WO2017179130A1

WO2017179130A1 - Sensibility database system

Info

Publication number: WO2017179130A1
Application number: PCT/JP2016/061833
Authority: WO
Inventors: 義和東; 邦忠高橋
Original assignee: 義和東; 邦忠高橋
Priority date: 2016-04-12
Filing date: 2016-04-12
Publication date: 2017-10-19

Abstract

Provided is a sensibility database system that: stores sensibility intensity information groups in association with identification information of respective products etc., each sensibility intensity information group being a collection of information indicating sensibility intensity that can be found by a questionnaire regarding a plurality of types of human sensibility images that can be answered in common for products, services, talents, etc., subjected to commercial transactions, said sensibility intensity indicating, for example, whether or not each sensibility image can be perceived; acquires a target product sensibility intensity information group which is a sensibility intensity information group for a target product etc. which is a product etc. of interest; and performs comparison-result output by comparing, product by product, the acquired target product sensibility intensity information group and the stored sensibility intensity information groups for the respective products etc., and outputting a comparison result.

Description

Kansei database system

The present invention relates to a database that holds various senses and images that a person can have in common with objects such as products, services, and talents as information associated with those products.

In the sale of products and the provision of services, advertising for these products and services is extremely important. In particular, selection of a medium for advertising such as television, radio, newspaper, magazine, etc., and selection of talents used for advertising are important for obtaining a high advertising effect.

Therefore, as in the invention disclosed in Patent Document 1, the optimal matching between the characteristics of the product related to the advertisement and the medium that advertises the product is determined and the potential target of the medium related to the advertisement is determined. A technology has been proposed that automatically increases the advertising effectiveness by automatically matching the appeal targets (readers and viewers).

JP 2004-234520 A

As in the case of the above-described invention, a technique for improving the efficiency of advertisement based on the degree of matching of target attributes is becoming obsolete. This is because the consumers are subdivided and refined from a collective existence that can be called a target to an infinite number of subdivided individuals. In such a social situation, it is necessary to develop an advertising strategy based on the individual's sensitivity level, rather than using a method that categorizes consumer groups by attribute.

The number of commercial productions in Japan is 2600 per year, 110,000, and about 1,500 commercials using talent, especially about 1,300 Japanese talents. As such, commercials of Japanese companies depend on talent, and in such a situation, consistency with talented companies, products and services is important. The same is true in relation to media such as magazines and television programs.

However, for example, in talent directory and advertising agency presentations, the differences between the talents are very conceptual and the selection factors in fashion and popularity are strong, and in terms of their role as an original communicator and suitability as a corporate face It can be said that the index used to judge is ambiguous.

Therefore, in order to plan an advanced commercial strategy, it is necessary to collect information at this level and to plan tactics and strategies individually based on the information gathering. The current situation is that advertising agency staff, etc. are still performing trial and error with their supervisors and clients based on their own experiences and feelings. In other words, it is extremely inefficient.

Matching of talents selected according to such aspects and advertisement-related products is not necessarily optimal, and there is a large range of success and failure depending on the ability of the individual, and the suitability of matching is objectively determined It is also difficult to evaluate.

Therefore, the present invention objectively captures an image that a person can have with respect to a product, service, or talent with a common scale, and based on this, a database that can generally evaluate images of various products, services, or talents It is an issue to provide.

In order to solve the above problems, the present invention provides the following sensibility database system. That is, as a first invention, a plurality of types that can be answered in common with respect to commodities, services, graphics, sounds, fragrances, three-dimensional designs, namings, copyrighted works, and talents (hereinafter referred to as “commodities”) that are subject to commercial transactions. Sensitivity intensity information group, which is a collection of information indicating the sensitivity intensity of each person's sensibility image, whether it feels or does not feel for each sensibility image, or the degree of sensation, the degree of feeling Sensitivity intensity information group accumulating unit for accumulating in association with equal identification information, and target product etc. sensitivity intensity information group for acquiring a target product etc. sensitivity intensity information group as a target product etc. A comparison unit that compares the acquisition unit, the acquired target product etc. sensitivity intensity information group, and the stored sensitivity intensity information group of each product, etc. for each product, A comparison result output unit for outputting a compare result, provides a sensibility database system with.

In addition to the above features, a sensitivity database system having a product etc. attribute information holding unit that holds product etc. identification information and product etc. attribute information that is attribute information of the identified product etc. in association with each other is provided. .

Further, in addition to the above features, there is provided a sensitivity database system further including a product etc. attribute information designating unit for designating product etc. attribute information such as a product to be compared among the products etc. stored in the comparison unit.

In addition to the above-described features, a storage data acquisition unit that acquires one or more storage data that is a group of sensitivity intensity information groups such as products associated with product identification information obtained through a questionnaire, and the acquired storage data Provided is a sensitivity database system further including a statistical processing section that performs statistical processing and a sensitivity intensity information group management section that manages a sensitivity intensity information group in association with product identification information according to the result of statistical processing.

In addition to the above features, a perspective calculation rule holding unit that holds a perspective calculation rule that is a rule for calculating whether a sensitivity image is close to or far from each other according to the output comparison result, and a comparison result output unit Sensitivity image proximity product that selects and outputs product identification information such as a product that is closest to the target product etc. based on the comparison result output from and the perspective calculation rule that is held, from the storage unit A sensitivity database system having an equal identification information output unit is provided.

Also, it provides an operation method when the above-described processing is executed by the Kansei database system. Furthermore, an emotion database system operation program is described in which these processes are described in a sense database system as a computer so as to be read and executed.

According to the present invention having the above-described configuration, it is possible to objectively grasp an image that a person can have with respect to a product, service, or talent, and to evaluate various products, services, or talent images on a general basis. Databases can be provided.

Conceptual diagram showing a case of selecting a product and a talent to be used for the advertisement using the sensitivity database system of the first embodiment Conceptual diagram showing the acquisition of the target product etc. sensitivity intensity information group, which is the sensitivity intensity information group for the product “Wine Soda Sao Paulo Red” illustrated in FIG. The figure which shows the aspect of the various comparisons performed in a comparison part The figure which shows an example of the functional block in the Kansei database system of Embodiment 1. Example of hardware configuration of Kansei database system of embodiment 1 An example of the flow of processing in the Kansei database system of the embodiment Example of functional block in the Kansei database system of the embodiment Conceptual diagram illustrating product attribute information Conceptual diagram showing an example of a display screen for accepting inputs such as target products Example of functional block of Kansei database system of embodiment 2 The figure for demonstrating composition, such as a data acquisition part for accumulation Example of functional block of Kansei database system of Embodiment 3 The figure which shows an example of the output by identification information output parts, such as a sensitivity image proximity goods

Hereinafter, embodiments of the present invention will be described with reference to the drawings. Note that the present invention is not limited to these embodiments, and can be implemented in various modes without departing from the spirit of the present invention.

The first embodiment will mainly describe claims 1, 6, and 7. In the second embodiment, claims 2 and 3 will be mainly described. In the third embodiment, claim 4 will be mainly described. In the fourth embodiment, claim 5 will be mainly described.
<Embodiment 1>
<Overview>

FIG. 1 is a conceptual diagram showing a case where a product to be advertised and a talent to be used for the advertisement of this product are selected using the sensitivity database system of the present embodiment. Here, it is a feature of the present invention that information relating to the sensibility image represented by a common evaluation item is accumulated for both products and talents.

As shown in the figure, the database that composes this system includes various sensibility images such as “freshness”, “brightness”, “youth”, “transparency”, and “coolness” for each of multiple talents. Information individually evaluated as to whether or not to perform is stored in association with information for identifying individual talents. These pieces of information are obtained by conducting a predetermined questionnaire.

For example, for a product “wine soda (alcoholic drink)” that is the object of advertising, a questionnaire having the same evaluation items is used to obtain information (file = 01625.data) about each sensitivity image for the product. , Acquired in association with information for identifying the product (ID 01625).

Then, the information about the sensibility image acquired for the product and the information about the sensibility image stored for each talent are respectively compared, and the comparison result is calculated, for example, in the form of “degree of matching”. As a result, for example, if the match level of the talent A is 75%, the talent A has the sensitivity image closest to that of the target product. I understand that.
<Configuration>

FIG. 4 is a diagram showing an example of functional blocks in the sensitivity database system of the present embodiment. Note that functional blocks of the apparatus and system described below can be realized as a combination of hardware and software. Specifically, if a computer is used, a CPU, a main memory, a bus, or a secondary storage device (a hard disk, a non-volatile memory, a storage medium such as a CD or a DVD, a read drive for the medium, etc.), information Input devices used for input, printing equipment, display devices, other hardware components such as external peripheral devices, interfaces for external peripheral devices, communication interfaces, driver programs for controlling these hardware, Other examples include application programs and user interface applications. Then, by CPU processing according to the program developed on the main memory, the data input from the input device or other interface and held in the memory or hard disk is processed and stored, An instruction for controlling the software is generated. Alternatively, the functional blocks of this system may be realized by dedicated hardware.

Each embodiment described in the present specification can be realized not only as an apparatus but also as a method. Moreover, a part of such an apparatus can be configured as software. Furthermore, a software product used to cause a computer to execute such software and a recording medium on which the product is fixed are naturally included in the technical scope of each embodiment described in this specification (this specification The same throughout.)

As shown in FIG. 4, the “Kansei database system” (0400) of this embodiment includes a “Kansei intensity information group storage unit” (0401) and a “Target product etc. KANSEI intensity information group acquisition unit” (0402). And a “comparison unit” (0403) and a “comparison result output unit” (0404).

The “Kansei Intensity Information Group Accumulation Unit” (0401) answers in common for products, services, graphics, sounds, fragrances, three-dimensional designs, naming, copyrighted works, and talents (hereinafter referred to as “products, etc.”) that are subject to commercial transactions. Sensitivity intensity, which is a collection of information that indicates whether or not each sensibility image feels or does not feel, or the degree of sensibility that is not felt It has a function of storing information groups in association with product etc. identification information.

Products may include products, services, graphics, sounds, scents, 3D designs, naming, copyrighted works, talents, and related items. For example, merchandise includes advertising media such as magazines and books, and services include advertising media such as television broadcasting and radio broadcasting. Talents also include entertainers, entertainers, TV presenters, cultural figures, politicians, critics, athletes and various professionals. Also, it may include commercials for products being broadcast. This is because merchandise commercials may remind people of the sensibility image, and there are cases where a talent suitable for the sensibility image of the commercial itself is selected and used.

As illustrated in FIG. 1, the sensibility image is a sensation that a person can recall with respect to an object, such as “freshness”, “brightness”, “youth”, “transparency”, “coolness”, etc. Or impression.

感 Kansei image that can be answered in common means a sensibility image that people can generally recall for any product. Whether or not it can be recalled may differ depending on the respondent's generation, the region where he / she resides, and the like, and may also differ depending on the era of obtaining answers. Therefore, there is no intention to make a strict definition. However, for example, the following can be mentioned. "Refreshing" "Brightness" "Youth" "Transparency" "Coolness" "Heat" "Energetic" "Motivation" "Quietness" "Fresh" "Oldness" "Imature" "Success" "Victory" "Peace" "Noble" "Elegant" "Brightness" "Sudden" "Lightness" "Agility" "Large" "Majesty" "Profoundness" "Motherhood" "Fatherhood" "Dignity" "Strictness" "Warmness" Warmth, gentleness, severity, sense of accomplishment, satisfaction, cleanliness, maturity, wandering, staying, storm, rain, cloudy, clear, evolution, progress “Green” “Eco” “Recycle” “Sharp” “Mild” “Big” “Infinite” “Eternal” “Peace” “Safe” “Safe” “Rich” “High Speed” “Carefully Selected” “Cute” “Fashion” “Cool” “Strong” and “soft” can be given. Therefore, if the sensitivity image is deliberately defined, it cannot be specifically determined such as, but this is an image that can convey the atmosphere to each other based on human sensitivity, It can be said that anyone can use it and express it in words. Thus, for example, numbers, colors defined using three primary colors, altitude, speed, acceleration, hardness, temperature, volume, length, age, gender, hobby, hometown, occupation, educational background, address, commuting The subway line used, vehicle type, annual income, family structure, types of household appliances used, manufacturer name, etc. are not sensitive images.

As shown in FIG. 1, the sensitivity intensity is indicated by whether or not it feels like “Yes”, “No”, “Neither”, or the like. Moreover, you may show on the basis of the prescribed perfect score.

With respect to the talent A illustrated in FIG. 1, for each sensitivity image such as “freshness”, “brightness”, “youth”, “transparency”, “coolness”, etc., “Yes”, “No”, “ Each sensitivity intensity such as “I can't say either” is shown. A group of these sensitivity intensities is called a sensitivity intensity information group.

By conducting a questionnaire for a plurality of people, it is possible to obtain, for example, sensitivity intensity information groups by a plurality of respondents for talent A. In this case, the plurality of sensitivity intensity information groups can be combined into a sensitivity intensity information group for the talent A. A mode of integrating a plurality of sensitivity intensity information groups will be described in a target product etc. sensitivity intensity information group acquisition unit described later.

The sensitivity intensity information group storage unit stores such sensitivity intensity information groups in association with product identification information that is information for identifying the talent A.

The “target product etc. sensitivity intensity information group acquisition unit” (0402) has a function of acquiring a target product etc. sensitivity intensity information group which is a sensitivity intensity information group of the target product etc. that is the target product etc.

FIG. 2 is a conceptual diagram illustrating the acquisition of the sensitivity information group for the target product, which is the sensitivity intensity information group for the product “Wine Soda Sao Paulo Red” illustrated in FIG. As shown in the figure, the questionnaire (Kansei Image Questionnaire) to acquire is related to Kansei images such as “Refreshing”, “Brightness”, “Youth”, “Transparency”, “Coolness”, etc. It is configured to ask for an answer about whether or not you feel the image.

By obtaining such responses to multiple sensibility image questionnaires (for example, housewife A, company employee B, university student C, etc.), obtain a group of sensibility information on the target product for each respondent. can do. Then, a plurality of target product isosensitive intensity information groups acquired from all respondents are integrated. As a method of combining, for example, it may be integrated by majority decision, or may be integrated by averaging.

When integrating the sensitivity information group for each target product such as housewife A, company employee B, and university student C by majority vote, as shown in the figure, “freshness” refers to company employee B and university student C among the three. Answer “None”, the “intensity” sensitivity intensity of “freshness” constituting the integrated target product etc. sensitivity intensity information group is “none”. By similarly voting for other sensibility images as well, as shown in the figure, one target product etc. sensitivity intensity information group can be obtained.

In addition, a degree is assigned to each sensitivity intensity, such as “Yes” is 100%, “None” is 0%, and “Neither” is 50%, and it is obtained from all respondents for each sensitivity image. By averaging the sensibility intensities, one sensibility information group such as a target product may be obtained as illustrated.

The sensitivity intensity information group accumulated for each talent shown in FIG. 1 is also obtained by integrating the sensitivity intensity information groups obtained from a plurality of respondents in the same manner as one sensitivity intensity information group. is there.

The “comparison unit” (0403) has a function of comparing the acquired target product etc. sensitivity intensity information group and the stored sensitivity intensity information group of each product etc. for each product etc. The comparison can be done in various ways. FIG. 3 is a diagram illustrating various comparison modes performed in the comparison unit. As shown in the figure, for example, the comparison can be made based on the number of matches. A comparison is made by calculating the number of matching sensitivity intensities for each sensitivity image in the sensitivity intensity information group of the target product and the like for each product. Further, the comparison may be made based on the difference between the average values of the sensibility intensities, the difference between the weighted averages, and the degree of coincidence between the sensibility intensities.

Sensitivity intensity information group of target product etc. and the accumulated sensitivity intensity information group of each product etc. are two-dimensional vector data composed of multiple sensitivity images and multiple sensitivity intensities. A comparison may be made based on the distance. As the distance in the vector space, for example, the Euclidean distance can be used.

The “comparison result output unit” (0404) has a function of outputting a comparison result. The mode of output is not limited, but it is preferable to output graphically, for example, by adding not only numerical values and characters but also diagrams.
<Hardware>

FIG. 5 is a schematic diagram showing an example of a configuration in the sensibility database system when the above functional components are realized as hardware. The operation of each hardware configuration will be described using this figure.

As shown in the figure, the Kansei database system reads “CPU” (0501) for executing various arithmetic processes and a program for performing various arithmetic processes for execution by the CPU and provides a work area for the program. Main memory "(0502). With these configurations, a target product etc. sensitivity intensity information group acquisition unit, a comparison unit, and a comparison result output unit are implemented. In addition, a storage device such as “HDD” (0503) for holding various programs such as the acquired sensitivity intensity information group, the acquired target product sensitivity level information group, the target product sensitivity level information group acquisition program, and the like, A “communication circuit” (0504) for acquiring a target product etc. sensitivity intensity information group via a network and a “display device” (0505) for displaying a comparison result are provided. Then, they are connected to each other by a data communication path such as “system bus” (0506) to transmit / receive information and process information.

First, the CPU expands the target product isosensitivity intensity information group acquisition program held in the HDD into the work area of the main memory, and executes the program to execute the target product isosensitivity intensity information group via the communication circuit. get. In addition, you may acquire the sensitivity information intensity | strength information group of object goods via an input interface. Then, the acquired target product etc. sensitivity intensity information group is stored in a predetermined storage area of the main memory.

Then, the CPU executes the comparison program and reads the product etc. sensitivity intensity information group held. Further, the stored target product iso-sensitivity intensity information group is read and compared with the product iso-sensitivity intensity information group. Such a comparison process is repeated for each stored product. Then, the result of the comparison process is stored in a predetermined storage area. Subsequently, the CPU reads the comparison result output program and outputs the comparison result to a display device or the like.

The sensitivity intensity storage unit embodied by the storage device does not need to be integrally included in the sensitivity database system, and the sensitivity intensity storage unit is configured as an independent database, the target product etc. sensitivity intensity information group acquisition unit, The comparison unit and the comparison result output unit may be configured to be accessible via a network or the like.
<Process flow>

FIG. 6 is a flowchart showing an example of a process flow in the sensitivity database system of the present embodiment. The steps shown below may be steps executed by each hardware configuration of the computer as described above, or may be processing steps that constitute a program for controlling the computer recorded on a medium. It does not matter (the same applies to Embodiment 2 and later described later).

As shown in the drawing, first, a target product etc. sensitivity intensity information group, which is a sensitivity intensity information group of the target product, is acquired (target product etc. sensitivity intensity information group acquisition step: S0601). The acquired target product etc. sensitivity intensity information group is compared with the stored sensitivity intensity information group of each product etc. (comparison step: S0602). Then, the comparison result is output (comparison result output step: S0603).
<Effect>

According to the sensitivity database system of the present embodiment, products and products (products include advertising media such as magazines and books), products and services (services include advertising media such as television broadcasting and radio broadcasting), services and services , Product and talent (including talent, entertainers, entertainers, TV presenters, cultural figures, politicians, critics, athletes, specialists, etc.), service and talent, talent and talent, etc. Since the degree can be known, it is possible to produce an effective advertisement based on the degree of match. Further, when the result is that the degree of coincidence of the talent's sensitivity image is low for a product group with strong advertising demand, it can be used in the direction of nurturing talent having such a sensitivity image.
<Embodiment 2>
<Overview>

The Kansei database of the present embodiment is based on the first embodiment, and is characterized in that the product identification information and the attribute information of the identified product are associated and stored. Thereby, identification, management, etc. of the product etc. sensitivity intensity information group can be performed based on the attribute information of the product etc.
<Configuration>

FIG. 7 is a diagram showing an example of functional blocks in the sensitivity database system of the present embodiment. As shown in the figure, the “Kansei database system” (0700) of this embodiment includes a “Kansei intensity information group storage unit” (0701), a “Target product etc. KANSEI intensity information group acquisition unit” (0702), Part "(0703)," comparison result output part "(0704)," product etc. attribute information holding part "(0705), and" product etc. attribute information designation part "(0706). The product etc. attribute designation unit is not necessarily an essential configuration in the sensitivity database of this embodiment.

The sensitivity intensity information group storage unit, the target product etc. sensitivity intensity information group acquisition unit, the comparison unit, and the comparison result output unit are functionally similar to the configuration of the same name in the first embodiment. Therefore, the product etc. attribute information holding unit and the product etc. attribute information specifying unit will be described below.

The “product etc. attribute information holding unit” (0705) has a function of associating and holding product etc. identification information and product etc. attribute information which is attribute information of the identified product etc.

FIG. 8 is a conceptual diagram illustrating product etc. attribute information held in the product etc. attribute information holding unit. As shown in the figure, products and the like are classified and identified in a hierarchical manner. For example, “product”, “service”, and “talent” are set as attributes in the uppermost layer, and “A”, “B”, and “C” are assigned as information for identifying each.

For the product, “Food & Drink”, “Clothing”, “Furniture” and the like are provided as subordinate attributes, and “01”, “02”, and “03” are assigned as information for identifying each item. . Further, an attribute subordinate to each attribute is further provided.

The product etc. attribute information can be constituted by serially identifying information of each attribute from the top layer to the bottom layer, for example. For example, the product attribute information of the product “red wine” is “A0110WR”.

In this way, by identifying and managing the product etc. identification information and the product etc. attribute information, the identification and management of the product etc. sensitivity intensity information group that is also associated with the product etc. identification information is based on the product etc. attribute information. It can be done and is effective.

“Product etc. attribute information designating section” (0706) has a function of designating product etc. attribute information such as products to be compared among the products etc. stored in the comparison section. As a result, the comparison target can be limited and the target product iso-sensitivity intensity information group can be compared with the stored product iso-sensitivity intensity information group, and an efficient comparison can be performed.

FIG. 9 is a conceptual diagram showing an example of a display screen for accepting input of a target product or the like when performing comparison by the comparison unit. The display screen is configured to be able to accept input of information such as product attribute information, target product sensitivity information group, and target product identification information.

When “Wine Soda Sao Paulo Red” illustrated in FIG. 1 and the like is a target product, etc., “ID 01625” is input as target product etc. identification information, and the target product etc. sensitivity intensity associated with this target product etc. identification information An information group “file = 01625.data” is also input. Then, “A0110WR” is input as product etc. attribute information. By inputting the product etc. attribute information, the comparison unit can identify and compare the product having the attribute identified by “A0110WR” as the product to be compared.

The target product etc. sensitivity intensity information group acquisition unit may be configured to acquire target product etc. attribute information that is attribute information of the target product etc. in association with identification information of the target product etc.
<Hardware>

The sensitivity database system of the present embodiment can be configured according to the hardware configuration related to the sensitivity database system of the first embodiment. For example, the storage device may be configured to hold product etc. attribute information, and the CPU may execute a product etc. attribute information designation program for designating product etc. attribute information such as products to be compared. .
<Process flow>

The flow of processing in the Kansei database system of the present embodiment includes, for example, a step for specifying product etc. attribute information such as products to be compared, in accordance with the Kansei database system of the first embodiment, The processing may be performed on a product having attribute information on the product or the like designated.
<Effect>

With the sensitivity database of this embodiment, identification and management of a product etc. sensitivity intensity information group can be performed based on product etc. attribute information.
<Embodiment 3>
<Overview>

The Kansei database system of this embodiment is based on

Embodiment

1 or 2, and statistically processes the Kansei intensity information group obtained by the questionnaire, and manages the Kansei intensity information group according to the processing result. .
<Configuration>

FIG. 10 is a diagram showing an example of functional blocks in the sensitivity database system of the present embodiment. As shown in the figure, the “Kansei database system” (1000) of this embodiment includes a “Kansei intensity information group storage unit” (1001), a “Target product etc. KANSEI intensity information group acquisition unit” (1002), Part "(1003)," comparison result output part "(1004)," accumulation data acquisition part "(1005)," statistical processing part "(1006), and" sensitivity intensity information group management part "(1007) ).

The sensitivity intensity information group storage unit, the target product etc. sensitivity intensity information group acquisition unit, the comparison unit, and the comparison result output unit are functionally similar to the configuration of the same name in the first or second embodiment. Therefore, hereinafter, the accumulation data acquisition unit, the statistical processing unit, and the sensitivity intensity information group management unit will be described with reference to FIG.

The “accumulation data acquisition unit” (1005) has a function of acquiring one or more accumulation data, which is a group of sensibility intensity information of products etc. associated with product etc. identification information obtained by a questionnaire. As shown in the figure, it is possible to obtain a group of sensitivity intensity information about “Wine Soda Sao Paulo Red” by a plurality of respondents through a questionnaire. These sensitivity intensity information groups are acquired as accumulation data. The questionnaire may be configured to input a paper questionnaire description from the terminal, or the questionnaire itself may be performed on the Internet. In addition, information collection by the Internet crawler can be included in a broad questionnaire in the present invention. For example, crawling user evaluation fields on web of specific products, blog articles, bulletin board postings, articles on SNS, etc., "fresh", "bright", "young", "transparency", "cool" and these You may comprise so that it may be set as the intensity | strength of each sensitivity image item by seeing the appearance frequency of the word understood as a synonym.

The “statistic processing unit” (1006) has a function of statistically processing the acquired accumulation data. For example, for each of the Kansei images that make up the Kansei intensity information group, order them in order of strong or weak Kansei intensity, or extract a Kansei image with a high level of Kansei intensity among multiple Kansei images. For example, the tendency of the sensibility intensity is extracted. For example, if there is a product called “Wine Soda Sao Paulo Red”, this “Wine Soda Sao Paulo Red” (identification information ID: 01625) is obtained by conducting a sensitivity image questionnaire for a total of 200 people and obtaining an average value for each sensitivity image. A social evaluation of the sensibility image can be obtained. For example, when this is a new product, the information is added to the sensitivity database system. In addition, by adding the sensitivity image data of “Wine Soda Sao Paulo Red” (identification information ID: 01625) to the products “Food & Drink” and “Alcoholic Drink” and “Wine” and “Red” By statistically processing the products to be identified, it is also possible to obtain an average of the social sensibility image of this type of beverage. In this case, by adding new product data, the average sensitivity image of this type of beverage is changed. It is also possible to compare the average value of the social sensitivity image of this type of beverage with the evaluation value of the sensitivity image of “Wine Soda Sao Paulo Red” (identification information ID: 01625).

The “Kansei intensity information group management unit” (1007) has a function of managing a KANSEI intensity information group in association with product etc. identification information according to the result of statistical processing. For example, if a certain tendency is recognized for the sensitivity intensity of each sensitivity image from the results of statistical processing, the sensitivity intensity information group that is significantly different from the tendency may be a sensitivity intensity information group based on random answers. For example, it is deleted from the storage data.
<Hardware>

The Kansei database system of this embodiment can be configured according to the hardware configuration related to the Kansei database system of

Embodiment

1 or 2. For example, the CPU is configured to execute a storage data acquisition program, a statistical processing program, a sensitivity intensity information group management program, and the like to be held.
<Process flow>

The flow of processing in the sensitivity database system of the present embodiment is the same as the sensitivity database system of the first or second embodiment, for example, an accumulation of sensitivity intensity information groups such as products associated with product identification information obtained by a questionnaire. A step of acquiring one or more business data, a step of statistically processing the acquired storage data, and a step of managing a sensitivity intensity information group in association with product etc. identification information according to the result of the statistical processing.
<Effect>

From the sensitivity database system of the present embodiment, it is possible to statistically process the sensitivity intensity information group obtained by the questionnaire and manage the sensitivity intensity information group according to the processing result.
<Embodiment 4>
<Overview>

The Kansei database system of the present embodiment is based on any one of the first to third embodiments, and products that are close in sensitivity image to the target product etc. by calculating whether the sensitivity images are close or far from each other according to the comparison result. It is characterized in that the product etc. identification information is output.
<Configuration>

FIG. 12 is a diagram showing an example of functional blocks in the sensitivity database system of the present embodiment. As shown in the figure, the “Kansei database system” (1200) of the present embodiment includes a “Kansei intensity information group storage unit” (1201), a “Target product etc. KANSEI intensity information group acquisition unit” (1202), Part "(1203)," comparison result output part "(1204)," perspective calculation rule holding part "(1205), and" sensitivity image proximity product etc. identification information output part "(1206).

The sensitivity intensity information group accumulation unit, the target product etc. sensitivity intensity information group acquisition unit, the comparison unit, and the comparison result output unit are functionally similar to the configuration of the same name in the first embodiment. Therefore, hereinafter, the perspective calculation rule holding unit and the sensitivity image proximity product etc. identification information output unit will be described.

The “perspective calculation rule holding unit” (1205) has a function of holding a perspective calculation rule that is a rule for calculating whether a sensitivity image is close to or far from each other according to the output comparison result.

The perspective calculation rule is, for example, a rule that is calculated based on the number of emotional images with which the emotional intensity matches, or the sensitivity intensity information group is treated as vector data and is based on a distance in the vector space (for example, Euclidean distance). It may be a rule to calculate.

The “sensitivity image proximity product etc. identification information output unit” (1206) is considered to be closest to the target product etc. based on the comparison result output from the comparison result output unit and the stored perspective calculation rules. It has a function of selecting and outputting product etc. identifying information such as products from the sensitivity intensity information group storage unit. Alternatively, it may be a person who has a function of selecting and outputting product etc. identification information such as a product close to a certain range with a sensitivity image from the sensitivity intensity information group storage unit. For example, a proximity product, such as the best 10. Conversely, it may be configured to output the farthest relationship.

FIG. 13 is a diagram showing an example of output by the identification information output unit for the sensitivity image proximity product etc. As shown in the figure, the talent A with a matching degree of “75%” is displayed as the closest talent image, and the talent B and the talent C below the next point are also shown with the matching degree. As a result, it is possible to specify the closest product in the sensitivity image with the target product.

The Kansei database system of this embodiment can be configured according to the hardware configuration related to the Kansei database system such as Embodiment 1. The processing flow in the sensitivity database system of the present embodiment is performed in accordance with the sensitivity database system of the first embodiment.
<Effect>

From the sensibility database system of the present embodiment, it is possible to specify the closest product in the sensibility image with the target product.

0400 Sensitivity database system 0401 Sensitivity intensity information group storage unit 0402 Target product etc. sensitivity intensity information group acquisition unit 0403 Comparison unit 0404 Comparison result output unit

Claims

Questionnaire survey on sensitivity images of multiple types of people who can answer in common about products, services, graphics, sounds, fragrances, three-dimensional design, naming, copyrighted works, and talents (hereinafter referred to as “products, etc.”) Sensitivity that accumulates Kansei Strength Information Group, which is a collection of information indicating Sensitivity Intensity that is or does not feel, or feels, for each Kansei Image obtained by performing in association with product identification information An intensity information group storage unit;
A target product etc. sensitivity intensity information group acquisition unit that acquires a target product etc. sensitivity intensity information group that is a target product etc. sensitivity intensity information group,
A comparison unit that compares the acquired sensitivity sensitivity information group of target products and the sensitivity sensitivity information group of each stored product for each product, etc.
A comparison result output unit for outputting a comparison result;
A sensibility database system.
The sensitivity database system according to claim 1, further comprising: a product etc. attribute information holding unit that holds the product etc. identification information and the product etc. attribute information that is attribute information of the identified product etc. in association with each other.
3. The sensibility database system according to claim 2, further comprising a product etc. attribute information designation unit for designating product etc. attribute information such as a product to be compared among the products etc. stored in the comparison unit.
A storage data acquisition unit that acquires one or more storage data that is a group of sensitivity intensity information such as products associated with product identification information obtained by a questionnaire;
A statistical processing unit for statistically processing the acquired data for storage;
Sensitivity intensity information group management unit for managing (adding, changing, deleting) the sensitivity intensity information group in association with product identification information according to the result of statistical processing,
The sensitivity database system according to any one of claims 1 to 3, further comprising:
A perspective calculation rule holding unit for holding a perspective calculation rule that is a rule for calculating whether the sensitivity image is close or far from each other according to the output comparison result;
Based on the comparison result output from the comparison result output unit and the stored perspective calculation rules, select product etc. identification information such as the product that is closest to the target product etc. from the sensitivity intensity information group storage unit The sensitivity database system according to any one of claims 1 to 4, further comprising an identification information output unit such as a sensitivity image proximity product to be output.
Questionnaire survey on sensitivity images of multiple types of people who can answer in common about products, services, graphics, sounds, fragrances, three-dimensional design, naming, copyrighted works, and talents (hereinafter referred to as “products, etc.”) Sensitivity that accumulates in association with product identification information etc. Kansei intensity information group that is a collection of information indicating sensitivity intensity that is or does not feel for each sensitivity image obtained by A method of operating a sensitivity database system having an intensity storage unit,
A target product etc. sensitivity intensity information group acquisition step for acquiring a target product etc. sensitivity intensity information group that is a sensitivity product information group of the target product etc. that is a target product, etc.,
A comparison step of comparing the acquired sensitivity evaluation information group of the target product and the sensitivity sensitivity information group of each stored product, etc .;
A comparison result output step for outputting a comparison result; and
Method of operating a sensitivity database system, which is a computer having
Questionnaire survey on sensitivity images of multiple types of people who can answer in common about products, services, graphics, sounds, fragrances, three-dimensional design, naming, copyrighted works, and talents (hereinafter referred to as “products, etc.”) Sensitivity that accumulates in association with product identification information etc. Kansei intensity information group that is a collection of information indicating sensitivity intensity that is or does not feel for each sensitivity image obtained by An operational program for a sensitivity database system having a strength storage unit,
A target product etc. sensitivity intensity information group acquisition step for acquiring a target product etc. sensitivity intensity information group that is a sensitivity product information group of the target product etc. that is a target product, etc.,
A comparison step of comparing the acquired sensitivity evaluation information group of the target product and the sensitivity sensitivity information group of each stored product, etc .;
A comparison result output step for outputting a comparison result; and
Kansei database system operation program which is described in the Kansei database system as a computer so that it can be read and executed.