shows a series of processes that the product description creation unit 203 performs when creating product description data 108 (specifically, scientific explanation data 110 or integrated explanation data 111) for each of the sensory products for which product description data 108 is to be created.

[0073] In step 1401 of Figure 14, the product description creation unit 203 selects one of the sensory products for which product description data 108 (specifically, scientific explanation data 110 or integrated explanation data 111) has been created, and for which product description data 108 (specifically, scientific explanation data 110 or integrated explanation data 111) has not yet been created. In step 1402 of Figure 14, the product description creation unit 203 acquires product identification data 107 for the emotional product selected in step 1401. As shown in Figure 1, if the product identification data 107 is recorded in the data pool 102, the product description creation unit 203 may request the product identification data 107 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the product identification data 107 it has recorded to the product information processing system 101 via the communication system 105. As already shown, the product identification data 107 is data (information) that identifies the emotional product. The product identification data 107 may be, for example, the name of the emotional product or an identifier (identification number) attached to the emotional product. In step 1403 of Figure 14, the product description creation unit 203 acquires general description data 109 for the sensory product selected in step 1401. As shown in Figure 1, if the general description data 109 is recorded in the data pool 102, the product description creation unit 203 may request the general description data 109 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the general description data 109 it has recorded to the product information processing system 101 via the communication system 105. As already shown, the general description data 109 is information that describes the sensory product and is information that has traditionally been used to explain sensory products to consumers, etc. For example, if the sensory product is a fragrance product, the general description data 109 may describe the fragrances and tones (fragrance notes; for example, those indicating changes in fragrance over time) used in the fragrance product. In step 1404 of Figure 14, the product description creation unit 203 acquires scientific expected efficacy data 112 and scientific survey results data 117 for the sensory product selected in step 1401. As shown in Figure 1, if the scientific expected efficacy data 112 and scientific survey results data 117 are recorded in the data pool 102, the product description creation unit 203 may request the scientific expected efficacy data 112 and scientific survey results data 117 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the scientific expected efficacy data 112 and scientific survey results data 117 that it has recorded to the product information processing system 101 via the communication system 105. Note that in step 1401, both the scientific expected efficacy data 112 and scientific survey results data 117 may be acquired, or either the scientific expected efficacy data 112 or the scientific survey results data 117 may be acquired.

[0074] In step 1405 (product description creation step) of Figure 14, the product description creation unit 203 creates scientific descriptive data 110 for the sensory product selected in step 1401. The product description creation unit 203 creates the scientific descriptive data 110 based on one or both of the scientific expected efficacy data 112 and scientific survey results data 117 obtained in step 1404. The scientific descriptive data 110 created by the product description creation unit 203 is designed to be easily understood by consumers who have little expertise in sensory products when displayed or output to them. (In contrast, the scientific survey results data 117 and scientific expected efficacy data 112 may be difficult to read or too technical for consumers with little expertise in sensory products if they are presented in their original form and content.) The scientific descriptive data 110 created by the product description creation unit 203 may be expressed in, for example, charts or natural language. Furthermore, in the process of creating scientific explanation data 110 performed by the product description creation unit 203 in step 1405, the product description creation unit 203 may input one or both of the scientific expected efficacy data 112 and scientific survey results data 117 obtained in step 1404 into a machine learning model (e.g., a generative AI), and then use the output from the machine learning model to create the scientific explanation data 110. (Alternatively, the output from the machine learning model may be treated as the scientific explanation data 110.)

[0075] In step 1406 of Figure 14, the product description creation unit 203 outputs (or displays) or saves (records) the scientific explanation data 110 created in step 1405. If the scientific explanation data 110 is to be saved (recorded), the product description creation unit 203 may transmit the scientific explanation data 110 to the data pool 102 via the communication system 105 and request the data pool 102 to record the scientific explanation data 110. Upon receiving the request, the data pool 102 records the scientific explanation data 110. If the scientific explanation data 110 is to be displayed or output, the product description creation unit 203 may control the scientific explanation data 110 to be displayed or output to the display or output device 307 of the product information processing system 101. Alternatively, the product description creation unit 203 may control the scientific explanation data 110 to be displayed or output to the product information output device 104. Furthermore, in step 1406, both the display or output of the scientific explanation data 110 and the storage or recording of the scientific explanation data 110 may be performed.

[0076] In step 1407 of Figure 14, the product description creation unit 203 may create integrated product description data 111 by integrating the general product description data 109 obtained in step 1403 and the scientific product description data 110 created in step 1405. The product description creation unit 203 may create integrated product description data 111 by, for example, linking the general product description data 109 and the scientific product description data 110. Alternatively, the product description creation unit 203 may create integrated product description data 111 by, for example, using a predetermined layout template when integrating the general product description data 109 and the scientific product description data 110. Alternatively, the product description creation unit 203 may create integrated product description data 111 by, for example, dynamically adjusting the layout structure when integrating the general product description data 109 and the scientific product description data 110. Furthermore, in the process of creating integrated descriptive data 111 executed by the product description creation unit 203 in step 1407, the product description creation unit 203 may input both the general descriptive data 109 obtained in step 1403 and the scientific descriptive data 110 created in step 1405 into a machine learning model (e.g., a generative AI) that is trained using machine learning, and then use the output from the machine learning model to create the integrated descriptive data 111. (Alternatively, the output from the machine learning model may be treated as the integrated descriptive data 111.) Alternatively, the processes in steps 1405 and 1407 of Figure 14 may be performed together. That is, after step 1404, the product description creation unit 203 may create integrated explanatory data 111 (without creating scientific explanatory data 110) based on the general explanatory data 109 obtained in step 1403 and one or both of the scientific expected efficacy data 112 and scientific survey results data 117 obtained in step 1404. In this case as well, a machine learning-trained learning model (e.g., generative AI) may be used to create the integrated explanatory data 111.

[0077] In step 1408 of Figure 14, the product description creation unit 203 outputs (or displays) or saves (records) the integrated description data 111 (integrated product description) created in step 1407. If the integrated description data 111 is to be saved (recorded), the product description creation unit 203 may transmit the integrated description data 111 to the data pool 102 via the communication system 105 and request the data pool 102 to record the integrated description data 111. Upon receiving the request, the data pool 102 records the integrated description data 111. If the integrated description data 111 is to be displayed or output, the product description creation unit 203 may control the integrated description data 111 to be displayed or output to the display or output device 307 of the product information processing system 101. Alternatively, the product description creation unit 203 may control the integrated description data 111 to be displayed or output to the product information output device 104. Furthermore, in step 1406, both the display or output of the integrated explanatory data 111 and the saving or recording of the integrated explanatory data 111 may be performed.

[0078] In step 1409 of Figure 14, the product description creation unit 203 determines whether there are any remaining emotional products for which product description data 108 (specifically, scientific explanation data 110 or integrated explanation data 111) has not yet been created. If the determination result in step 1409 is positive, control returns to step 1401. If the determination result in step 1409 is negative, the processing of the product description creation unit 203 ends (because product description data 108 (specifically, scientific explanation data 110 or integrated explanation data 111) has been created for all emotional products for which product description data 108 (specifically, scientific explanation data 110 or integrated explanation data 111) has been created).

[0079] 5-2-2. Examples of scientific explanatory data, etc. (Figure 15) Figure 15 shows an example of how product description data 108 (of which scientific explanation data 110 or integrated explanation data 111) is created based on the product description creation unit 203 (and the expected efficacy data creation unit 202). Figure 15 shows an example of how product description data 108 (of which scientific explanation data 110 or integrated explanation data 111) is created for a white musk product, which is an example of a sensory product (fragrance product).

[0080] First, the expected efficacy data creation unit 202 creates scientific expected efficacy data 112 for the white musk product shown in the upper right of Figure 15, using the scientific survey results data 117 (and analysis results-efficacy correlation data 122 (first correlation data)) for the white musk product shown in the upper left of Figure 15, based on the process shown in the flowchart of Figure 4, for example. In the example of Figure 15, based on either or both of the electrocardiogram survey results data 120, which is an example of electrocardiogram survey results data 120, and the psychological survey (POMS) results, which is an example of emotional survey results data 118, data (information) such as "(White musk products) reduce negative emotions and stress" is created, which may be an example of electrocardiogram-based efficacy data 115 or emotional-based efficacy data 113. Furthermore, in the example in Figure 15, data (information) such as "(White musk products) activate the mind and brain," which could be an example of emotion-based efficacy data 113 or electroencephalogram-based efficacy data 114, is created based on either or both of the "Psychological Survey (VAS)," which is an example of emotion survey data 118, and the "Overall Brain Function Survey Results," which is an example of electroencephalogram survey data 119. In the example in Figure 15, data (information) such as "(White musk products) may be trying to relax the left brain and enhance feelings of pleasure," which is an example of electroencephalogram-based efficacy data 114, is created based on the "Electroencephalogram (Frequency Distribution) Survey Results," which is an example of electroencephalogram survey data 119. In addition, the explanations for Figures 5 to 12 described examples in which the expected efficacy data creation unit 202 creates sensory-based efficacy data 113 using sensory survey data 118, creates electroencephalogram-based efficacy data 114 using electroencephalogram survey data 119, and creates electrocardiogram-based efficacy data 115 using electrocardiogram survey data 120. On the other hand, as shown in the example in Figure 15, the expected efficacy data creation unit 202 may create a single scientific expected efficacy data 112 using multiple types of scientific survey data 117 (for example, sensory, electroencephalogram, electrocardiogram, and component types).

[0081] Next, the product description creation unit 203 creates scientific explanatory data 110 for the white musk product, using the scientific expected efficacy data 112 for the white musk product shown in the upper right of Figure 15, based on the process shown in the flowchart of Figure 14, for example. In the example of Figure 15, the product description creation unit 203 uses the data (information) that "(white musk products) reduce negative emotions and stress," the data (information) that "(white musk products) activate the mind and brain," and the data (information) that "(white musk products) may be trying to relax the left brain and enhance pleasant emotions," to create an example of scientific explanatory data 110 for the white musk product, which is "(white musk products) are expected to have the effect of making you feel positive, activating the entire brain, and at the same time reducing negative emotions and stress, and changing brain function to bring about good emotions."

[0082] As shown in Figure 15, the product description creation unit 203 may create a single scientific explanation data 110 using multiple types of scientific expected efficacy data 112 (for example, types such as emotional, electroencephalogram, electrocardiogram, and component). Compared to scientific explanation data 110 that relies on only one type of scientific expected efficacy data 112 (for example, types such as emotional, electroencephalogram, electrocardiogram, and component), scientific explanation data 110 that relies on multiple types of scientific expected efficacy data 112 is expected to capture the emotional product from multiple perspectives and have more appropriate content. In particular, as shown in Figure 15, scientific explanation data 110 that relies on three or more types of scientific expected efficacy data 112 (in the example in Figure 15, emotional, electroencephalogram, and electrocardiogram) is expected to be highly reliable and accurate as product description data 108 provided to consumers. In other words, it is expected that the effect of assisting consumers when selecting and purchasing emotional products will be enhanced.

[0083] In the example in Figure 15, only text expressed in natural language is shown as scientific explanatory data 110, but scientific explanatory data 110 may be accompanied by figures and tables. In the example in Figure 15, for example, figures and tables included in scientific survey results data 117 may be incorporated into scientific explanatory data 110. Also, in the example in Figure 15, scientific explanatory data 110 is shown as relying on scientific expected efficacy data 112, but scientific explanatory data 110 may rely on either or both of scientific survey results data 117 and scientific expected efficacy data 112.

[0084] The product description creation unit 203 may, for example, create integrated product description data 111 by integrating general descriptive data 109 and scientific descriptive data 110 about the white musk product, as shown in the example in Figure 15, based on the process shown in the flowchart in Figure 14. In the example in Figure 15, the general descriptive data 109 about the white musk product is pre-recorded (for example in the data pool 102) as "(The white musk product) has a clean, elegant, soap-like scent and a deep musk scent that brings strength and comfort, enhancing the wearer's charm, instilling confidence, and empowering them." In the example in Figure 15, the product description creation unit 203 may create integrated product description data 111 by integrating general descriptive data 109 and scientific descriptive data 110 about the white musk product, using the method described with respect to step 1407 in Figure 14.

[0085] 5-3. Product Classification (Figures 16-17) Figure 16 shows a flowchart 1600 of the processes performed by the product classification unit 204, which is a functional unit that the product information processing system 101 may have. The flowchart 1600 shows a series of processes in which the product classification unit 204 determines the classification (category) to which each of the emotional products to be classified belongs, and creates product classification data 123 that reflects the determination result.

[0086] In step 1601 of Figure 16, the product classification unit 204 selects one of the sensory products that are subject to classification and have not yet been classified. In step 1602 of Figure 16, the product classification unit 204 acquires product identification data 107 for the emotional product selected in step 1601. As shown in Figure 1, if the product identification data 107 is recorded in the data pool 102, the product classification unit 204 may request the product identification data 107 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the product identification data 107 it has recorded to the product information processing system 101 via the communication system 105. As already shown, the product identification data 107 is data (information) that identifies the emotional product. The product identification data 107 may be, for example, the name of the emotional product or an identifier (identification number) attached to the emotional product. In step 1603 of Figure 16, the product classification unit 204 acquires general description data 109 for the sensory product selected in step 1601. As shown in Figure 1, if the general description data 109 is recorded in the data pool 102, the product classification unit 204 may request the general description data 109 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the general description data 109 it has recorded to the product information processing system 101 via the communication system 105. As already shown, the general description data 109 is information that describes the sensory product and is information that has been traditionally used to explain sensory products to consumers, etc. For example, if the sensory product is a fragrance product, the general description data 109 may describe the fragrances and tones (fragrance notes; for example, those indicating changes in fragrance over time) used in the fragrance product. In step 1604 of Figure 16, the product classification unit 204 acquires scientific expected efficacy data 112 and scientific survey results data 117 for the sensory products selected in step 1601. As shown in Figure 1, if the scientific expected efficacy data 112 and scientific survey results data 117 are recorded in the data pool 102, the product classification unit 204 may request the scientific expected efficacy data 112 and scientific survey results data 117 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the scientific expected efficacy data 112 and scientific survey results data 117 that it has recorded to the product information processing system 101 via the communication system 105. In step 1604, both the scientific expected efficacy data 112 and scientific survey results data 117 may be acquired, or either the scientific expected efficacy data 112 or the scientific survey results data 117 may be acquired.

[0087] In step 1605 (product classification step) of Figure 16, the product classification unit 204 determines the classification (category) to which the emotional product selected in step 1601 belongs. The product classification unit 204 may determine the classification (category) to which the emotional product belongs using, for example, one or more of the general descriptive data 109 that may be obtained in step 1603, the scientific expected efficacy data 112 that may be obtained in step 1604, or the scientific survey results data 117 that may be obtained in step 1604.

[0088] Here, the classification (category) to which a single emotional product belongs does not need to be limited to a single classification (category) based on a single criterion. Rather, even if multiple criterion standards exist (and classifications (categories) are provided in a multidimensional manner), the classification (category) to which a single emotional product belongs can be each of the classifications (categories) based on each of the criterion standards. In other words, there can be multiple classifications (categories) to which a single emotional product belongs. Furthermore, the classification (category) to which a single emotional product belongs can be multiple classifications (categories) based on a single criterion.

[0089] Figure 17 shows an example of product classification data 123. Figure 17 also illustrates the criteria used to determine the classification (category) to which emotional products belong, as well as examples of classifications (categories) to which emotional products belong.

[0090] In the example shown in Figure 17, the product classification data 123 consists of six tables. Specifically, the product classification data 123 is composed of an ingredient-based table 1701, a sensory-based table 1702, an electroencephalogram-based table 1703, a tone-based table 1704, a fragrance-based table 1705, and an electrocardiogram-based table 1706.

[0091] The ingredient-based table 1701 records the results of classifying (categorizing; in this case, ingredient-based categories) sensory products based on judgment criteria for one or both of the ingredient-based efficacy data 116 and the ingredient survey results data 121. In the example in Figure 17, ingredient-based table 1701 shows that the sensory product with product identification data 107 A, the sensory product with product identification data 107 D, and the sensory product with product identification data 107 H belong to the ingredient-based category where the characteristic ingredient contained in the sensory product is "Galaxolide (synthetic musk component)". Also in the example in Figure 17, ingredient-based table 1701 shows that the sensory product with product identification data 107 A, the sensory product with product identification data 107 B, and the sensory product with product identification data 107 E belong to the ingredient-based category where the characteristic ingredient contained in the sensory product is "Tonalid (synthetic musk component)". As described above, it is permissible for a sensory product whose product identification data 107 is A to belong to multiple ingredient-based categories (classifications) in the criteria for judging ingredients.

[0092] The Sensibility-Based Table 1702 records the results of classifying (categorizing; in this case, Sensibility-Based Categories) Sensibility Products based on judgment criteria for one or both of the Sensibility-Based Efficacy Data 113 and Sensibility Survey Results Data 118. In the example in Figure 17, Sensibility-Based Table 1702 shows that the Sensibility-Based Category to which the expected benefit of a Sensibility Product from a Sensibility Perspective is "Reduction of Negative Emotions" includes Sensibility Products with Product Identification Data 107 A, Sensibility Products with Product Identification Data 107 B, and Sensibility Products with Product Identification Data 107 G. Also in the example in Figure 17, Sensibility-Based Table 1702 shows that the Sensibility-Based Category to which the expected benefit of a Sensibility Product from a Sensibility Perspective is "Reduction of Psychological Stress" includes Sensibility Products with Product Identification Data 107 A, Sensibility Products with Product Identification Data 107 B, and Sensibility Products with Product Identification Data 107 C. As described above, a product whose product identification data 107 is A may belong to multiple emotion-based categories (classifications) in the criteria for judging emotion.

[0093] The EEG-based table 1703 records the results of classifying (categorizing; in this case, EEG-based categories) emotional products based on judgment criteria for one or both of the EEG-based efficacy data 114 and the EEG survey results data 119. In the example in Figure 17, the EEG-based table 1703 shows that the emotional product with product identification data 107 A, the emotional product with product identification data 107 D, and the emotional product with product identification data 107 F belong to the EEG-based category where the expected benefit to which the emotional product contributes from an EEG perspective is "left brain relaxation, pleasant emotions." Also in the example in Figure 17, the EEG-based table 1703 shows that the emotional product with product identification data 107 H, the emotional product with product identification data 107 F, and the emotional product with product identification data 107 I belong to the EEG-based category where the expected benefit to which the emotional product contributes from an EEG perspective is "improved concentration." As described above, it is permissible for a sensory product whose product identification data 107 is F to belong to multiple brainwave-based categories (classifications) in the brainwave-related judgment criteria.

[0094] The tone-based table 1704 records the results of classifying (categorizing; in this case, tone-based categories) emotional products based on the judgment criteria for the descriptive information regarding the tone of the emotional products included in the general descriptive data 109. In the example in Figure 17, the tone-based table 1704 shows that the emotional products with product identification data 107 A, product identification data 107 D, and product identification data 107 G belong to the tone-based category where the tone of the emotional products is "Oriental". Also in the example in Figure 17, the tone-based table 1704 shows that the emotional products with product identification data 107 B, product identification data 107 E, and product identification data 107 F belong to the tone-based category where the tone of the emotional products is "Floral". Note that an emotional product with a specific value for product identification data 107 may belong to multiple tone-based categories (classifications) based on the judgment criteria regarding tone.

[0095] Fragrance base table 1705 records the results of classifying (categorizing; in this case, fragrance base categories) sensory products based on the judgment criteria for the descriptive information regarding fragrances in sensory products included in the general description data 109. In the example in Figure 17, fragrance base table 1705 shows that the sensory product with product identification data 107 C, the sensory product with product identification data 107 D, and the sensory product with product identification data 107 G belong to the fragrance base category where the fragrance of the sensory product is "lemon". Also in the example in Figure 17, fragrance base table 1705 shows that the sensory product with product identification data 107 A, the sensory product with product identification data 107 E, and the sensory product with product identification data 107 F belong to the fragrance base category where the fragrance of the sensory product is "musk". Furthermore, it is permissible for a sensory product whose product identification data 107 is a specific value to belong to multiple fragrance base categories (classifications) in the criteria for judging fragrances.

[0096] The electrocardiogram-based table 1706 records the results of classifying (categorizing; in this case, electrocardiogram-based categories) emotional products based on judgment criteria for one or both of the electrocardiogram-based efficacy data 115 and the electrocardiogram survey results data 120. In the example in Figure 17, the electrocardiogram-based table 1706 shows that the emotional product with product identification data 107 A, the emotional product with product identification data 107 D, and the emotional product with product identification data 107 F belong to the electrocardiogram-based category where the expected efficacy to be contributed by the emotional product from an electrocardiogram perspective is "reduction of negative emotions." Also in the example in Figure 17, the electrocardiogram-based table 1706 shows that the emotional product with product identification data 107 A, the emotional product with product identification data 107 D, and the emotional product with product identification data 107 F belong to the electrocardiogram-based category where the expected efficacy to be contributed by the emotional product from an electrocardiogram perspective is "reduction of psychological stress." As described above, it is permissible for a sensory product whose product identification data 107 is A to belong to multiple brainwave-based categories (classifications) in the brainwave-related judgment criteria.

[0097] Returning to the explanation of Figure 16, in step 1606 (product classification step) of Figure 16, the product classification unit 204 updates the information contained in the product classification data 123 to reflect the classification (category) to which the emotional products determined in step 1605 belong. (If the tables, etc., contained in the product classification data 123 do not yet exist, the product classification unit 204 may create the product classification data 123.) For example, suppose the product identification data 107 (e.g., identifier) ​​for the emotional product selected in step 1601 is A, and the classification (category) to which the emotional product belongs, as determined in step 1605, is "Galaxolide" and "Tonalid" as ingredient-based categories, "Negative Emotion Reduction" and "Psychological Stress Reduction" as emotion-based categories, "Left Brain Relaxation, Pleasant Emotions" as EEG-based categories, "Oriental" as tone-based category, "Musk" as fragrance-based category, and "Negative Emotion Reduction" and "Psychological Stress Reduction" as electrocardiogram-based categories. In this case, in step 1606, the product classification unit 204 may add A as product identification data 107 to each of the ingredient-based table 1701, emotion-based table 1702, EEG-based table 1703, tone-based table 1704, fragrance-based table 1705, and electrocardiogram-based table 1706, which constitute the product classification data 123, so as to reflect the determination result in step 1605.

[0098] In step 1607 of Figure 16, the product classification unit 204 determines whether there are any remaining sensory products that have not yet been classified. If the result of the determination in step 1607 is positive, control returns to step 1601. If the result of the determination in step 1607 is negative, (because all of the sensory products to be classified have already been classified) the processing performed by the product classification unit 204 is terminated.

[0099] 5-4. Guide creation (Figure 18) Figure 18 shows a flowchart 1800 of the processes performed by the guide creation unit 205, which is a functional unit that the product information processing system 101 may have. The flowchart 1800 shows a series of processes by which the guide creation unit 205 creates product selection guide data 124 to assist consumers in selecting emotional products. As has already been pointed out, the product selection guide data 124 may be a combination of category selection questionnaire data 125, product list data 126, and link information 127, as shown in Figure 1.

[0100] In step 1801 of Figure 18, the guide creation unit 205 acquires product classification data 123. As shown in Figure 1, if the product classification data 123 is recorded in the data pool 102, the guide creation unit 205 may request the product classification data 123 from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the product classification data 123 it has recorded to the product information processing system 101 via the communication system 105. As already shown, the product classification data 123 is data (information) that shows the result of classifying emotional products so that they form a set of emotional products with the same or similar expected effects, etc. The product classification data 123 may be data (information) that shows the result of classifying each emotional product into a category from multiple perspectives (for example, from the perspectives of ingredients, emotionality, electroencephalogram, tone, fragrance, and electrocardiogram (Figure)), for example, as shown in Figure 17 above.

[0101] In step 1802 (guide creation step) of Figure 18, the guide creation unit 205 may create category selection questionnaire data 125 from the data (information) included in the product selection guide data 124. As already shown, the category selection questionnaire data 125 is such that when a consumer provides answers (or selection results by choosing answer options) to the questionnaire indicated by the data, the classification (category) of the emotional product that suits the consumer's needs can be determined. The guide creation unit 205 may, for example, use the product classification data 123 obtained in step 1801 to create questions (and answer options for those questions) to be included in the category selection questionnaire data 125 such that each consumer response result or selection result (combination) corresponds to each classification (category) included in the product classification data 123 (to which emotional products may belong). For example, if the product classification data 123 is as shown in Figure 17, the guide creation unit 205 may create a first question about negative emotions and answer options for the first question (A, B, C, etc.) and a second question about negative emotions and answer options for the second question (α, β, γ, etc.) for one of the emotion-based categories, "Reduction of Negative Emotions," and then associate the selection result of the answer options for the first question being a predetermined one (e.g., option B) and the answer option for the second question being another predetermined one (e.g., option γ) with one of the emotion-based categories, "Reduction of Negative Emotions."

[0102] In step 1803 (guide creation step) of Figure 18, the guide creation unit 205 may create product list data 126 from the data (information) included in the product selection guide data 124. As already shown, the product list data 126 is data (information) that shows a list of emotional products included in each classification (category) of emotional products, which is determined according to the consumer's answers or selections to the questionnaire shown in the category selection questionnaire data 125. For example, in step 1802, if each of the consumer's response results or selection results (combinations) for the group of questions included in the category selection questionnaire data 125 is associated with a classification (category) to which an emotional product may belong, then in step 1803, the guide creation unit 205 may use the product classification data 123 to identify a list of emotional products included in the associated classification (category) for each of the consumer's response results or selection results (combinations). For example, as mentioned above, if the selection result of the answer to the first question is a predetermined one (e.g., option B), and the answer to the second question is another predetermined one (e.g., option γ), and this corresponds to "Negative Emotion Reduction," one of the emotion-based categories shown in Figure 17, then the guide creation unit 205 may use the product classification data 123 shown in Figure 17 to identify the list of emotion products corresponding to the selection result of the answer to the first question being a predetermined one (e.g., option B), and the answer to the second question being another predetermined one (e.g., option γ), as a group of emotion products whose product identification data 107 is A, B, G, etc. The guide creation unit 205 may, after identifying a list of emotionally appealing products for each of the consumer's response results or selection results (combinations), create product list data 126 based on the identified list of emotionally appealing products.

[0103] In step 1804 (guide creation step) of Figure 18, the guide creation unit 205 may create link information 127 from the data (information) included in the product selection guide data 124. As already shown, the link information 127 is link information that identifies the location of the product description data 108 (for example, scientific description data 110 or integrated description data 111) for each of the sensory products listed in the product list data 126. The guide creation unit 205 may create link information 127 by associating information that identifies the location of product description data 108 (for example, scientific description data 110 or integrated description data 111) for each of the pieces of information that identifies the emotional products included in the product list data 126 created in step 1803 (for example, information similar to product identification data 107). Here, if the product description data 108 (for example, scientific description data 110 or integrated description data 111) is recorded in the data pool 102, the information that identifies the location of the product description data 108 (for example, scientific description data 110 or integrated description data 111) within the data pool 102 may be information indicating the location of the product description data 108 (for example, scientific description data 110 or integrated description data 111) within the data pool 102 (for example, information on the drive path name for managing the files recorded in the data pool 102). Alternatively, the information identifying the location of the product description data 108 (for example, scientific explanation data 110 or integrated explanation data 111) may be the file name that indicates the product description data 108 (for example, scientific explanation data 110 or integrated explanation data 111).

[0104] In step 1805 (guide creation step) of Figure 18, the guide creation unit 205 may use a set of the category selection questionnaire data 125 created in step 1802, the product list data 126 created in step 1803, and the link information 127 created in step 1804 as the product selection guide data 124. In step 1806 of Figure 18, the guide creation unit 205 transmits the created product selection guide data 124 to the data pool 102 via the communication system 105 and may request the data pool 102 to record the product selection guide data 124. In that case, the data pool 102 records the product selection guide data 124. Alternatively, in step 1806, the guide creation unit 205 may control the system to display or output some or all of the information contained in the created product selection guide data 124. The destination for display or output may be the display or output device 307 in the product information processing system 101, the product information output device 104, or the consumer terminal 103.

[0105] 5.5. Guide provided (Figure 19) Figure 19 shows a flowchart 1900 of the processing performed by the guide provision unit 207, which is a functional unit that the product information processing system 101 may have. The flowchart 1900 shows a series of processes that use product selection guide data 124 to assist consumers in selecting emotional products, and display or output information that identifies an emotional product or the classification to which an emotional product belongs, or product description data 108 about an emotional product, based on the information input by the consumer.

[0106] In step 1901 of Figure 19, the guide provision unit 207 determines whether or not a consumer (consumer terminal 103) has requested guidance for selecting an emotional product. The consumer terminal 103 can, for example, transmit a request for guidance for selecting an emotional product to the product information processing system 101 via the communication system 105. If the determination result in step 1901 is positive, control proceeds to step 1902. If the determination result in step 1901 is negative, control returns to step 1901 (for example, by delaying the operation of the guide provision unit 207 for a certain period of time, or by putting the guide provision unit 207 into a paused state until a request is received from the consumer terminal 103).

[0107] In step 1902 (guide provision step) of Figure 19, the guide provision unit 207 presents the category selection questionnaire to the consumer (consumer terminal 103). The guide provision unit 207 transmits either the category selection questionnaire data 125 itself, or information for displaying or outputting the category selection questionnaire created based on the category selection questionnaire data 125, to the consumer terminal 103 via the communication system 105. Based on the above, the consumer terminal 103 displays a category selection questionnaire on its display device (e.g., a display). The displayed category selection questionnaire may include a list of questions and a list of answer options for each question. Furthermore, radio buttons may also be displayed for each of the answer options. Consumers may select a radio button corresponding to their chosen option by operating the consumer terminal 103 (for example, by clicking a radio button with a mouse or by tapping a radio button on the screen). When a consumer activates a button (confirm button) indicating that they have finished answering the category selection questionnaire (for example, by clicking the confirm button on the screen with a mouse or by tapping the confirm button on the screen), information regarding which radio button (the option indicated by it) was selected for each question may be transmitted from the consumer terminal 103 to the product information processing system 101 via the communication system 105.

[0108] In step 1903 (guide provision step) in Figure 19, the guide provision unit 207 receives information from the consumer terminal 103 indicating the answer results for each question, or the selection results of the answer choices. Following the example shown in step 1902, the guide provision unit 207 may receive information from the consumer terminal 103 regarding which radio button (or choice indicated by it) was selected.

[0109] In step 1904 (guide provision step) in Figure 19, the guide provision unit 207 identifies the classification (category) to which the emotional product may belong, corresponding to the response result or the selection result of the response options received in step 1903. For example, the guide provision unit 207 uses the information contained in the category selection questionnaire data 125 to identify the classification (category) to which the emotional product may belong, corresponding to the response result or the selection result of the response options. Following the example given in the explanation of the guide creation unit 205, for example, if the selection result of the answer option for the first question is a predetermined one (e.g., option B), and the answer option for the second question is another predetermined one (e.g., option γ), then "Negative Emotion Reduction," one of the emotional base categories shown in Figure 17, may be identified as the classification (category). The guide provision unit 207 uses the product list data 126 to identify a list of emotional products that fall under the classification (category) identified above. The guide provision unit 207 transmits information indicating the list of emotional products identified above to the consumer terminal 103 via the communication system 105. The guide provision unit 207 may also transmit information identifying the classification (category) to the consumer terminal 103 via the communication system 105. Based on the above, the consumer terminal 103 displays a list of emotional products on its display device (e.g., a display). This display may include a checkbox for each emotional product. The consumer terminal 103 may also display the name of the classification (category) identified above on its display device (e.g., a display). Consumers may select the checkbox corresponding to their chosen emotional product by operating the consumer terminal 103 (for example, by clicking the checkbox with a mouse or tapping the checkbox on the screen). When a consumer activates a button indicating that they have finished selecting an emotional product (a confirmation button) (for example, by clicking the confirmation button on the screen with a mouse or by tapping the confirmation button on the screen), information regarding which emotional product from the list of emotional products has been selected may be transmitted from the consumer terminal 103 to the product information processing system 101.

[0110] In step 1905 (guide provision step) of Figure 19, the guide provision unit 207 receives information from the consumer terminal 103 regarding which of the sensory products (included in the list of sensory products) was selected. Following the example shown in step 1904, the guide provision unit 207 may also receive information from the consumer terminal 103 regarding which checkbox was selected.

[0111] In step 1906 of Figure 19, the guide provider 207 uses the link information 127 to identify the location of the product description data 108 (e.g., scientific explanation data 110 or integrated explanation data 111) for the sensory product that the consumer has been found to have selected. The guide provider 207 then retrieves the product description data 108 (e.g., scientific explanation data 110 or integrated explanation data 111). If product description data 108 (for example, scientific description data 110 or integrated description data 111) is recorded in the data pool 102, the guide provision unit 207 requests the product description data 108 (for example, scientific description data 110 or integrated description data 111) from the data pool 102 via the communication system 105. In response, the data pool 102 transmits the product description data 108 (for example, scientific description data 110 or integrated description data 111) to the product information processing system 101 via the communication system 105.

[0112] In step 1907 (guide provision step) in Figure 19, the guide provision unit 207 transmits the product description data 108 (for example, scientific explanation data 110 or integrated explanation data 111) acquired in step 1906, or data processed from said data, to the consumer terminal 103 via the communication system 105. Based on the above, the consumer terminal 103 displays the product description data 108 (for example, scientific explanation data 110 or integrated explanation data 111) itself, or data obtained by processing said data, for the sensory product selected by the consumer, on the display device (for example, a display) of the consumer terminal 103. After step 1907, control may be returned to step 1901.

[0113] 5-6. Product Proposal (Figure 20) Figure 20 shows a flowchart 2000 of the processes performed by the product suggestion unit 208, which is a functional unit that the product information processing system 101 may have. The flowchart 2000 shows a series of processes that identify consumer needs, identify categories of emotional products that meet those needs, select emotional products belonging to the identified categories, and display or output information identifying the selected emotional products or product description data 108 for those emotional products.

[0114] In step 2001 (product proposal step) of Figure 20, the product proposal unit 208 identifies the needs of the consumers to whom the emotional product is proposed. Here, the needs of the consumers to whom the emotional product is proposed may include, for example, the expected effects that the consumers expect from the emotional product. The product proposal unit 208 may acquire text data in the form of free-form writing created by the consumer or an agent on behalf of the consumer, such as a statement describing the effects the consumer expects from the sensory product or a statement describing the consumer's own profile, in order to identify the consumer's needs. In this case, for example, the text data may be transmitted from the consumer terminal 103 to the product information processing system 101 via the communication system 105. Alternatively, the product proposal unit 208 may acquire any information about the consumer in order to identify the consumer's needs. The product proposal unit 208 may use any of the following methods to identify consumer needs based on the acquired text data and arbitrary information. For example, the product proposal unit 208 may apply a natural language analysis method to the text data and arbitrary information to extract consumer needs. Alternatively, the product proposal unit 208 may, for example, input the above text data and arbitrary information into a machine learning model and then use the output from the machine learning model to identify consumer needs.

[0115] In step 2002 (product proposal step) of Figure 20, the product proposal unit 208 identifies classifications (categories) to which emotional products may belong that match the consumer needs identified in step 2001. At this time, the product proposal unit 208 may obtain a list of classifications (categories) to which emotional products may belong from the product classification data 123. The product proposal unit 208 may identify classifications (categories) to which emotional products may belong that match the consumer needs by comparing the list of classifications (categories) to which emotional products may belong with the consumer needs identified in step 2002. Alternatively, the product proposal unit 208 may, for example, input the information indicating the consumer needs and the information indicating the list of classifications (categories) to which emotional products may belong into a learning model that is trained using machine learning, and then use the output from the learning model to identify classifications (categories) (to which emotional products may belong) that match the consumer needs.

[0116] In step 2003 (product proposal step) of Figure 20, the product proposal unit 208 selects one or more emotional products from among those included in the classification (category) identified in step 2002. The product proposal unit 208 may select all emotional products included in the classification (category). Alternatively, the product proposal unit 208 may narrow down the emotional products included in the classification (category) based on some information (for example, arbitrary information such as consumer profiles or inventory information of emotional products) and select one or more emotional products.

[0117] In step 2004 (product proposal step) of Figure 20, the product proposal unit 208 proposes one or more of the emotional products selected in step 2003 to the consumer. The product proposal unit 208 may, for example, transmit information (product identification data 107) that identifies one or more of the emotional products to be proposed to the consumer terminal 103 via the communication system 105. In step 2005 (product proposal step) of Figure 20, the product proposal unit 208 provides consumers with product description data 108 (e.g., scientific explanation data 110 and integrated explanation data 111) for one or more of the emotional products selected in step 2003. The product proposal unit 208 may, for example, request product description data 108 (e.g., scientific explanation data 110 and integrated explanation data 111) for one or more of the emotional products to be proposed from the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 may transmit the product description data 108 (e.g., scientific explanation data 110 and integrated explanation data 111) to the product information processing system 101 via the communication system 105. Furthermore, the product proposal unit 208 may transmit product description data 108 (for example, scientific explanation data 110 or integrated explanation data 111) for one or more of the sensory products being proposed to the consumer terminal 103 via the communication system 105. The information transmitted to the consumer terminal 103 in steps 2004 and 2005 may be displayed on the display device of the consumer terminal 103, or output (e.g., printed) to the output device of the consumer terminal 103. Alternatively, the destination of the information transmitted in steps 2004 and 2005 may be the product information output device 104, and the product information output device 104 may display or output this information.

[0118] 5-7. Provision of information on designated products (Figure 21) Figure 21 shows a flowchart 2100 of the processing performed by the designated product information provision unit 209, which is a functional unit that the product information processing system 101 may have. The flowchart 2100 shows a series of processes that, upon receiving information to identify a sentimental product, acquire product description data 108 for the identified sentimental product and display or output the acquired product description data 108.

[0119] In step 2101 (designated product information provision step) in Figure 21, the designated product information provision unit 209 receives information from the consumer that identifies the emotional product. For example, information that identifies the emotional product (for example, data (information) corresponding to product identification data 107) may be transmitted from the consumer terminal 103 to the product information processing system 101 via the communication system 105. In step 2102 (specified product information provision step) in Figure 21, the specified product information provision unit 209 acquires product description data 108 (e.g., scientific description data 110 and integrated description data 111) for the emotional product indicated by the information received in step 2101. As shown in Figure 1, if the product description data 108 (e.g., scientific description data 110 and integrated description data 111) is to be recorded in the data pool 102, the specified product information provision unit 209 may transmit a request for the product description data 108 (e.g., scientific description data 110 and integrated description data 111) to the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the product description data 108 (e.g., scientific description data 110 and integrated description data 111) to the product information processing system 101 via the communication system 105.

[0120] In step 2103 (designated product information provision step) in Figure 21, the designated product information provision unit 209 provides the consumer with the product description data 108 (e.g., scientific explanation data 110 and integrated explanation data 111) acquired in step 2102. The designated product information provision unit 209 transmits either the product description data 108 (e.g., scientific explanation data 110 and integrated explanation data 111) itself, or data processed from the product description data 108 (e.g., scientific explanation data 110 and integrated explanation data 111), to the consumer terminal 103 via the communication system 105. The information transmitted to the consumer terminal 103 in step 2103 may be displayed on the display device of the consumer terminal 103, or output (e.g., printed) to an output device of the consumer terminal 103. Alternatively, the destination of the information transmitted in step 2103 may be the product information output device 104, and the product information output device 104 may display or output this information.

[0121] 5-8. Market support (Figure 22) Figure 22 shows a flowchart 2200 of the processes performed by the market support unit 211, which is a functional unit that the product information processing system 101 may have. The flowchart 2200 shows a series of processes for creating sales support data 131, which serves as reference information for companies selling emotional products to formulate sales policies (sales promotion activity policies) for emotional products.

[0122] In step 2201 of Figure 22, the market support unit 211 acquires consumer trend data 128. As shown in Figure 1, if the consumer trend data 128 is to be recorded in the data pool 102, the market support unit 211 may transmit a request for the consumer trend data 128 to the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the consumer trend data 128 to the product information processing system 101 via the communication system 105. As already shown, the consumer trend data 128 is data (information) that shows the trends in demand for each consumer of the emotional products. The consumer trend data 128 may include sales information for each emotional product. In step 2202 of Figure 22, the market support unit 211 acquires product classification data 123. As shown in Figure 1, if the product classification data 123 is to be recorded in the data pool 102, the market support unit 211 may transmit a request for the product classification data 123 to the data pool 102 via the communication system 105. Upon receiving the request, the data pool 102 transmits the product classification data 123 to the product information processing system 101 via the communication system 105.

[0123] In step 2203 (market support step) of Figure 22, the market support unit 211 may create classification-result correlation data 129 (second correlation data) using the consumer trend data 128 obtained in step 2201 and the product classification data 123 obtained in step 2202. As already shown, classification-result correlation data 129 (second correlation data) is data (information) that shows the level and degree of demand from consumers, etc. for each classification (category) of emotional products. For example, the market support unit 211 may edit the sales data for each emotional product shown in the consumer trend data 128 into sales data for each classification (category) according to the classification (category) information to which the emotional products shown in the product classification data 123 belong, and use this as classification-result correlation data 129 (second correlation data). The market support unit 211 may record the created classification-outcome correlation data 129 (second correlation data). As shown in Figure 1, when the classification-outcome correlation data 129 (second correlation data) is to be recorded in the data pool 102, the market support unit 211 may transmit the classification-outcome correlation data 129 (second correlation data) to the data pool 102 via the communication system 105 and request that the data pool 102 record the classification-outcome correlation data 129 (second correlation data). Upon receiving the request, the data pool 102 records the classification-outcome correlation data 129 (second correlation data).

[0124] In step 2204 (market support step) of Figure 22, the market support unit 211 creates sales support data 131 using classification-outcome correlation data 129 (second correlation data). As already shown, the sales support data 131 may be, for example, classifications (categories) of emotional products for which consumer demand is expected (the expected value of demand is relatively high) or information identifying emotional products. Therefore, the market support unit 211 may identify classifications (categories) of emotional products for which consumer demand is expected (the expected value of demand is relatively high) based on sales data for each classification (category) included in the classification-outcome correlation data 129 (second correlation data), and include the information of the identified classifications (categories) in the sales support data 131. Furthermore, the market support department 211 may use the product classification data 123 to identify a list of emotional products that fall under the classification (category) of emotional products for which consumer demand is expected (i.e., the expected value of demand is relatively high), and include the information of the identified list of emotional products in the sales support data 131. In addition to the classification (category) of emotional products for which consumer demand is expected (the expected value of demand is relatively high) and information identifying emotional products, the Market Support Department 211 may also include related information regarding the identified classification (category) and emotional products (for example, product description data 108, consumer trend data 128, or sales performance data derived from classification-performance correlation data 129 (second correlation data)) in the sales support data 131. The market support unit 211 may, via the communication system 105, request the data pool 102 to record the sales support data 131 it has created, while simultaneously transmitting the data pool 102. Upon receiving the request, the data pool 102 records the sales support data 131. The market support unit 211 may control the system to display or output the created sales support data 131. The destination for display or output may be the display or output device 307 within the product information processing system 101, the product information output device 104, or a display or output device on an information terminal or information processing device handled by the person receiving sales support.

[0125] 5.9. Development Support (Figure 23) Figure 23 shows a flowchart 2300 of the processes performed by the development support unit 212, which is a functional unit that the product information processing system 101 may have. The flowchart 2300 shows a series of processes for creating development support data 132, which serves as reference information for companies developing emotional products to formulate development policies indicating what kind of emotional products they will develop.

[0126] The processes performed in steps 2301, 2302, and 2303 (development support steps) in Figure 23 are the same as the processes performed in steps 2201, 2202, and 2203 in Figure 22. However, steps 2201, 2202, and 2203 in Figure 22 are executed by the market support unit 211, whereas steps 2301, 2302, and 2303 (development support steps) in Figure 23 are executed by the development support unit 212. Therefore, the functional unit for executing the above steps may be common to both the market support unit 211 and the development support unit 212.

[0127] In step 2304 (development support step) in Figure 23, the development support unit 212 uses the classification-outcome correlation data 129 (second correlation data) to identify the classification (category) to which a hypothetical emotional product would be expected to have high results (e.g., consumer demand (market sales)) if such a product were developed. This identification method may be the same as in step 2204 in Figure 22. For example, if the classification-outcome correlation data 129 (second correlation data) includes sales data for each classification (category), the development support unit 212 may use the classification-outcome correlation data 129 (second correlation data) to identify classifications (categories) with high sales.

[0128] In step 2305 (development support step) of Figure 23, the development support unit 212 creates development support data 132, taking into account the classification (category) identified in step 2304. As already shown, the development support data 132 may include information about emotional products or classifications to which emotional products belong that are expected to have high demand. While sales support data 131 is generally intended to support the formulation of sales policies (promotional activity policies) for existing sensory products, development support data 132 is intended to support the formulation of development policies for developing sensory products that do not yet exist. Therefore, development support data 132 may include various information useful for the development of sensory products. For example, in addition to information that identifies classifications (categories), development support data 132 may include various information for each sensory product included in the classification (category) identified in step 2304, such as expected efficacy (scientific expected efficacy data 112), contained fragrances (general descriptive data 109), tone (general descriptive data 109), and characteristic components (component-based efficacy data 116, component survey result data 121). The development support unit 212 may transmit the development support data 132 to the data pool 102 via the communication system 105 and request that the development support data 132 be recorded. Upon receiving the request, the data pool 102 records the development support data 132.

[0129] In step 2306 of Figure 23, the development support unit 212 presents the development support data 132 created in step 2305 to the person developing the emotional product. The development support unit 212 controls, for example, to display or output the development support data 132. The destination for displaying or outputting the development support data 132 may be the display or output device 307 within the product information processing system 101, the product information output device 104, or a display or output device of an information terminal or information processing device handled by the person developing the emotional product.

[0130] 6. Other (Variations) This disclosure is not limited to the embodiments described above and includes various modifications. Some of the configurations and processes of the embodiments may be replaced with configurations and processes of other conceivable embodiments. Configurations and processes of other conceivable embodiments may be added to the configurations and processes of the embodiments. For example, the following modifications of the embodiments may be made in this disclosure.

[0131] (A) Inclusion of data pool within product information processing system In the above embodiment, the product information processing system 101 and the data pool 102 were able to communicate with each other via the communication system 105. In a modified example, the data pool 102 may be included within the product information processing system 101. For example, the data (information) shown in Figure 1 may be recorded on the non-volatile recording medium (recording device) 303 within the product information processing system 101. The above modification allows for a more compact overall system configuration and enables faster access to various types of data (information) from the product information processing system 101.

[0132] (B) Printed materials, such as paper media, from the created data. The above description primarily explained the use of the generated data (information), including scientific explanation data 110, integrated explanation data 111, scientific expected efficacy data 112 (sensory-based efficacy data 113, electroencephalogram-based efficacy data 114, electrocardiogram-based efficacy data 115, and component-based efficacy data 116), product classification data 123, product selection guide data 124 (category selection questionnaire data 125, product list data 126, and link information 127), classification-outcome correlation data 129 (second correlation data), and business support data 130 (sales support data 131 and development support data 132), as digital data. However, the use of the data (information) created in the embodiments of this disclosure may be in other ways. For example, some or all of the data (information) created in the embodiments of this disclosure may be printed on paper or the like using some kind of output device to become printed materials (e.g., brochures, reports, questionnaires). Consumers who intend to select and purchase emotional products, those who intend to sell emotional products, those who intend to formulate sales policies (promotional activity policies) for emotional products, those who intend to develop emotional products, and those who intend to formulate development policies for emotional products may then view the printed materials and make various decisions based on the contents of the printed materials. Even when using printed materials as described above, this disclosure will produce the same effect as when the created data (information) is used as digital data. Furthermore, even if it is difficult or undesirable to handle the data (information) as digital data due to the circumstances of consumers who select and purchase emotional products, those who sell emotional products, those who formulate sales policies (promotional activity policies) for emotional products, those who develop emotional products, or those who formulate development policies for emotional products, this disclosure can provide a flexible solution through the use of printed materials as described above.

[0133] (C) Use of learning models in product selection guide function In the above embodiment, the product selection guide data 124 consisted of category selection questionnaire data 125, product list data 126, and link information 127. In this modified version, it is assumed that a machine learning-trained learning model is constructed to output information that identifies an emotional product or the classification to which an emotional product belongs, based on information input by consumers, etc. In this modified version, the product selection guide data 124 may be data that represents the learning model. Here, the information input to the learning model may be the results of consumers' answers to some questionnaire or the results of their selection of answer choices. The above modifications can be used to construct a system that assists consumers in selecting emotionally appealing products by utilizing learning model techniques.

[0134] The technical matters shown in each of the embodiments and modifications of the embodiments described above can be combined as appropriate, as long as no technical inconsistencies arise.

[0135] 7. Addendum As shown above, embodiments (or variations) of this disclosure may include, for example, the following technical matters:

[0136] (Note 1) Product information processing system, A product information processing system having a product description creation unit that creates scientific explanatory data for an emotional product based on expected efficacy data for that emotional product. (Effects of Appendix 1) The product information processing system (see Appendix 1) generates scientific explanatory data for sensory products. This scientific explanatory data is based on expected efficacy data for sensory products. Therefore, by referring to the scientific explanatory data, consumers can obtain information about sensory products that is based on objective scientific evidence. Furthermore, even if the description content and methodology of the expected efficacy data for sensory products itself are specialized and difficult for consumers to understand, the scientific explanatory data itself is easy for consumers to understand. As described above, the product information processing system (Appendix 1) can enable consumers to more easily and accurately select sensory products such as fragrances in the market.

[0137] (Note 2) (Note 1) The product information processing system described above, The expected efficacy data for the aforementioned sensory product is sensory-based efficacy data, electroencephalogram-based efficacy data, electrocardiogram-based efficacy data, or ingredient-based efficacy data for the said sensory product. Product information processing system, wherein the product description creation unit creates scientific descriptive data for the emotional product, expressed in charts or natural language, based on one or more of the emotional-based efficacy data, electroencephalogram-based efficacy data, electrocardiogram-based efficacy data, or component-based efficacy data for the emotional product. (Effects of the effects described in Appendix 2) The product information processing system (Note 2) can handle the expected effects of a sensory product related to the user's sensibilities, the user's brainwaves, the user's electrocardiogram, or one or more of the components (e.g., characteristic components) contained in the sensory product. Furthermore, the product information processing system (Note 2) can express scientific explanatory data about sensory products based on such expected effects in charts and natural language. Therefore, the product information processing system (Note 2) can handle reasonable expected effects of sensory products and provide scientific explanatory data that is reasonable from a scientific or objective standpoint. In addition, by expressing reasonable scientific explanatory data in charts and natural language, the product information processing system (Note 2) can provide information in an easy-to-understand manner even to consumers who may not have extensive expertise in sensory products. Furthermore, when the product information processing system (Note 2) creates scientific explanatory data based on multiple types of expected efficacy-based data (for example, types such as emotional, electroencephalogram, electrocardiogram (Figure), and ingredient), the resulting scientific explanatory data can be expected to be more scientifically and objectively valid. In particular, when the product information processing system (Note 2) creates scientific explanatory data based on three or more types of expected efficacy-based data, the reliability and accuracy of the resulting scientific explanatory data can be expected to increase. In other words, the effectiveness of assisting consumers in selecting and purchasing emotional products can be expected to increase.

[0138] (Note 3) (Note 1) The product information processing system described above, The product information processing system includes a product description creation unit that creates integrated descriptive data for the emotional product by integrating the scientific descriptive data for the emotional product with the general descriptive data for the emotional product. (Effects of Appendix 3) The product information processing system described in (Note 3) can provide multifaceted information about emotional products to those who view the integrated descriptive data by creating integrated descriptive data about emotional products. Furthermore, since general descriptive data and scientific descriptive data about emotional products are presented together, those receiving the information are spared the trouble of gathering information from various sources.

[0139] (Note 4) Product information processing system, A product information processing system having an expected efficacy data creation unit that creates expected efficacy data for a product based on first correlation data and survey results data for the product. (Effects of Appendix 4) The product information processing system described in (Note 4) can create expected efficacy data that expresses the expected efficacy of a sensory product, based on survey results data, which, although scientific or objective data (information), does not necessarily directly express the expected efficacy of a sensory product. Therefore, the product information processing system described in (Note 4) can provide useful data (information) for processing that uses data (information) related to the expected efficacy of a sensory product. In this case, the product information processing system described in (Note 4) can appropriately create expected efficacy data based on survey results data by utilizing the first correlation data.

[0140] (Note 5) (Appendix 4) The product information processing system described above, The aforementioned survey data for the aforementioned sensory product is sensory survey data, electroencephalogram (EEG) survey data, electrocardiogram (ECG) survey data, or component survey data for the said sensory product. The expected efficacy data for the aforementioned sensory product is sensory-based efficacy data, electroencephalogram-based efficacy data, electrocardiogram-based efficacy data, or ingredient-based efficacy data for the said sensory product. The aforementioned expected efficacy data creation unit, Based on the first correlation data and the sensory survey results data, the sensory-based efficacy data is created, Based on the first correlation data and the electroencephalogram (EEG) survey results, the EEG-based efficacy data is created, Based on the first correlation data and the electrocardiogram survey results, the electrocardiogram-based efficacy data is created, A product information processing system that creates ingredient-based efficacy data based on the first correlation data and the ingredient investigation results data. (Effects of Appendix 5) The product information processing system (Note 5) can handle research results and expected efficacy of the product relating to the sensibilities of the user of the product, the brainwaves of the user of the product, the electrocardiogram of the user of the product, or one or more of the components (e.g., characteristic components) contained in the product. Therefore, the product information processing system (Note 5) can handle research results and expected efficacy of the product that are appropriate, and can provide expected efficacy data that is appropriate from a scientific or objective standpoint.

[0141] (Note 6) (Appendix 4) The product information processing system described above, The product information processing system includes a product information processing unit which inputs the first correlation data and the survey results data for the emotional product to a learning model that is trained by machine learning, and uses the output data from the learning model to create the expected efficacy data for the emotional product. (Effects of Appendix 6) (Note 6) The product information processing system utilizes a learning model trained using machine learning, which allows for a simplified functional configuration of parts other than the learning model, and as the level of training of the learning model increases, it becomes possible to obtain highly accurate expected efficacy data.

[0142] (Note 7) Product information processing system, A product information processing system having a product classification unit that determines the classification to which a product belongs based on one or more of the following: general descriptive data, survey results data, or expected efficacy data for the product, and creates product classification data indicating the classification to which each product belongs. (Effects of Appendix 7) The product information processing system described in (Appendix 7) classifies emotional products using data relating to at least one of various perspectives. Therefore, it provides a reasonable classification of emotional products. Furthermore, the product information processing system described in (Appendix 7) creates product classification data that shows the results of the classification of emotional products, so it can provide appropriate classification information for processing that relies on the classification of emotional products.

[0143] (Note 8) (Appendix 7) The product information processing system described above, The classification of the aforementioned sensory product is one or more of the following: classification based on the ingredients of the sensory product, classification based on the sensibilities of the person who used the sensory product, classification based on the brainwaves of the person who used the sensory product, classification based on the tone of the sensory product, classification based on the fragrances contained in the sensory product, and classification based on the electrocardiogram of the person who used the sensory product. The aforementioned product classification unit is Based on the ingredient survey results data or ingredient-based efficacy data for the aforementioned sensory product, a classification based on the ingredients of the sensory product is performed, Based on the sensory survey results data or sensory-based efficacy data for the aforementioned sensory product, a classification based on the sensory perception of the person who used the product is performed. Based on the electroencephalogram (EEG) survey data and EEG-based efficacy data for the aforementioned sensory product, a classification based on the brainwaves of those who used the sensory product may be performed. Based on the information regarding the tone of the aforementioned emotional product included in the general descriptive data for that emotional product, a classification based on the tone of the emotional product may be performed. Based on the information regarding the fragrances contained in the aforementioned sensory product included in the general descriptive data for the said sensory product, a classification based on the fragrances contained in the said sensory product may be performed. A product information processing system that performs classification based on the electrocardiogram of a person who used the aforementioned sensory product, based on electrocardiogram survey data or electrocardiogram-based efficacy data for the said sensory product. (Effects of Appendix 8) The product information processing system (see Appendix 8) performs at least one of the following classifications of sensory products: classification based on the ingredients of the sensory product, classification based on the sensory product's sensibilities, classification based on the user's brainwaves, classification based on the tone of the sensory product, classification based on the fragrance of the sensory product, and classification based on the user's electrocardiogram. Therefore, it can provide useful product classification data for classifying sensory products. Furthermore, in any of the classifications performed by the product information processing system (see Appendix 8), the system classifies sensory products based on appropriate data (information) for that type, thus providing appropriate product classification data.

[0144] (Note 9) Product information processing system, A product information processing system having a guide creation unit that creates product selection guide data to assist consumers in selecting emotionally appealing products. (Effects of Appendix 9) The product information processing system (see Appendix 9) can create product selection guide data, which forms the basis of a guide function to assist consumers in selecting emotionally appealing products. Therefore, when consumers want assistance in selecting emotionally appealing products, a guide function for assistance can be provided promptly.

[0145] (Note 10) (Appendix 9) The product information processing system described above, The aforementioned product selection guide data is Category selection questionnaire data that allows for the identification of the classification of sensory products based on the answers to the included questions or the selection results to the answer choices, Product list data showing a list of the sentiment products included in the classification identified according to the aforementioned response result or selection result, This includes link information indicating a link to product description data for each of the aforementioned emotional products included in the aforementioned list of emotional products, The aforementioned guide creation unit creates the category selection questionnaire data and the product list data based on the product classification data indicating the classification to which each of the aforementioned sensory products belongs, in a product information processing system. (Effects of Appendix 10) The product information processing system (see Appendix 10) can create product selection guide data, thereby enabling a series of functions: identifying the classification (category) to which an emotional product may belong based on the answers to the questions in the category selection questionnaire or the selection of answer options; identifying a list of emotional products included in the identified classification (category); and accessing product description data for the emotional products included in that list. In this way, the product information processing system (see Appendix 10) can construct a mechanism that allows consumers to quickly obtain assistance in selecting emotional products when they answer (select answer options in) the category selection questionnaire.

[0146] (Note 11) (Appendix 9) The product information processing system described above, The product information processing system is a system in which the product selection guide data represents a learning model that is trained using machine learning to output information that identifies the emotional product or the classification to which the emotional product belongs, based on the information input by the consumer. (Effects of the effects described in Appendix 11) The product information processing system (Appendix 11) uses product selection guide data as data representing a learning model trained using machine learning. This allows the product information processing system (Appendix 11) to build a mechanism that utilizes the learning model method to assist consumers in selecting emotionally appealing products.

[0147] (Note 12) Product information processing system, A product information processing system having a guide providing unit that uses product selection guide data to assist consumers in selecting emotional products, and outputs information that identifies the emotional product or the category to which the emotional product belongs, or product description data about the emotional product, based on information input by the consumer. (Effects of Appendix 12) The product information processing system (see Appendix 12) can assist consumers in selecting emotional products. Here, the product information processing system (see Appendix 12) provides consumers with information that identifies emotional products or categories, as well as product description data about emotional products, and can therefore provide appropriate information to assist consumers in selecting emotional products.

[0148] (Note 13) (Appendix 12) The product information processing system described above, The aforementioned product selection guide data is Category selection questionnaire data that allows for the identification of the classification of sensory products based on the answers to the included questions or the selection results to the answer choices, Product list data showing a list of the sentiment products included in the classification identified according to the aforementioned response result or selection result, This includes link information indicating a link to product description data for each of the aforementioned emotional products included in the aforementioned list of emotional products, A product information processing system comprising: a guide provision unit that displays or outputs data based on the category selection questionnaire data, identifies a list of emotional products corresponding to the answer results or selection results, displays or outputs the identified list of emotional products, receives selection information for an emotional product from the list of emotional products, obtains product description data for the emotional product using the link information for the emotional product indicated by the selection information, and displays or outputs the obtained product description data. (Effects of Appendix 13) The product information processing system described in (Note 13) can perform a series of functions, including identifying the classification (category) to which an emotional product may belong based on the answers to the questions included in the category selection questionnaire or the selection of answer options, identifying a list of emotional products included in the identified classification (category), accessing product description data for the emotional products included in that list, and providing that product description data to consumers. In this way, when consumers answer the category selection questionnaire (select answer options), they can quickly obtain assistance in selecting emotional products.

[0149] (Note 14) (Appendix 12) The product information processing system described above, The aforementioned guide provision unit implements a learning model that is trained by machine learning based on the aforementioned product selection guide data, and outputs information that identifies the aforementioned emotional product or the classification to which the aforementioned emotional product belongs, in response to the information input by the consumer, as a product information processing system. (Effects of Appendix 14) The product information processing system described in (Appendix 14) allows for a simplified functional configuration of parts other than the learning model, and as the level of training of the learning model increases (the quality of the product selection guide data improves), it becomes possible to provide consumers with more appropriate information to assist them in selecting emotionally appealing products.

[0150] (Note 15) Product information processing system, A product information processing system having a product suggestion unit that identifies consumer needs, identifies categories of emotional products that meet those needs, selects emotional products belonging to the identified categories, and displays or outputs information identifying the selected emotional products or product description data for those emotional products. (Effects of Appendix 15) The product information processing system described in (Note 15) can identify consumer needs and provide consumers with information on emotionally appealing products that meet those needs, even when consumer needs are not clearly presented.

[0151] (Note 16) (Appendix 15) The product information processing system described above, A product information processing system in which the consumer's needs include information that identifies the expected efficacy, which is the efficacy that the consumer expects from the sensory product. (Effects of Appendix 16) The product information processing system (see Appendix 16) identifies the expected effects that consumers expect from emotional products as a need, thus ensuring that the selection of emotional products is appropriate.

[0152] (Note 17) Product information processing system, A product information processing system having a designated product information provision unit that, upon receiving information identifying a product, acquires product description data for the identified product and displays or outputs the acquired product description data. (Effects of the effects described in Appendix 17) The product information processing system (see Appendix 17) can provide consumers with product description data for explicitly identified emotional products. In this way, the product information processing system (see Appendix 17) can appropriately respond when consumers request product description data for emotional products that they have identified in advance.

[0153] (Note 18) Product information processing system, A product information processing system having a market support unit that creates sales support data including information about the aforementioned emotional products or the classification to which the aforementioned emotional products belong that is expected to have high demand, using consumption trend data for each emotional product and product classification data indicating the classification to which each of the aforementioned emotional products belongs. (Effects of Appendix 18) The product information processing system (see Appendix 18) can provide sales support data to those who formulate sales policies (market strategies and tactics) for emotional products, which can help them in formulating those sales policies (market strategies and tactics). In other words, those who formulate sales policies (market strategies and tactics) for emotional products can formulate sales policies (market strategies and tactics) that introduce emotional products into the market for which consumer demand is expected to be relatively high.

[0154] (Note 19) (Appendix 18) The product information processing system described above, The Market Support Department, using the Consumer Trend Data and the Product Classification Data, creates a second correlation data showing the correlation between the classification to which the emotional product belongs and the sales performance of the emotional product belonging to that classification, in a product information processing system. (Effects of Appendix 19) The product information processing system (see Appendix 19) can provide second correlation data showing consumer demand (sales results) for each classification (category) to which emotional products may belong. Such second correlation data can serve as a source of information that forms the basis for formulating sales policies (market strategies and tactics) for emotional products.

[0155] (Note 20) Product information processing system, A product information processing system having a development support unit that creates development support data including information about the aforementioned emotional products or the classification to which the aforementioned emotional products belong that is expected to have high demand, using consumption trend data for each emotional product and product classification data indicating the classification to which each of the aforementioned emotional products belongs. (Effects of Appendix 20) The product information processing system (see Appendix 20) can provide development support data to those who formulate development policies for emotional products, which can assist in the formulation of such development. In other words, those who formulate development policies for emotional products can target emotional products for which consumer demand is expected to be relatively high, and formulate development policies to develop such products.

[0156] (Note 21) (Note 20) The product information processing system described above, The aforementioned development support department creates a second correlation data that shows the correlation between the classification to which the emotional product belongs and the sales performance of the emotional product belonging to that classification, using the aforementioned consumer trend data and the aforementioned product classification data, in a product information processing system. (Effects of Appendix 21) The product information processing system (see Appendix 21) can provide a second set of correlational data showing consumer demand (sales results) for each category to which emotional products may belong. Such second set of correlational data can serve as a source of information to support the formulation of development policies for emotional products.

[0157] (Note 22) (Note 20) The product information processing system described above, The aforementioned development support department identifies the categories to which emotional products that are expected to sell well belong, and includes information on the emotional products belonging to the identified categories in the development support data. A product information processing system in which the information on the sensory product included in the development support data includes information on one or more of the following: expected efficacy data for the sensory product, the classification to which the sensory product belongs, the fragrances contained in the sensory product, the tone of the sensory product, or the components that characterize the sensory product. (Effects of the effects described in Appendix 22) The product information processing system (see Appendix 22) can provide information that serves as a hint regarding the various attributes that the emotional products being developed may possess, as development support data.

[0158] (Note 23) A method for processing product information that a system performs, A product information processing method comprising a step of creating a product description, which creates scientific explanatory data for an emotional product based on expected efficacy data for the said emotional product. (Effects of Appendix 23) The product information processing method described in (Appendix 23) has the same or similar effects as the product information processing system described in (Appendix 1).

[0159] (Note 24) A method for processing product information that a system performs, A product information processing method comprising a step of creating expected efficacy data for a product based on first correlation data and survey results data for the product. (Effects of Appendix 24) The product information processing method described in (Appendix 24) has the same or similar effects as the product information processing system described in (Appendix 4).

[0160] (Note 25) A method for processing product information that a system performs, A product information processing method comprising a product classification step of determining the classification to which a product belongs based on one or more of the following: general descriptive data, survey results data, or expected efficacy data for the product; and creating product classification data indicating the classification to which each product belongs. (Effects of Appendix 25) The product information processing method described in (Appendix 25) has the same or similar effects as the product information processing system described in (Appendix 7).

[0161] (Note 26) A method for processing product information that a system performs, A product information processing method comprising a guide creation step for creating product selection guide data to assist consumers in selecting emotional products. (Effects of Appendix 26) The product information processing method described in (Appendix 26) has the same or similar effects as the product information processing system described in (Appendix 9).

[0162] (Note 27) A method for processing product information that a system performs, A product information processing method comprising a guide provision step that uses product selection guide data to assist consumers in selecting emotional products, and outputs information that identifies the emotional product or the category to which the emotional product belongs, or product description data about the emotional product, based on information input by the consumer. (Effects of Appendix 27) The product information processing method described in (Appendix 27) has the same or similar effects as the product information processing system described in (Appendix 12).

[0163] (Note 28) A method for processing product information that a system performs, A product information processing method comprising a product suggestion step of identifying consumer needs, identifying categories of emotional products that meet those needs, selecting emotional products belonging to the identified categories, and displaying or outputting information that identifies the selected emotional products or product description data for those emotional products. (Effects of Appendix 28) The product information processing method described in (Appendix 28) has the same or similar effects as the product information processing system described in (Appendix 15).

[0164] (Note 29) A method for processing product information that a system performs, A product information processing method comprising a designated product information provision step of receiving information that identifies a product, acquiring product description data for the identified product, and displaying or outputting the acquired product description data. (Effects of Appendix 29) The product information processing method described in (Appendix 29) has the same or similar effects as the product information processing system described in (Appendix 17).

[0165] (Note 30) A method for processing product information that a system performs, A product information processing method comprising a market support step of creating sales support data that includes information about the aforementioned emotional products or the classification to which the aforementioned emotional products belong, using consumption trend data for each emotional product and product classification data indicating the classification to which each of the aforementioned emotional products belongs. (Effects of Appendix 30) The product information processing method described in (Appendix 30) has the same or similar effects as the product information processing system described in (Appendix 18).

[0166] (Note 31) A method for processing product information that a system performs, A product information processing method comprising a development support step of creating development support data that includes information about the aforementioned emotional products or the classification to which the aforementioned emotional products belong, using consumption trend data for each emotional product and product classification data indicating the classification to which each emotional product belongs. (Effects of Appendix 31) The product information processing method described in (Appendix 31) has the same or similar effects as the product information processing system described in (Appendix 20).

[0167] (Note 32) Product information processing program, In the system, A product information processing program that executes a product description creation step, which generates scientific explanatory data for a sensory product based on expected efficacy data for that product. (Effects of Appendix 32) The product information processing program (Appendix 32) has the same or similar effects as the product information processing system (Appendix 1).

[0168] (Note 33) Product information processing program, In the system, A product information processing program that executes an expected efficacy data creation step, which creates expected efficacy data for a particular emotional product based on the first correlation data and the survey results data for that emotional product. (Effects of Appendix 33) The product information processing program (Appendix 33) has the same or similar effects as the product information processing system (Appendix 4).

[0169] (Note 34) Product information processing program, In the system, A product information processing program that performs a product classification step to determine the classification to which a product belongs based on one or more of the following: general descriptive data, survey results data, or expected efficacy data for the product, and to create product classification data indicating the classification to which each product belongs. (Effects of Appendix 34) The product information processing program (Appendix 34) has the same or similar effects as the product information processing system (Appendix 7).

[0170] (Note 35) Product information processing program, In the system, A product information processing program that executes a guide creation step to generate product selection guide data to assist consumers in selecting emotionally appealing products. (Effects of Appendix 35) The product information processing program (Appendix 35) has the same or similar effects as the product information processing system (Appendix 9).

[0171] (Note 36) Product information processing program, In the system, A product information processing program for executing a guidance providing step of outputting information for specifying the sentimental product or the classification to which the sentimental product belongs, or product description data about the sentimental product, with respect to the information input by the consumer, using product selection guide data for assisting the consumer in selecting a sentimental product. (Operation and effect of Supplementary Note 36) The product information processing program of (Supplementary Note 36) has the same or similar operation and effect as the product information processing system of (Supplementary Note 12).

[0172] (Supplementary Note 37) A product information processing program, for causing a system to execute a product proposal step of identifying the needs of a consumer, identifying the classification of sentimental products that meet the needs, selecting the sentimental products belonging to the identified classification, and displaying or outputting information for specifying the selected sentimental products or product description data about the sentimental products. (Operation and effect of Supplementary Note 37) The product information processing program of (Supplementary Note 37) has the same or similar operation and effect as the product information processing system of (Supplementary Note 15).

[0173] (Supplementary Note 38) A product information processing program, for causing a system to execute a designated product information providing step of obtaining product description data about the specified sentimental product in response to receiving information for specifying a sentimental product, and displaying or outputting the obtained product description data. (Operation and effect of Supplementary Note 38) The product information processing program of (Supplementary Note 38) has the same or similar operation and effect as the product information processing system of (Supplementary Note 17).

[0174] (Supplementary Note 39) A product information processing program, for causing a system to A product information processing program that executes a market support step to create sales support data that includes information about the aforementioned emotional products or the classification to which the aforementioned emotional products belong, using consumption trend data for each emotional product and product classification data indicating the classification to which each of the aforementioned emotional products belongs. (Effects of Appendix 39) The product information processing program (Appendix 39) has the same or similar effects as the product information processing system (Appendix 18).

[0175] (Note 40) Product information processing program, In the system, A product information processing program that executes a development support step to create development support data that includes information about the aforementioned emotional products or the classification to which the aforementioned emotional products belong, using consumption trend data for each emotional product and product classification data indicating the classification to which each of the aforementioned emotional products belongs. (Effects of Appendix 40) The product information processing program (Appendix 40) has the same or similar effects as the product information processing system (Appendix 20).

Claims

1. Product information processing system, The aforementioned product information processing system is Based on the first correlation data and the survey results data for the sensory product, the expected efficacy data creation unit creates expected efficacy data for the sensory product. The system has a product description creation unit that creates scientific explanatory data or integrated explanatory data for the aforementioned sensory product based on the expected efficacy data for the said sensory product, The aforementioned survey results data for the aforementioned emotional product includes the emotional survey results data for the said emotional product. The expected efficacy data for the aforementioned sensory product includes sensory-based efficacy data for the said sensory product. The product description creation unit creates, based on the sensory-based efficacy data for the sensory product, the scientific explanatory data expressed in charts or natural language, or the integrated explanatory data that includes the content that should be included in the scientific explanatory data for the sensory product. The aforementioned sentiment survey results are obtained based on the VAS method or mood profile test. The data from the aforementioned sensory survey results for the aforementioned sensory products, The results of a sentiment survey obtained by statistically processing the responses of a sentiment questionnaire answered by those who have been exposed to the comparison object, and the results obtained by statistically processing the group of people who have been exposed to the comparison object, The results of a survey on the emotional aspects of a questionnaire answered by those who have encountered the aforementioned emotional product, including the results obtained by performing statistical processing on the group of people who have encountered the said emotional product, The first correlation data mentioned above is data that represents knowledge or theories obtained from academic research, The first correlation data includes data showing the relationship between the content of the responses to the emotional questionnaire regarding the emotional product and the expected efficacy of the emotional product. The expected efficacy data creation unit creates the emotion-based efficacy data based on the expected efficacy associated in the first correlation data with the emotion in which the emotion product has a stronger influence compared to the comparison target, as indicated by the emotion survey results data for the emotion product. The aforementioned product description creation unit, The expected efficacy data is input into a learning model that is trained using machine learning, and the scientific explanatory data is created based on the output from the learning model corresponding to the input, or, A product information processing system that inputs the expected efficacy data and general descriptive data about the emotional product into a learning model trained by machine learning, and creates the integrated descriptive data based on the output from the learning model in response to the input.

2. Product information processing system, The aforementioned product information processing system is Based on the first correlation data and the survey results data for the sensory product, the expected efficacy data creation unit creates expected efficacy data for the sensory product. The system has a product description creation unit that creates scientific explanatory data or integrated explanatory data for the aforementioned sensory product based on the expected efficacy data for the said sensory product, The aforementioned survey data for the aforementioned sensory product includes electroencephalogram (EEG) survey data for the said sensory product. The expected efficacy data for the aforementioned sensory product includes electroencephalogram-based efficacy data for the said sensory product. The product description creation unit creates, based on the electroencephalogram-based efficacy data for the said sensory product, the said scientific explanatory data expressed in charts or natural language, or the said integrated explanatory data that includes the content that should be included in the said scientific explanatory data. The electroencephalogram (EEG) survey results include data relating to the frequency band of the EEG and the intensity of the EEG relative to its location within the brain, or data showing the relationship between the logarithm of the EEG frequency and the EEG intensity. The electroencephalogram (EEG) survey results for the aforementioned sensory product are as follows: This includes data on the frequency band of brainwaves and the intensity of brainwaves relative to their location in the brain of a person who has come into contact with the object being compared, and data on the frequency band of brainwaves and the intensity of brainwaves relative to their location in the brain of a person who has come into contact with the aforementioned sensory product, or This includes data showing the relationship between the frequency and logarithm of brainwave intensity of a person who has been exposed to a comparative object, and data showing the relationship between the frequency and logarithm of brainwave intensity of a person who has been exposed to the aforementioned sensory product. The first correlation data mentioned above is data that represents knowledge or theories obtained from academic research, The first correlation data includes data showing the relationship between the results of an electroencephalogram (EEG) survey on the sensory product and the expected efficacy of the sensory product. The aforementioned expected efficacy data creation unit, Based on the differences in the frequency band and brainwave intensity relative to the brainwave location for the aforementioned sensory product compared to the comparison target, as shown in the brainwave survey results data for the aforementioned sensory product, brainwave-based efficacy data is created based on the expected efficacy associated in the first correlation data, or, Based on the expected efficacy associated in the first correlation data, the difference in the slope of the regression line between the frequency and intensity of brain waves produced by the aforementioned sensory product compared to the comparison target, as shown in the brainwave survey results data for the aforementioned sensory product, is used to create the brainwave-based efficacy data. The aforementioned product description creation unit, The expected efficacy data is input into a learning model that is trained using machine learning, and the scientific explanatory data is created based on the output from the learning model corresponding to the input, or, A product information processing system that inputs the expected efficacy data and general descriptive data about the emotional product into a learning model trained by machine learning, and creates the integrated descriptive data based on the output from the learning model in response to the input.

3. Product information processing system, The aforementioned product information processing system is Based on the first correlation data and the survey results data for the sensory product, the expected efficacy data creation unit creates expected efficacy data for the sensory product. The system has a product description creation unit that creates scientific explanatory data or integrated explanatory data for the aforementioned sensory product based on the expected efficacy data for the said sensory product, The aforementioned survey data for the aforementioned sensory product includes electrocardiogram survey data for the said sensory product. The expected efficacy data for the aforementioned sensory product includes electrocardiogram-based efficacy data for the said sensory product. The expected efficacy data creation unit creates the electrocardiogram-based efficacy data based on the first correlation data and the electrocardiogram survey results data. The product description creation unit creates, based on the electrocardiogram-based efficacy data for the said emotional product, the said scientific explanatory data expressed in charts or natural language, or the said integrated explanatory data that includes the content that should be included in the said scientific explanatory data. The electrocardiogram survey results are obtained as indicators of sympathetic nerve activity or autonomic nervous system stress. The electrocardiogram survey results data for the aforementioned sensory product are as follows: Electrocardiogram results obtained by statistically processing the electrocardiograms of individuals who were exposed to the comparison target, using sympathetic nervous system activity indicators or autonomic nervous system stress indicators obtained from the electrocardiograms of those exposed to the comparison target, and This includes the results of an electrocardiogram survey obtained by statistically processing the electrocardiograms of individuals who have come into contact with the aforementioned emotional product, using sympathetic nervous system activity indicators or autonomic nervous system stress indicators obtained from those individuals, for a group of individuals who have come into contact with the emotional product. The first correlation data includes data that represents knowledge or theories obtained from academic research, The first correlation data includes data showing the relationship between the content of events indicated by sympathetic nervous system activity indicators or autonomic nervous system stress indicators for the said emotional product and the expected efficacy of the said emotional product. The expected efficacy data creation unit creates the electrocardiogram-based efficacy data based on the expected efficacy associated in the first correlation data with the difference in the impact of the emotional product on events shown in the sympathetic nerve activity index or autonomic nervous system stress index compared to the comparison target, as shown in the electrocardiogram survey results data for the emotional product. The aforementioned product description creation unit, The expected efficacy data is input into a learning model that is trained using machine learning, and the scientific explanatory data is created based on the output from the learning model corresponding to the input, or, A product information processing system that inputs the expected efficacy data and general descriptive data about the emotional product into a learning model trained by machine learning, and creates the integrated descriptive data based on the output from the learning model in response to the input.

4. A product information processing system according to claim 1, 2, or 3, The product information processing system has a product classification unit that determines the classification to which the emotional product belongs based on one or more of the general description data, the survey results data, or the expected efficacy data for the emotional product, and creates product classification data indicating the classification to which each of the emotional products belongs. The aforementioned survey data for the aforementioned sensory product is sensory survey data, electroencephalogram (EEG) survey data, electrocardiogram (ECG) survey data, or component survey data for the said sensory product. The expected efficacy data for the aforementioned sensory product is sensory-based efficacy data, electroencephalogram-based efficacy data, electrocardiogram-based efficacy data, or ingredient-based efficacy data for the said sensory product. The classification of the aforementioned sensory product is one or more of the following: classification based on the ingredients of the sensory product, classification based on the sensibilities of the person who used the sensory product, classification based on the brainwaves of the person who used the sensory product, classification based on the tone of the sensory product, classification based on the fragrances contained in the sensory product, and classification based on the electrocardiogram of the person who used the sensory product. The aforementioned product classification unit is Based on the characteristic components contained in the aforementioned sensory product, as indicated by the component analysis data or component-based efficacy data for that product, a classification based on the components of the sensory product is performed. The classification of the aforementioned sensory product is based on the sensibilities of the person who used it, using as the criterion the effects that the sensory product is expected to contribute from a sensory perspective, as indicated by the sensory survey results data or sensory-based efficacy data for the aforementioned sensory product. Based on the electroencephalogram (EEG) survey data or EEG-based efficacy data for the aforementioned sensory product, the classification of the sensory product is based on the brainwaves of the person who used the product, using the expected efficacy from an EEG perspective as the criterion for judgment. Based on the general descriptive data for the aforementioned emotional product, the classification of the emotional product is based on the tone of the emotional product, or Based on the general descriptive data for the aforementioned sensory product, should the classification of the sensory product be based on the fragrances it contains, or, A product information processing system that classifies individuals based on their electrocardiograms, using as a criterion the effects that the aforementioned sensory product is expected to contribute to from an electrocardiogram perspective, as indicated by electrocardiogram survey data or electrocardiogram-based efficacy data for the said sensory product.

5. A product information processing system according to Claim 4, The aforementioned product information processing system has a market support unit that uses the consumption trend data for each of the aforementioned emotional products and the product classification data to create sales support data that includes information about the classification to which the emotional products to which high demand is expected belong. The aforementioned Market Support Department, The sales figures for each of the aforementioned emotional products, as shown in the aforementioned consumer trend data, are compiled according to the aforementioned product classification data, and this is used as the second correlation data. A product information processing system that identifies categories with high demand based on sales for each category as indicated by the second correlation data, and includes information indicating the identified categories in the sales support data.

6. A product information processing system according to claim 4, The aforementioned product information processing system has a development support unit that uses the consumption trend data for each of the aforementioned emotional products and the product classification data to create development support data that includes information about the classification to which the emotional products expected to have high demand belong. The aforementioned Development Support Department The sales figures for each of the aforementioned emotional products, as shown in the aforementioned consumer trend data, are compiled according to the aforementioned product classification data, and this is used as the second correlation data. Based on the sales figures for each category shown in the second correlation data, the categories to which the emotional products with high demand and promising sales potential belong are identified, and information indicating the identified categories is included in the development support data. The aforementioned emotional products belonging to the identified classification are identified using the aforementioned product classification data, and information indicating the identified emotional products is included in the aforementioned development support data. The information relating to the aforementioned sensory products is obtained from the aforementioned general descriptive data, the aforementioned survey results data, or the aforementioned expected efficacy data, and the obtained information is included in the aforementioned development support data. A product information processing system in which the information on the sensory product included in the development support data includes information on one or more of the expected efficacy data for the sensory product, the classification to which the sensory product belongs, the fragrances contained in the sensory product, the tone of the sensory product, or the components that characterize the sensory product.

7. A product information processing system according to claim 1, 2, or 3, The product information processing system has a guide providing unit that uses product selection guide data to assist consumers in selecting the emotional products, and outputs information that identifies the emotional product or the category to which the emotional product belongs, or product description data about the emotional product, based on the information input by the consumer. The product description data for the aforementioned sensory product is the scientific explanation data or the integrated explanation data for the said sensory product. The aforementioned product selection guide data is Category selection questionnaire data such that there exists a classification of the sentiment product associated with the answer results to one or more questions included or the selection results to the answer options, Product list data showing a list of the sentiment products included in the classification identified according to the aforementioned response result or selection result, This includes link information indicating a link to the product description data for each of the aforementioned products included in the aforementioned list of products, The guide provision unit displays or outputs data based on the category selection questionnaire data. The guide provisioning unit receives the answer results or selection results for one or more of the questions presented by the category selection questionnaire data, The guide provision unit uses the information contained in the category selection questionnaire data to identify the classification that corresponds to the received response result or selection result, The guide provisioning unit uses the product list data to identify a list of the emotional products included in the identified classification, The guide providing unit displays or outputs a list of the identified sensory products. The guide provision unit receives the selection information for the emotional products included in the list of emotional products, The guide providing unit uses the link information for the emotional product indicated by the selection information to acquire the product description data for the emotional product. The aforementioned guide provision unit is a product information processing system that displays or outputs the acquired product description data.

8. A product information processing system according to claim 7, The aforementioned product information processing system includes a guide creation unit that creates the aforementioned product selection guide data using product classification data. The aforementioned product classification data is data showing the result of each of the aforementioned emotional products being assigned to each of the aforementioned classifications. The guide creation unit creates the category selection questionnaire data by creating at least the questions using the product classification data such that there exists a classification associated with the answer result to one or more questions or the selection result to the answer options. The guide creation unit uses the product classification data to create a list of the emotional products included in each classification, corresponding to the answer results or selection results for one or more of the questions presented by the category selection questionnaire data, as the product list data. The guide creation unit is a product information processing system that creates link information for each of the emotional products included in the created product list data by associating information that identifies the location of the scientific explanatory data or the integrated explanatory data for the emotional product with information that identifies the emotional product.

9. A product information processing system according to claim 1, 2, or 3, The product information processing system includes a product suggestion unit that identifies consumer needs, identifies the classification of the emotional products that meet those needs, selects the emotional products belonging to the identified classification, and displays or outputs information identifying the selected emotional products or product description data for those emotional products. The consumer's needs include information that identifies the expected effects, which are the effects that the consumer expects from the sensory product. The product proposal unit acquires text data, which is either a text describing the effects that the consumer expects from the emotional product, or a text describing the consumer's profile. The aforementioned product proposal department, By applying a natural language analysis method to the acquired text data, the consumer's needs can be identified, or The acquired text data is input into a machine learning model, and the output from the model corresponding to the input is used to identify the consumer's needs. The aforementioned product proposal department, By comparing each of the aforementioned classifications of emotional products with the identified consumer's needs, one can identify the classification that best suits the consumer's needs, or The list of classifications of the aforementioned sensory products and information indicating the needs of the identified consumer are input into a machine learning model, and the output from the machine learning model corresponding to the input is used to identify the classification that matches the consumer's needs. The aforementioned product proposal department selects one or more of the aforementioned emotional products included in the specified classification, The aforementioned product proposal unit is a product information processing system that displays or outputs the scientific descriptive data or the integrated descriptive data relating to one or more selected emotional products as the product description data.

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