WO2023182437A1 - Système d'évaluation de produit, serveur de gestion, terminal utilisateur et programme - Google Patents

Système d'évaluation de produit, serveur de gestion, terminal utilisateur et programme Download PDF

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Publication number
WO2023182437A1
WO2023182437A1 PCT/JP2023/011553 JP2023011553W WO2023182437A1 WO 2023182437 A1 WO2023182437 A1 WO 2023182437A1 JP 2023011553 W JP2023011553 W JP 2023011553W WO 2023182437 A1 WO2023182437 A1 WO 2023182437A1
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evaluation
product
value
management server
user terminal
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PCT/JP2023/011553
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English (en)
Japanese (ja)
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健一 中西
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株式会社彩いろり
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0203—Market surveys; Market polls

Definitions

  • Patent Document 1 Japanese Patent Publication No. 2016-503914
  • the bulletin states ⁇ For example, before purchasing a product, you may want to research whether other people have given it good reviews.
  • the seller can assign a score to each of the various aspects of the transaction (e.g. quality of product, attitude of the seller, speed of delivery, etc.), with pre-configured scores corresponding to each specific range of scores. Evaluation parameter values ("positive”, "neutral”, or "negative") are determined for aspects of the transaction.
  • the purchaser may provide a text-based rating description of the product. ” (see paragraphs 0004-0005).
  • Patent Document 1 describes that a purchaser evaluates the quality of a product.
  • product quality is often evaluated based on absolute evaluation.
  • the absolute evaluation referred to here is an evaluation made using absolute values on a 5-level or 10-level scale. This absolute evaluation is effective for highly functional products such as home appliances, where there are “correct answers” for many of the product's features and functions.
  • This absolute evaluation may not be effective for highly palatable products such as alcoholic beverages, where there is no "correct answer” for many of the product's features and functions. Therefore, the present disclosure provides a mechanism that enables the use of relative evaluation of products.
  • the present application includes multiple means for solving the above problems, and one example is a product evaluation system in which a management server calculates evaluation values that relatively represent evaluations of a plurality of products.
  • the present invention is characterized by comprising a management means for managing the products, and a transmission means for transmitting the evaluation values of the plurality of products to a user terminal, and the user terminal includes a display means for displaying the evaluation values of the plurality of products.
  • FIG. 1 shows an example of variables used in a relative evaluation scheme.
  • FIG. 2 shows an example of a normal distribution of user evaluation results.
  • FIG. 3 is a diagram illustrating differences in analysis methods of evaluation schemes.
  • FIG. 4 shows an example of the configuration of a product evaluation system 400.
  • FIG. 5 shows an example of the hardware configuration of the management server 401.
  • FIG. 6 shows an example of the hardware configuration of the user terminal 402.
  • FIG. 7 shows an example of a user master table 700.
  • FIG. 8 shows an example of a brand master table 800.
  • FIG. 9 shows an example of a posting table 900.
  • FIG. 10 shows an example of a posted issue table 1000.
  • FIG. 11 shows an example of a post evaluation table 1100.
  • FIG. 12 shows an example of a tasting scoring table 1200.
  • FIG. 13 shows an example of a brand feature table 1300.
  • FIG. 14 shows an example of a preference model table 1400.
  • FIG. 15 shows an example of a preference estimation table 1500.
  • FIG. 16 shows an example of a comprehensive evaluation screen generation flow 1600.
  • FIG. 17 shows an example of an element evaluation screen generation flow 1700.
  • FIG. 18 shows an example of a generation flow 1800 of the first preference understanding screen.
  • FIG. 19 shows an example of a generation flow 1900 of the second preference understanding screen.
  • FIG. 20 shows an example of a generation flow 2000 of the first taste difference understanding screen.
  • FIG. 21 shows an example of a generation flow 2100 of the second taste difference understanding screen.
  • FIG. 22 shows an example of a tasting evaluation sequence 2200.
  • FIG. 23 shows an example of a subjective quantitative value correction flow 2300.
  • FIG. 24 shows an example of a preference model generation flow 2400.
  • FIG. 25 shows an example of a first product extraction flow 2500.
  • FIG. 26 shows an example of a second product extraction flow 2600.
  • FIG. 27 shows an example of a comprehensive evaluation screen 2700.
  • FIG. 28 shows an example of a comprehensive evaluation screen 2800.
  • FIG. 29 shows an example of an element evaluation screen 2900.
  • FIG. 30 shows an example of an element evaluation screen 3000.
  • FIG. 31 shows an example of an element evaluation screen 3100.
  • FIG. 32 shows an example of the first preference understanding screen 3200.
  • FIG. 33 shows an example of the first preference understanding screen 3300.
  • FIG. 34 shows an example of the first preference understanding screen 3400.
  • FIG. 35 shows an example of a first preference understanding screen 3500.
  • FIG. 36 shows an example of a second preference understanding screen 3600.
  • FIG. 37 shows an example of the first taste difference understanding screen 3700.
  • FIG. 38 shows an example of the first taste difference understanding screen 3800.
  • FIG. 39 shows an example of the second taste difference understanding screen 3900.
  • FIG. 40 shows an example of a score specification screen 4000.
  • FIG. 41 shows an example of an answer screen 4100.
  • FIG. 42 shows an example of an element correspondence table 4200.
  • FIG. 43 shows an example of an element correspondence table 4300.
  • FIG. 44 shows an example of an element correspondence table 4400.
  • FIG. 45 shows an example of an answer screen 4500.
  • FIG. 46 shows an example of a score specification screen 4600.
  • FIG. 47 shows an example of an input screen 4700.
  • FIG. 48 shows an example of an input screen 4800.
  • FIG. 49 shows an example of an input screen 4900.
  • FIG. 50 shows an example of a generation flow 5000 of a comprehensive evaluation screen.
  • FIG. 51 shows an example of an element evaluation screen generation flow 5100.
  • FIG. 52 shows an example of an element evaluation screen 5200.
  • FIG. 53 shows an example of a comprehensive evaluation screen 5300.
  • Example 1-1 Summary of Example This example provides a relative evaluation scheme for products. Table 1 shows the differences between this relative evaluation scheme and the conventional evaluation scheme.
  • the product can be recommended based on other people's evaluation records and purchase records.
  • the relative evaluation scheme according to this embodiment does not make the characteristics of the products that the user prefers clear one by one, but only records the overall evaluation of the products, thereby understanding the user's preferences. be able to.
  • FIG. 1 shows examples of variables used in the present relative evaluation scheme.
  • the comprehensive evaluation 101 is a variable indicating the user's perceived quality of the product, or whether he likes or dislikes the product. This comprehensive evaluation 101 is evaluated differently depending on the user. Note that the quality of a product means whether it is delicious or not delicious if the product to be evaluated is an alcoholic beverage.
  • the preference model 102 is an estimation model that shows the relationship between the comprehensive evaluation 101, which is an objective variable, and the product characteristics 103, which is an explanatory variable. This preference model 102 is expressed, for example, by the following equation.
  • the product characteristic 103 is a value representing a feature or function that the product has.
  • the product characteristics 103 are classified into three types: qualitative values 104, subjective quantitative values 105, and objective quantitative values 106, depending on the characteristics.
  • the subjective quantitative value 105 is the subject of element evaluation to be described later.
  • This subjective quantitative value 105 may include a value indicating the degree of suitability of the product to the usage scene.
  • the subjective quantitative value 105 may include a value indicating the degree of suitability for celebrations or the degree of suitability for white fish sashimi.
  • this subjective quantitative value 105 may include a value indicating the degree of suitability for a specific food. Note that "food” here includes ingredients and dishes.
  • the above-mentioned comprehensive evaluation 101 may be liked or disliked depending on the user, but the subjective quantitative value 105 is basically treated as an element for which there is a correct answer (in other words, the average value calculated from a large number of evaluation values is a meaningful element). Therefore, when extracting the preference model 102, the weighted average value of other users may be applied, or if the target user has recorded a necessary and sufficient amount of element evaluations, the user's own evaluation value may be applied. May be applied. In addition, even if it is a subjective quantitative value 105, if it has become possible to measure it as an objective quantitative value 106 with the advancement of technology, the objective quantitative value 106 may be applied preferentially.
  • FIG. 2 shows an example of a normal distribution of user evaluation results. Assuming that users' evaluation results follow a normal distribution, in a 5-level evaluation, 38% of products will have the same score as the other 38% of products, and 48% of products will have the same score as the other 48% of products. (See Figure 2(a)). At this time, it is not possible to judge the superiority or inferiority of products within the same rating. On the other hand, in the 10-point evaluation, 38% of products have the same score as the other 19% of products, and 30% of the products have the same score as the other 15% of products (see FIG. 2(b)). Again, it is not possible to judge the superiority or inferiority of a product within the same rating.
  • the method of assigning scores is to specify an arbitrary location within a certain range, and assign values with unlimited levels. By enlarging and displaying a specific range within a certain range or expanding the projection screen area of the interface, it is possible to assign different scores to virtually all products. For example, even with the current smartphone interface, there is no problem if the number of evaluation results is less than 1,000, but if it is less than 10,000, it is possible to give different scores to all products.
  • FIG. 3 is a diagram illustrating the difference in analysis methods between the two evaluation schemes.
  • Traditional evaluation schemes mainly focus on analysis along the horizontal axis. In other words, the main calculation is the weighted average value for each segment.
  • the relative evaluation scheme according to this embodiment allows analysis along the vertical axis. That is, it is possible to extract each user's preference model 102.
  • This scheme is a scheme that collects and accumulates element evaluations of products in the form of relative evaluation. According to this scheme, the user can record and view product characteristics in a visually easy-to-understand format.
  • (3) Understanding Taste Differences This scheme is a scheme that visualizes the extent to which a user's element evaluation deviates from other users' element evaluations. According to this scheme, a user can grasp the extent to which his or her own taste deviates from that of other users.
  • This scheme is a scheme in which a preference model 102 is generated based on the comprehensive evaluation 101 and product characteristics 103 accumulated for the user, and recommended products are extracted using the generated preference model 102. According to this scheme, users can receive recommendations of products that match their tastes.
  • Tasting Evaluation Scheme This scheme is a scheme for evaluating and scoring a user's tasting ability. According to this scheme, users can grasp their own tasting ability.
  • FIG. 4 shows an example of the configuration of a product evaluation system 400.
  • the product evaluation system 400 includes a management server 401 and a plurality of user terminals 402. These management server 401 and the plurality of user terminals 402 are connected to each other via a wired or wireless network, and can transmit and receive information via this network.
  • the management server 401 is a server that manages evaluations of alcoholic beverages.
  • the user terminal 402 is a terminal used by a user who drinks alcoholic beverages or a business entity that handles alcoholic beverages (for example, a manufacturer, a wholesaler, a retailer, a restaurant business).
  • the management server 401 and the user terminal 402 may be, for example, a mobile terminal such as a smartphone, a tablet, a mobile phone, or a personal digital assistant (PDA), or may be a wearable terminal such as a glasses-type, wristwatch-type, or clothing-type terminal. . Further, the management server 401 and the user terminal 402 may be stationary or portable computers, or servers placed on a cloud or a network. Further, the management server 401 and the user terminal 402 may be functionally a VR (Virtual Reality) terminal, an AR (Augmented Reality) terminal, or an MR (Mixed Reality) terminal. Alternatively, the management server 401 and the user terminal 402 may be a combination of these multiple terminals. For example, a combination of one smartphone and one wearable terminal can logically function as one terminal. Moreover, the management server 401 and the user terminal 402 may be information processing terminals other than these.
  • the management server 401 and user terminal 402 each include a processor that executes an operating system, applications, programs, etc., a main storage device such as RAM (Random Access Memory), an IC card, a hard disk drive, an SSD (Solid State Drive), and a flash drive.
  • main storage device such as RAM (Random Access Memory), an IC card, a hard disk drive, an SSD (Solid State Drive), and a flash drive.
  • Auxiliary storage devices such as memory, communication control units such as network cards, wireless communication modules, and mobile communication modules, input devices such as touch panels, keyboards, mice, voice input, and input based on motion detection using image capture by camera units, and monitors. and an output device such as a display.
  • the output device may be a device or terminal that transmits information to be output to an external monitor, display, projector, printer, device, or the like.
  • the main storage device stores various programs, applications, etc. (modules), and when the processor executes these programs and applications, each functional element of the overall system is realized.
  • each of these modules may be implemented in hardware by integrating them or the like.
  • each module may be an independent program or application, or may be implemented as a part of a subprogram or function within one integrated program or application.
  • each module is described as a subject (subject) that performs processing, but in reality, a processor that processes various programs, applications, etc. (modules) executes processing.
  • DB databases
  • a “database” is a functional element (storage unit) that stores a data set so as to be able to respond to arbitrary data operations (eg, extraction, addition, deletion, overwriting, etc.) from a processor or an external computer.
  • the implementation method of the database is not limited, and may be, for example, a database management system, spreadsheet software, or a text file such as XML or JSON.
  • FIG. 5 shows an example of the hardware configuration of the management server 401.
  • the management server 401 is configured of a server placed on a cloud, for example.
  • the main storage device 501 includes a posting management module 511, an evaluation management module 512, a comprehensive evaluation module 513, an element evaluation module 514, a first preference understanding module 515, a second preference understanding module 516, and a first taste difference understanding module. 517, a second taste difference understanding module 518, a model generation module 519, a correction module 520, a first product extraction module 521, a second product extraction module 522, a scoring module 523, a transmission module 524, and other programs are stored. There is. Each functional element of the management server 401 is realized by the processor 503 executing these programs.
  • the post management module 511 stores information regarding posts received from the user terminal 402 in the transaction DB 532.
  • the evaluation management module 512 manages evaluation values that relatively represent evaluations of a plurality of products. Specifically, this module manages a comprehensive evaluation value and element evaluation values regarding a plurality of evaluation axes.
  • the plurality of evaluation axes of managed element evaluation values are hierarchically configured (see FIGS. 42 to 44). Therefore, the user can evaluate the characteristics of the product in detail.
  • the evaluation axis of the managed element evaluation values can be set by the user of the user terminal 402. Therefore, professional users can evaluate products based on their own evaluation criteria.
  • the module updates the evaluation value stored in the posted issue table 544 or posted evaluation table 545 upon receiving an evaluation value update instruction received from the user terminal 402.
  • the comprehensive evaluation module 513 generates a comprehensive evaluation screen (see FIG. 27 or 28) in response to a screen request received from the user terminal 402.
  • the element evaluation module 514 receives a screen request received from the user terminal 402 and generates an element evaluation screen (see FIGS. 29 to 31).
  • the first preference grasping module 515 generates a first preference grasping screen (see FIGS. 32 to 35) in response to a screen request received from the user terminal 402.
  • the second preference grasping module 516 generates a second preference grasping screen (see FIG. 36) in response to a screen request received from the user terminal 402.
  • the first taste difference understanding module 517 generates a first taste difference understanding screen (see FIG. 37 or 38) in response to a screen request received from the user terminal 402.
  • the second taste difference understanding module 518 generates a second taste difference understanding screen (see FIG. 39) in response to a screen request received from the user terminal 402.
  • the model generation module 519 generates a preference model based on the comprehensive evaluation value accumulated in the posted brand table 1000 and the applied value stored in the brand feature table 1300.
  • the correction module 520 calculates the applied value based on the stored values stored in the brand feature table 1300. At this time, the module calculates the average value and standard deviation of the element evaluation values of the plurality of users accumulated in the posted evaluation table 1100, and calculates the applied value based on the calculated value and the stored value.
  • the first product extraction module 521 receives an extraction request received from the user terminal 402 and extracts products that match the user's preferences. Specifically, the module inputs the applied values stored in the brand feature table 1300 into the preference model and estimates the overall evaluation value of each product. Then, the module extracts, from among those products, products whose estimated comprehensive evaluation value satisfies a predetermined condition.
  • the products whose estimated comprehensive evaluation values satisfy a predetermined condition are, for example, the top three products in terms of comprehensive evaluation values.
  • the second product extraction module 522 also receives an extraction request received from the user terminal 402 and extracts products that match the user's preferences.
  • the products extracted here are in a different category from the products referenced when generating the preference model. For example, if the preference model is generated with reference to Japanese sake, the product is red wine.
  • the module corrects the preference model based on element evaluation axes that are common or similar between the product to be extracted and the product referred to when generating the preference model.
  • the module then inputs the applied values stored in the brand feature table 1300 into the corrected preference model to estimate the overall evaluation value of each product.
  • the module extracts, from among those products, products whose comprehensive evaluation value satisfies a predetermined condition.
  • the products whose estimated comprehensive evaluation values satisfy a predetermined condition are, for example, the top three products in terms of comprehensive evaluation values.
  • the scoring module 523 scores the user's tasting ability. Specifically, the module calculates the score by comparing the element evaluation value specified by the user with a predetermined value stored in the tasting scoring table 1200. Transmission module 524 transmits information generated by each module to user terminal 402.
  • the transmission module 524 also transmits to the user terminal 402 a code for incorporating the information (for example, a chart) generated by each module into a viewing screen (for example, a web page) managed by a third party.
  • the code referred to here is, for example, an iframe element.
  • the user of the user terminal 402 who has received this code can use the code to incorporate his or her chart into a web service operated by a third party.
  • the auxiliary storage device 502 includes databases such as a master DB 531, a transaction DB 532, and an analysis data DB 533.
  • the master DB 531 stores user information and product information managed by the management server 401.
  • the transaction DB 532 stores posted information and product evaluation information received from the user terminal 402 and score information managed by the management server 401.
  • the analysis data DB 533 stores product characteristic information, preference model information, and preference estimation values obtained by analyzing the information in the transaction DB 532.
  • FIG. 6 shows an example of the hardware configuration of the user terminal 402.
  • the user terminal 402 includes, for example, a smartphone, a tablet, a notebook PC, a desktop PC, or the like.
  • the main storage device 601 stores programs such as a posting module 611, a display module 612, an evaluation sending module 613, and a receiving module 614. Each functional element of the user terminal 402 is realized by the processor 603 executing these programs.
  • the posting module 611 generates product posting information according to the user's input operation, and sends it to the management server 401.
  • the display module 612 displays various screens received from the management server 401.
  • the evaluation sending module 613 sends the evaluation value corresponding to the changed position to the management server 401.
  • an icon is a figure or symbol that represents a product and is displayed on a computer display.
  • the evaluation transmission module 613 uploads the evaluation value or various screens received from the management server 401 to the web server so that a third party can view them, in response to an instruction input by the user.
  • the reception module 614 receives various screens from the management server 401.
  • the reception module 614 also receives from the management server 401 a code for incorporating information (for example, a chart) generated by the management server 401 into a viewing screen (for example, a web page) managed by a third party.
  • the code referred to here is, for example, an iframe element.
  • the user of the user terminal 402 who has received this code can use the code to incorporate his or her chart into a web service operated by a third party.
  • Auxiliary storage device 602 stores user terminal data 621. This user terminal data 621 is, for example, data on various screens received from the management server 401.
  • FIG. 7 and 8 show examples of two tables that constitute the master DB 531 of the management server 401.
  • FIG. 7 shows an example of a user master table 700.
  • the user master table 700 stores information about users who use the user terminals 402.
  • the table has fields such as user ID 701, type 702, nationality 703, gender 704, and year of birth 705.
  • the type 702 stores a value indicating the type of user.
  • the types of users mentioned here include consumers, manufacturers, wholesalers, retailers, food and beverage companies, and the like.
  • the table may also include fields for storing other user information (for example, a password for accessing the management server 401, address information, credit card information).
  • FIG. 8 shows an example of a brand master table 800.
  • the brand master table 800 stores information regarding brands of alcoholic beverages.
  • the table has fields such as brand ID 801, category ID 802, manufacturer ID 803, production area ID 804, and raw material ID 805.
  • the category ID 802 stores a value indicating the category of alcoholic beverages.
  • the category of alcoholic beverages mentioned here includes sake, red wine, white wine, beer, shochu, whiskey, awamori, etc.
  • the table may also include fields for storing other attribute information.
  • FIG. 9 shows an example of a posting table 900.
  • Post table 900 stores posted information received from user terminal 402.
  • the table has fields such as a posting ID 901, a user ID 902, a posting type 903, the number of stocks 904, and a title 905.
  • the post type 903 stores a value indicating the selection criteria for the entry target item at the time of posting.
  • the number of stocks 904 stores a value indicating whether the posting target is a single stock or multiple stocks.
  • the table may also include fields for storing other posting-related information (for example, posting date and time).
  • FIG. 10 shows an example of a posted issue table 1000.
  • the posted brand table 1000 stores comprehensive evaluation values for brands received from the user terminal 402.
  • the table has fields such as a post ID 1001, a stock number 1002, a stock ID 1003, a comprehensive evaluation (good or bad) 1004, a comprehensive evaluation (like or dislike) 1005, and a comment 1006.
  • the value stored in the stock number 1002 is used as a key between the posted stock table 1000 and the posted evaluation table 1100, considering that the target stock may be changed during post editing. be done.
  • the values stored in the comprehensive evaluation (good or bad) 1004 and the values stored in the comprehensive evaluation (like or dislike) 1005 may be used selectively.
  • the table may also include fields for storing other brand-related information (for example, category ID).
  • the user of the user terminal 402 specifies a target brand for each number.
  • the management server 401 analyzes the images uploaded for each number from the user terminal 402 and identifies the target brand.
  • the management server 401 analyzes the image uploaded from the user terminal 402, identifies the target stocks, and links them sequentially from the left to No. 1, No. 2, and so on.
  • FIG. 11 shows an example of a post evaluation table 1100.
  • Post evaluation table 1100 stores element evaluation values for brands received from user terminal 402.
  • the table has fields such as a post ID 1101, a stock number 1102, an element ID 1103, and an element evaluation 1104.
  • the table may also include fields for storing other brand-related information (eg, category ID, brand ID).
  • FIG. 12 shows an example of a tasting scoring table 1200.
  • the tasting scoring table 1200 stores scores in association with the element evaluation values of brands.
  • the table has fields such as brand ID 1201, element ID 1202, score range 1203, and score 1024.
  • FIG. 13 shows an example of a brand feature table 1300.
  • the brand feature table 1300 stores information regarding product characteristics stored in the brand master table 800 and information regarding element evaluations obtained by analyzing the information in the posted evaluation table 1100.
  • the table has fields such as brand ID 1301, type A 1302, type B 1303, element ID 1304, stored value 1305, average value 1306, standard deviation 1307, and applied value 1308.
  • type A 1302 stores a value indicating whether the product characteristic value is a subjective value or an objective value.
  • Type B 1303 stores a value indicating whether the product characteristic value is a quantitative value or a qualitative value.
  • the stored value 1305 stores a value set in advance for each product characteristic. In particular, subjective quantitative values are set by the manufacturer or business operator of the brand.
  • the applied value 1308 a value converted to dummy data (1) is stored for a qualitative value, and the value of the stored value 1305 is stored as is for an objective quantitative value.
  • the applied value 1308 stores the value of the stored value 1305 or the value obtained by correcting the value of the stored value 1305 with the value of the average value 1306 and the value of the standard deviation 1307.
  • the brand feature table 1300 may also include fields for storing other brand-related information (for example, category ID).
  • FIG. 14 shows an example of a preference model table 1400.
  • the preference model table 1400 stores a preference model obtained by performing multiple regression analysis on the information in the posted brand table 1000 and the brand feature table 1300.
  • the table has fields such as user ID 1401, category ID 1402, element number 1403, element ID 1404, partial regression coefficient 1405, standard partial regression coefficient 1406, and intercept 1407.
  • the value stored in the standard partial regression coefficient 1406 is a value representing the degree of influence that the product characteristics have on the user's preference.
  • FIG. 15 shows an example of a preference estimation table 1500.
  • the preference estimation table 1500 stores the overall evaluation value of each brand estimated using the preference model.
  • the table has fields of user ID 1501, category ID 1502, brand ID 1503, explanatory variable 1504, and objective variable 1505.
  • the explanatory variable 1504 is divided into multiple fields such as element “1” 1506, element “2" 1507, element “3” 1508, and so on.
  • element "1” stores a qualitative value (dummy variable)
  • element "2" 1507 stores an objective quantitative value
  • element "3” 1508 stores a qualitative value (dummy variable). stores subjective quantitative values.
  • the objective variable 1505 is divided into an actual value 1509 and an estimated value 1510. Among these, the actual value 1509 stores the overall evaluation value of the posted brand table 1000, and the estimated value 1510 stores the overall evaluation value estimated using the preference model.
  • FIG. 16 shows an example of a comprehensive evaluation screen generation flow 1600 executed by the comprehensive evaluation module 513 of the management server 401.
  • the comprehensive evaluation module 513 acquires the screen request received from the user terminal 402 (step 1610). This screen request includes a posting ID.
  • the module acquires the overall evaluation value associated with the acquired posting ID from the posted issue table 1000 (step 1620).
  • the module generates a comprehensive evaluation screen based on the obtained comprehensive evaluation value (step 1630).
  • the generated comprehensive evaluation screen is information indicating the comprehensive evaluation value for each of the plurality of stocks.
  • This comprehensive evaluation screen is transmitted by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received comprehensive evaluation screen is displayed by the display module 612 of the user terminal 402.
  • FIG. 27 shows a comprehensive evaluation screen 2700 as an example of the displayed comprehensive evaluation screen.
  • the comprehensive evaluation screen 2700 shows a single-axis comprehensive evaluation (good or bad) chart 2701.
  • a plurality of icons 2702 are arranged.
  • Each icon 2702 represents a brand, and the position of each icon 2702 represents the overall evaluation value of the brand. Further, the positional relationship between the icons 2702 represents the ranking and degree of spread of the overall evaluation of the corresponding brand.
  • the user can easily grasp the ranking and degree of spread of the comprehensive evaluation of each brand. Furthermore, the user can easily identify brands that match his or her tastes.
  • the comprehensive evaluation screen 2700 shows a chart 2701 of a single-axis comprehensive evaluation (good or bad)
  • a chart of a single-axis comprehensive evaluation may be shown instead.
  • the chart shown on the screen (in other words, the evaluation axis) may be selected by the user of the user terminal 402.
  • FIG. 28 shows a comprehensive evaluation screen 2800 as another example of the displayed comprehensive evaluation screen.
  • the comprehensive evaluation screen 2800 shows a chart 2801 of two axes of comprehensive evaluation (good and bad, and likes and dislikes).
  • a plurality of icons 2802 are arranged.
  • Each icon 2802 represents a brand, and the position of each icon 2802 represents the overall evaluation value of the brand with respect to the two evaluation axes.
  • This comprehensive evaluation screen 2800 also allows the user to easily grasp the ranking and degree of spread of the comprehensive evaluations of each brand, and also to easily grasp the brands that match his or her tastes.
  • the above-mentioned icon 2702 or 2802 is displayed so as to be operable.
  • the evaluation transmission module 613 of the user terminal 402 transmits the comprehensive evaluation value corresponding to the changed position to the management server 401.
  • the transmitted comprehensive evaluation value is acquired by the evaluation management module 512 of the management server 401.
  • the module updates the overall evaluation value of the issue stored in the posted issue table 1000 with the obtained overall evaluation value.
  • the evaluation transmission module 613 of the user terminal 402 can upload the above comprehensive evaluation screen 2700 or 2800 or the evaluation value shown on the screen to the web server in response to an instruction input by the user. Therefore, a user who is a blogger can incorporate the above comprehensive evaluation screen 2700 or 2800 into his/her own blog.
  • businesses that handle alcoholic beverages for example, manufacturers, wholesalers, retailers, and restaurant businesses
  • the business operator can incorporate the comprehensive evaluation screen 2700 or 2800 into its homepage or EC site.
  • retailers and restaurant businesses can publish articles comparing drinks to consumers with the aim of attracting customers to their stores.
  • FIG. 17 shows an example of an element evaluation screen generation flow 1700 executed by the element evaluation module 514 of the management server 401.
  • the element evaluation module 514 obtains the screen request received from the user terminal 402 (step 1710).
  • This screen request includes a posting ID and a category ID.
  • the module acquires the element ID associated with the acquired category ID from a table (not shown) that associates categories and elements (step 1720).
  • the module obtains the element evaluation value associated with the obtained post ID and element ID from the post evaluation table 1100 (step 1730).
  • the module generates an element evaluation screen based on the acquired element evaluation value (step 1740).
  • the generated element evaluation screen is information showing the element evaluation value of the evaluation axis specified by the user for each of the plurality of stocks.
  • This element evaluation screen is transmitted by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received element evaluation screen is displayed by the display module 612 of the user terminal 402.
  • FIG. 29 shows an element evaluation screen 2900 as an example of a displayed element evaluation screen.
  • the element evaluation screen 2900 shows a chart 2901 of one-axis element evaluation (fragrance).
  • a plurality of icons 2902 are arranged.
  • Each icon 2902 represents a brand, and the position of each icon 2902 represents the element evaluation value of the brand. Further, the positional relationship between the icons 2902 represents the ranking and degree of spread of the element evaluations of the corresponding brands.
  • the user can easily grasp the ranking and degree of spread of the element evaluations of each brand.
  • FIG. 30 shows an element evaluation screen 3000 as another example of the displayed element evaluation screen.
  • the element evaluation screen 3000 shows a chart 3001 of one-axis element evaluation (fragrance). This chart 3001 is different from the chart 2901 described above, which is weighted in one direction, and is weighted in both directions.
  • a plurality of icons 3002 are arranged. Each icon 3002 represents a brand, and the position of each icon 3002 represents the element evaluation value of the brand. Further, the positional relationship between the icons 3002 represents the ranking and degree of spread of the element evaluations of the corresponding brands.
  • This element evaluation screen 3000 also allows the user to easily grasp the ranking and spread of the element evaluations of each brand.
  • the element evaluation screens 2900 and 3000 show charts 2901 and 3001 for 1-axis element evaluation (fragrance), but instead, 1-axis element evaluation charts for other elements may be shown. .
  • the chart shown on the screen (in other words, the evaluation axis) may be selected by the user of the user terminal 402.
  • FIG. 31 shows an element evaluation screen 3100 as yet another example of the displayed element evaluation screen.
  • the element evaluation screen 3100 shows a chart 3101 of two axis element evaluations (sweet and spicy and light and heavy).
  • a plurality of icons 3102 are arranged.
  • Each icon 3102 represents a brand, and the position of each icon 3102 represents the element evaluation value of the brand with respect to the two evaluation axes.
  • This element evaluation screen 3100 also allows the user to easily grasp the ranking and degree of spread of element evaluations for each stock.
  • a three-axis 3D chart representing element evaluations for three types of evaluation axes may be shown on the element evaluation screen.
  • the display module 612 may receive a user's selection of an evaluation axis and display a single-axis chart of the selected evaluation axis. For example, the module may display a chart of one-axis element evaluation (sweet and spicy) in response to the user selecting the evaluation axis "sweet and spicy" in the chart 3101 described above.
  • the display module 612 may dynamically change the number of axes of the chart to be displayed according to the number of evaluation axes specified by the user. For example, the module may display a chart 3101 of two axis element evaluations (sweet and spicy and light and heavy) in response to the user specifying two element evaluations, ⁇ sweet and spicy'' and ⁇ light and heavy''.
  • the display module 612 may dynamically change the design of the chart to be displayed according to the evaluation axis specified by the user. For example, the module may display a chart weighted in one direction as shown in FIG. 29, or a chart weighted in both directions as shown in FIG. 30, depending on the evaluation axis specified by the user. may be displayed.
  • the above icons 2902, 3002, or 3102 are displayed so as to be operable.
  • the evaluation transmission module 613 of the user terminal 402 transmits the element evaluation value corresponding to the changed position to the management server 401.
  • the transmitted element evaluation value is acquired by the evaluation management module 512 of the management server 401.
  • the module updates the element evaluation value of the brand stored in the posted evaluation table 1100 with the acquired element evaluation value.
  • the evaluation transmission module 613 of the user terminal 402 can upload the above element evaluation screen 2900, 3000, or 3100 or the evaluation value shown on the screen to the web server in response to an instruction input by the user. Therefore, a user who is a blogger can incorporate the above-mentioned element evaluation screen 2900, 3000, or 3100 into his or her own blog.
  • businesses that handle alcoholic beverages for example, manufacturers, wholesalers, retailers, and restaurant businesses
  • the business operator can incorporate the element evaluation screen 2900, 3000, or 3100 into its homepage or EC site.
  • retailers and restaurant businesses can publish articles comparing drinks to consumers with the aim of attracting customers to their stores.
  • FIG. 18 shows an example of a first preference ascertainment screen generation flow 1800 executed by the first preference ascertainment module 515 of the management server 401.
  • the first preference understanding module 515 acquires a screen request received from the user terminal 402 (step 1810).
  • This screen request includes a user ID, category ID, and element ID.
  • the module obtains the overall evaluation value and product characteristic value of each brand. Specifically, the module obtains the post ID associated with the obtained user ID from the post table 900 (step 1820). The module acquires the brand ID associated with the acquired category ID from the brand master table 800 (step 1830).
  • the module acquires a comprehensive evaluation value associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 1840).
  • the module acquires the brand number associated with the acquired brand ID from the posted brand table 1000 (step 1850).
  • the module acquires the element evaluation value associated with the acquired posting ID, stock number, and element ID from the posting evaluation table 1100.
  • the module acquires product characteristic values associated with the acquired brand ID and element ID from the brand master table 800 (step 1860).
  • the module calculates the frequency distribution of evaluation results for the product characteristic (step 1870).
  • the module calculates the dispersion of the overall evaluation value for each class of the calculated frequency distribution (step 1880). Note that in this specification, "class” includes not only the numerical range of quantitative values but also the type of qualitative values.
  • the module generates a first preference understanding screen based on the calculated frequency distribution and variation (step 1890).
  • the generated first preference understanding screen is information indicating the calculated frequency distribution and variation.
  • This first preference understanding screen is transmitted by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received first preference understanding screen is displayed by the display module 612 of the user terminal 402.
  • FIG. 32 shows a first preference grasping screen 3200 as an example of the displayed first preference grasping screen.
  • the first preference understanding screen 3200 shows the relationship between product characteristics (particularly quantitative values) and comprehensive evaluation.
  • a histogram 3201 representing the frequency distribution of evaluation results for each class of taste, which is a product characteristic, is shown.
  • the width of the class of this histogram 3201 is determined according to the number of evaluation results.
  • the screen shows a boxplot (boxes only) 3202 at the top that represents the dispersion of the overall evaluation value in units of classes of the histogram 3201.
  • the user can understand the range of values for product characteristics that match his or her preferences. Further, the user can grasp the correlation and relationship between the product characteristic value and the comprehensive evaluation value.
  • FIG. 33 shows a first preference grasping screen 3300 as another example of the displayed first preference grasping screen.
  • the first preference grasping screen 3300 shows a histogram 3301 and a boxplot (with points) 3302, similarly to the first preference grasping screen 3200.
  • points 3303 or icons 3304 representing the user's individual evaluation results (vertical axis is the overall evaluation value, horizontal axis is the element evaluation value) are plotted on the boxplot 3302 of the screen.
  • the icon 3304 represents the comprehensive evaluation value registered in the most recent predetermined period
  • the point 3303 represents the other comprehensive evaluation values.
  • points or icons filled with the class value of the relevant class are plotted on the horizontal axis.
  • FIG. 34 shows a first preference grasping screen 3400 as yet another example of the displayed first preference grasping screen.
  • the first preference understanding screen 3400 shows the relationship between product characteristics (particularly qualitative values) and comprehensive evaluation.
  • a histogram 3401 representing the frequency distribution of evaluation results for each raw material, which is a product characteristic, is shown.
  • the screen shows a boxplot (boxes only) 3402 in the upper part of the histogram 3401 representing the variation in the overall evaluation value for each raw material.
  • FIG. 35 shows a first preference grasping screen 3500 as yet another example of the displayed first preference grasping screen.
  • the first preference grasping screen 3500 shows a histogram 3501 and a box plot (with dots) 3502, similarly to the first preference grasping screen 3400.
  • points 3503 or icons 3504 representing the user's individual evaluation results (the vertical axis is the overall evaluation value) are plotted.
  • the icon 3504 represents the comprehensive evaluation value registered in the most recent predetermined period
  • the point 3503 represents the other comprehensive evaluation values.
  • the user can grasp the range of values of product characteristics that match his or her preferences. Further, the user can grasp the correlation and relationship between the product characteristic value and the comprehensive evaluation value.
  • FIG. 19 shows an example of a second preference grasping screen generation flow 1900 executed by the second preference grasping module 516 of the management server 401.
  • the flow shown in the figure is different from the above-described first preference understanding screen generation flow 1800 for illustrating the relationship between specific elements and comprehensive evaluation, and is different from the flow 1800 for generating the first preference understanding screen described above for illustrating the relationship between specific elements and comprehensive evaluation. It's a flow.
  • the second preference understanding module 516 acquires the screen request received from the user terminal 402 (step 1910).
  • This screen request includes a user ID and a category ID.
  • the module acquires, from the preference model table 1400, element IDs that are associated with the acquired category ID and whose standard partial regression coefficients are within the top five (step 1920). Note that the number of element IDs acquired here is not limited to "5" and may be any number.
  • the module obtains the overall evaluation value and product characteristic value of each brand. Specifically, the module obtains a post ID associated with the obtained user ID from the post table 900 (step 1930). The module acquires the brand ID associated with the acquired category ID from the brand master table 800 (step 1940).
  • the module acquires a comprehensive evaluation value associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 1950).
  • the module acquires the brand number associated with the acquired brand ID from the posted brand table 1000 (step 1960).
  • the module acquires the element evaluation value associated with the acquired posting ID, stock number, and element ID from the posting evaluation table 1100.
  • the module acquires product characteristic values associated with the acquired brand ID and element ID from the brand master table 800 (step 1970).
  • the module After acquiring the comprehensive evaluation value and product characteristic value for each brand, the module calculates the dispersion of the comprehensive evaluation value in units of product characteristic values for the qualitative values (step 1980). The module generates a second preference understanding screen based on the calculated variation, the acquired evaluation value, and the standard partial regression coefficient (step 1990). The generated second preference understanding screen is information indicating the calculated variation, the product characteristic value corresponding to the evaluation axis of the quantitative value, and the comprehensive evaluation value.
  • This second preference understanding screen is transmitted by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received second preference understanding screen is displayed by the display module 612 of the user terminal 402.
  • FIG. 36 shows an example of a second preference understanding screen 3600.
  • the second preference understanding screen 3600 shows the relationship between each product characteristic and the overall evaluation.
  • This screen has columns such as factor 3601, relationship strength 3602, and relationship 3603.
  • factor 3601 shows product characteristics.
  • the relationship strength 3602 column shows the degree of influence that the product characteristics have on the overall evaluation. This degree of influence is determined by the standard partial regression coefficient corresponding to the product characteristic.
  • a chart showing the relationship between product characteristics and comprehensive evaluation is shown.
  • a line graph showing correlations and relationships between objects is shown, with the horizontal axis representing the product characteristics and the vertical axis representing the overall evaluation.
  • a boxplot representing the dispersion of comprehensive evaluation values in units of product characteristic values is shown.
  • FIG. 20 shows an example of a generation flow 2000 of a first taste difference understanding screen executed by the first taste difference understanding module 517 of the management server 401.
  • the first taste difference understanding module 517 acquires a screen request received from the user terminal 402 (step 2010).
  • This screen request includes a user ID, brand ID, and element ID.
  • the module obtains the element evaluation value of the target user. Specifically, the module acquires a posting ID associated with the acquired user ID from the posting table 900 (step 2020). The module acquires the brand number associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 2030). The module acquires the element evaluation value associated with the acquired posting ID, brand number, and element ID from the posting evaluation table 1100 (step 2040).
  • the module obtains the element evaluation values of other users. Specifically, the module acquires a posting ID (another user's posting ID) associated with a user ID other than the acquired user ID from the posting table 900 (step 2050). The module acquires the brand number associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 2060). The module obtains the post ID of the other user, the stock number of the other user, and the element evaluation value associated with the element ID from the post evaluation table 1100 (step 2070).
  • a posting ID another user's posting ID
  • the module acquires the brand number associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 2060).
  • the module obtains the post ID of the other user, the stock number of the other user, and the element evaluation value associated with the element ID from the post evaluation table 1100 (step 2070).
  • the module After acquiring the element evaluation values of other users, the module calculates the frequency distribution of the evaluation results by other users with respect to the evaluation axis of the element evaluation values (step 2080). The module generates a first taste difference understanding screen based on the calculated frequency distribution and the obtained element evaluation value of the target user (step 2090).
  • the generated first taste difference understanding screen is information indicating the calculated frequency distribution and the target user's element evaluation value. Note that the frequency distribution referred to here is information indicating the number of evaluation results by one or more other users corresponding to each class.
  • the generated first taste difference understanding screen is transmitted by the user terminal 402 by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received first taste difference understanding screen is displayed by the display module 612 of the user terminal 402.
  • FIG. 37 shows a first taste difference understanding screen 3700 as an example of the displayed first taste difference understanding screen.
  • the first taste difference understanding screen 3700 shows a histogram 3701 representing the frequency distribution of evaluation results by other users with respect to the evaluation axis (taste) of element evaluation.
  • This histogram 3701 has five classes. The width of this class is determined according to the number of evaluation results.
  • an icon 3702 is plotted on the histogram 3701 at a position corresponding to the element evaluation value of the target user. Therefore, this first taste difference understanding screen 3700 shows the degree of deviation between the element evaluation values of the target user and other users.
  • the target user can understand the difference in taste from other users.
  • FIG. 38 shows a first taste difference understanding screen 3800 as another example of the displayed first taste difference understanding screen.
  • the first taste difference understanding screen 3800 shows a histogram 3801 and an icon 3802 similarly to the first taste difference understanding screen 3700.
  • the histogram 3801 of the screen has 10 classes.
  • the target user can understand the difference in taste from other users.
  • FIG. 21 shows an example of a second taste difference understanding screen generation flow 2100 executed by the second taste difference understanding module 518 of the management server 401.
  • the flow shown in the figure differs from the above-described first taste difference understanding screen generation flow 2000, which targets the evaluation results for a combination of a specific brand and a specific taste, and the flow shows the evaluation results for multiple brands for a specific taste. It is targeted.
  • the second taste difference understanding module 518 acquires the screen request received from the user terminal 402 (step 2110).
  • This screen request includes a user ID, category ID, and element ID.
  • the module obtains the element evaluation value of the target user. Specifically, the module obtains the post ID associated with the obtained user ID from the post table 900 (step 2120). The module acquires the brand ID associated with the acquired category ID from the brand master table 800 (step 2130).
  • the module acquires the brand number associated with the acquired brand ID from the posted brand table 1000 (step 2140).
  • the module acquires the element evaluation value associated with the acquired posting ID, stock number, and element ID from the posting evaluation table 1100 (step 2150).
  • the module obtains the element evaluation values of other users. Specifically, the module acquires a posting ID (another user's posting ID) associated with a user ID other than the acquired user ID from the posting table 900 (step 2160). The module acquires, from the post evaluation table 1100, the post ID of another user, the brand number, and the element evaluation value associated with the element ID (step 2170).
  • a posting ID another user's posting ID
  • the module acquires, from the post evaluation table 1100, the post ID of another user, the brand number, and the element evaluation value associated with the element ID (step 2170).
  • the module After acquiring the element evaluation values of other users, the module calculates the average value of the element evaluation values for each brand (step 2180). The module generates a second taste difference understanding screen based on the calculated average value and the obtained element evaluation value of the target user (step 2190). The generated second taste difference understanding screen is information indicating the calculated average value and the target user's element evaluation value. Note that the average value here is information indicating the element evaluation values of one or more other users.
  • the generated second taste difference understanding screen is transmitted by the user terminal 402 by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received second taste difference understanding screen is displayed by the display module 612 of the user terminal 402.
  • FIG. 39 shows an example of the second taste difference understanding screen 3900.
  • the second taste difference understanding screen 3900 shows a scatter diagram representing the correlation (in other words, the degree of deviation) between the element evaluations (sourness) of the target user and other users.
  • the vertical axis represents the element evaluation of the target user
  • the horizontal axis represents the average value of the element evaluations of other users.
  • Each point 3902 in the figure represents a brand.
  • An area 3903 in the figure is an area where element evaluations are similar between the target user and other users.
  • this second taste difference understanding screen 3900 the target user can understand the difference in taste from other users.
  • FIG. 22 shows an example of a tasting evaluation sequence 2200 executed by the product evaluation system 400.
  • the scoring module 523 of the management server 401 acquires the stock ID received from the scorer's user terminal 402 (step 2205). This brand ID is information indicating the brand to be subjected to tasting evaluation.
  • the module stores the acquired brand ID in the tasting scoring table 1200 (step 2210).
  • the module generates a score designation screen based on the acquired stock ID (step 2215) and sends it to the scorer's user terminal 402 (step 2220).
  • the display module 612 of the user terminal 402 that receives this score designation screen displays the screen (step 2225).
  • FIG. 40 shows an example of a score specification screen 4000.
  • the score designation screen 4000 is a screen for designating a score for the element evaluation value of a brand.
  • the screen shows, as an example, a chart 4001 of one-axis element evaluation (taste) for one brand.
  • score designation frames 4002 to 4004 are arranged. These score designation frames 4002 to 4004 indicate score ranges of 40 points, 70 points, and 100 points, respectively.
  • the scorer can assign scores to each element evaluation value by operating score designation frames 4002 to 4004.
  • the user terminal 402 transmits score setting information to the management server 401 (step 2235).
  • This score setting information is information indicating scores for each element of each brand.
  • the scoring module 523 of the management server 401 that has received this score setting information stores the brand ID, element ID, and score in association with each other in the tasting scoring table 1200 (step 2240).
  • the module generates a response screen based on the brand ID obtained in step 2205 (step 2245), and sends it to the respondent's user terminal 402 (step 2250).
  • the display module 612 of the user terminal 402 that received this answer screen displays the screen (step 2255).
  • FIG. 41 shows an example of an answer screen 4100.
  • the response screen 4100 shows, as an example, a chart 4101 of one-axis element evaluation (fragrance).
  • a plurality of icons 4102 are arranged in this chart 4101. Each icon 4102 represents a brand, and the position of each icon 4102 represents the element evaluation value of the brand. By operating these icons 4102, the respondent can specify the element evaluation value of the corresponding brand.
  • the user terminal 402 transmits the answer information to the management server 401 (step 2265).
  • This response information is information indicating the element evaluation value for each brand for each element.
  • the scoring module 523 of the management server 401 that has received this answer information scores the received answer (step 2270). Specifically, the module refers to the tasting scoring table 1200 and obtains a score corresponding to the received element evaluation value for each element and brand.
  • the module After obtaining scores for all elements and brands, the module generates a scoring result screen (step 2275) and sends it to the respondent's user terminal 402 (step 2280).
  • the display module 612 of the user terminal 402 that received this scoring result screen displays the screen (step 2285).
  • the displayed scoring result screen (not shown) is a screen that shows scores for each brand regarding each element.
  • FIG. 23 shows an example of a subjective quantitative value correction flow 2300 executed by the correction module 520 of the management server 401.
  • the correction module 520 acquires one element ID to be processed from the brand characteristic table 1300 for the brand to be processed (step 2310).
  • the module acquires the element ID (that is, the element ID of the subjective quantitative value) that is associated with type A "subjective" and type B "quantitative value" in the stock feature table 1300.
  • the module acquires all element evaluation values associated with the acquired element ID from the posted evaluation table 1100 (step 2320).
  • the module calculates the average value and standard deviation of the obtained element evaluation values (step 2330).
  • the module stores the calculated average value and standard deviation in the brand feature table 1300 in association with the acquired element ID (step 2340).
  • the module acquires the stored value associated with the acquired element ID from the brand feature table 1300 (step 2350).
  • the module calculates an applied value based on the obtained stored value and the calculated mean value and standard deviation (step 2360). Specifically, if the stored value is within the range of ⁇ 1 ⁇ of the average value, the module considers the stored value to be the applied value; otherwise, the weighted average value of the stored value and the average value is used as the applied value. I reckon.
  • the module stores the calculated application value in the brand feature table 1300 in association with the acquired element ID (step 2370).
  • the module determines whether applied values have been calculated for all subjective quantitative values of the stock to be processed (step 2380). If the result of this determination is that applied values have not been calculated for all subjective quantitative values, the module executes step 2310. On the other hand, if the applied values have been calculated for all the subjective quantitative values, the module determines whether the applied values have been calculated for all the stocks to be processed (step 2390). As a result of this determination, if the applied values have not been calculated for all the stocks to be processed, the module executes step 2310. On the other hand, when the applied values have been calculated for all the stocks to be processed, the module ends this correction flow.
  • the user's actual evaluation can be reflected in the stored values stored in advance in the brand feature table 1300.
  • FIG. 24 shows an example of a preference model generation flow 2400 executed by the model generation module 519 of the management server 401. The flow shown in the figure is periodically executed for each user and category pair. The model generation module 519 obtains the user ID and category ID to be processed (step 2410).
  • the module obtains the overall evaluation value and product characteristic value of each brand. Specifically, the module obtains a post ID associated with the obtained user ID from the post table 900 (step 2420). The module acquires the brand ID associated with the acquired category ID from the brand master table 800 (step 2430).
  • the module acquires the overall evaluation value associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 2440).
  • the module acquires the applied value associated with the acquired brand ID from the brand feature table 1300 (step 2450).
  • the module After obtaining the comprehensive evaluation value and applied value for each brand, the module performs multiple regression analysis based on the obtained comprehensive evaluation value and applied value, and creates a preference model with the overall evaluation as the objective variable and product characteristics as the explanatory variable. Generate (step 2460). At this time, the module may narrow down the elements to be used as explanatory variables to predetermined elements in order to prevent multicollinearity from occurring.
  • the module stores the partial regression coefficient and standard partial regression coefficient for each element, and the intercept in the preference model table 1400 in association with the acquired user ID and category ID (step 2470).
  • FIG. 25 shows an example of a first product extraction flow 2500 executed by the first product extraction module 521 of the management server 401.
  • the first product extraction module 521 acquires a product extraction request received from the user terminal 402 (step 2505).
  • This product extraction request includes a user ID and a category ID.
  • the module generates a preference estimation table 1500 for product extraction. Specifically, the module obtains the post ID associated with the obtained user ID from the post table 900 (step 2510). The module acquires the brand ID associated with the acquired category ID from the brand master table 800 (step 2515).
  • the module acquires the comprehensive evaluation value associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 2520).
  • the module acquires the applicable value of each element associated with the acquired post ID from the brand feature table 1300 (step 2525).
  • the module associates the acquired user ID, category ID, brand ID, comprehensive evaluation value, and application value and stores them in the preference estimation table 1500 (step 2530).
  • the module After generating the preference estimation table 1500, the module acquires a preference model associated with the acquired user ID and category ID from the preference model table 1400 (step 2535). For each brand ID registered in the preference estimation table 1500, the module estimates a comprehensive evaluation value by inputting the applied value into the acquired preference model, and stores the estimated comprehensive evaluation value in the preference estimation table 1500 (step 2540 ). At this time, the module estimates the comprehensive evaluation value only for brand IDs for which comprehensive evaluation values (actual values) are not registered among the brand IDs registered in the preference estimation table 1500.
  • the module acquires the top three brand IDs in overall evaluation value from the preference estimation table 1500 (step 2545).
  • the module acquires various information associated with the acquired brand ID from the brand master table 800 (step 2550).
  • the module generates a stock recommendation screen based on the acquired various information (step 2555).
  • the generated brand recommendation screen provides various information about the extracted brands.
  • This brand recommendation screen is transmitted from the user terminal 402 by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received stock recommendation screen is displayed by the display module 612 of the user terminal 402.
  • the preference estimation table 1500 is created for convenience in order to make the explanation easier to understand. When data is expanded using SQL or calculation memory, it is not necessary to create the table.
  • FIG. 26 shows an example of a second product extraction flow 2600 executed by the second product extraction module 522 of the management server 401.
  • this second product extraction flow 2600 unlike the first product extraction flow 2500 described above in which products with the same category as the preference model are extracted, products with a different category from the preference model are extracted. For example, brands of red wine are extracted using a preference model for Japanese sake.
  • the second product extraction module 522 acquires the product extraction request received from the user terminal 402 (step 2605).
  • This product extraction request includes a user ID, a first category ID, and a second category ID.
  • the first category ID is an ID that identifies the category of the preference model
  • the second category ID is an ID that identifies the category of the product to be extracted.
  • the module generates a preference estimation table 1500 for product extraction. Specifically, the module obtains the post ID associated with the obtained user ID from the post table 900 (step 2610). The module acquires the brand ID associated with the acquired second category ID from the brand master table 800 (step 2615).
  • the module acquires the comprehensive evaluation value associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 2620).
  • the module acquires the applicable value of each element associated with the acquired post ID from the brand feature table 1300 (step 2625).
  • the module associates the acquired user ID, second category ID, brand ID, comprehensive evaluation value, and application value and stores them in the preference estimation table 1500 (step 2630).
  • the module After generating the preference estimation table 1500, the module then generates a preference model. Specifically, the module acquires a preference model associated with the acquired user ID and first category ID from the preference model table 1400 (step 2635).
  • the module corrects the acquired preference model (step 2640). Specifically, this module identifies common or similar elements between the product to be extracted and the product referenced when generating the preference model, and extracts elements other than the identified elements from the explanatory variables of the preference model. exclude. At this time, the module identifies common or similar elements among the products, for example, with reference to an element correspondence table.
  • FIG. 42 to 44 show examples of element correspondence tables.
  • 42 shows an element correspondence table 4200
  • FIG. 43 shows an element correspondence table 4300
  • FIG. 44 shows an element correspondence table 4400.
  • Element correspondence tables 4200, 4300, and 4400 show correspondences between elements of Japanese sake and red wine.
  • the elements shaded in the element correspondence tables 4200, 4300, and 4400 are common or similar elements between the two products.
  • the module may multiply similar elements between both products by a coefficient according to the degree of similarity.
  • the module After correcting the preference model, the module estimates a comprehensive evaluation value by inputting an applied value into the corrected preference model for each brand ID registered in the preference estimation table 1500, and then inputs the estimated comprehensive evaluation value into the preference estimation table 1500. (step 2645). At this time, the module estimates the comprehensive evaluation value only for brand IDs for which comprehensive evaluation values (actual values) are not registered among the brand IDs registered in the preference estimation table 1500.
  • the module acquires the top three brand IDs in overall evaluation value from the preference estimation table 1500 (step 2650).
  • the module acquires various information associated with the acquired brand ID from the brand master table 800 (step 2655).
  • the module generates a brand recommendation screen based on the acquired various information (step 2660).
  • the generated brand recommendation screen provides various information about the extracted brands.
  • This brand recommendation screen is transmitted from the user terminal 402 by the transmission module 524 of the management server 401 and received by the reception module 614 of the user terminal 402.
  • the received stock recommendation screen is displayed by the display module 612 of the user terminal 402.
  • the preference estimation table 1500 is created for convenience in order to make the explanation easier to understand.
  • data is expanded using SQL or calculation memory, it is not necessary to create the table.
  • variables a and b are weighting factors. These coefficients may be specified by the scorer.
  • the variable X indicates whether the relative relationship of the brands (in other words, the order of the brands with respect to the evaluation axis) is correct.
  • the variable Y indicates whether the evaluation of each brand is correct.
  • the score specification screen 4000 and answer screen 4100 in the tasting evaluation sequence 2200 are used to specify and answer element evaluations for one evaluation axis. However, instead of this, a screen for specifying and answering element evaluations for two evaluation axes may be generated.
  • FIG. 45 shows an example of an answer screen 4500 generated in that case.
  • the answer screen 4500 shows a chart 4501 of two-axis element evaluation (sweet and spicy and light and heavy).
  • a plurality of icons 4502 are arranged.
  • Each icon 4502 represents a brand, and the position of each icon 4502 represents the element evaluation value of the brand.
  • FIG. 46 shows an example of a score specification screen 4600 for element evaluation of a brand.
  • the score specification screen 4600 is a screen for specifying scores for two element evaluation values of a brand.
  • This screen shows a chart 4601 of two-axis element evaluation (sweet and spicy and light and heavy).
  • score designation frames 4602 to 4604 are arranged. These score designation frames 4602 to 4604 indicate score ranges of 40 points, 70 points, and 100 points, respectively.
  • the user can assign a score to each element evaluation value by operating score designation frames 4602 to 4604.
  • a preference model is generated by multiple regression analysis (see step 2470).
  • the preference model may be generated using other statistical methods or machine learning methods.
  • alcoholic beverages are assumed to be the target product.
  • other palatability products may be adopted as target products instead of alcoholic beverages.
  • the management server 401 generates various screens.
  • the management server 401 may transmit data necessary for generating various screens to the user terminal 402, and the user terminal 402 may generate the various screens.
  • the evaluation value is input in the form of relative evaluation (for example, see FIG. 29).
  • input in a format other than relative evaluation may also be selectable.
  • FIG. 47 shows an example of an input screen 4700 in a format other than relative evaluation.
  • the input screen 4700 has buttons 4701 for a plurality of elements to be input.
  • FIG. 48 shows an example of an input screen 4800 that is displayed when any of the buttons 4701 on the input screen 4700 is selected.
  • Input screen 4800 has dialog box 4801.
  • FIG. 49 shows an example of an input screen 4900 that is displayed when "Yes" is selected in the dialog box 4801 of the input screen 4800.
  • the input screen 4900 has buttons 4901 for a plurality of elements to be input, and an indicator 4902 for specifying an evaluation value.
  • the user can specify an evaluation value by operating the indicator 4902.
  • the specified evaluation value is transmitted to the management server 401 by the evaluation transmission module 613 of the user terminal 402.
  • the transmitted evaluation value is stored in the posted evaluation table 1100 by the evaluation management module 512 of the management server 401.
  • the user is asked to specify a posting ID (see step 1610).
  • the user may be allowed to specify the user ID and category ID.
  • FIG. 50 shows an example of a comprehensive evaluation screen generation flow 5000 executed in that case.
  • the comprehensive evaluation module 513 acquires the screen request received from the user terminal 402 (step 5010).
  • This screen request includes a user ID and a category ID.
  • the module obtains the post ID associated with the obtained user ID from the post table 900 (step 5020).
  • the module acquires the brand ID associated with the acquired category ID from the brand master table 800 (step 5030).
  • the module acquires the overall evaluation value associated with the acquired posting ID and brand ID from the posted brand table 1000 (step 5040).
  • the module generates a comprehensive evaluation screen based on the obtained comprehensive evaluation value (step 5050). According to this modification, it is possible to generate a comprehensive evaluation screen for brands belonging to a specific category.
  • step 1710 the user is asked to specify a posting ID and a category ID (see step 1710). However, instead of this, the user may be allowed to specify the category ID and element ID.
  • FIG. 51 shows an example of an element evaluation screen generation flow 5100 executed in that case.
  • the element evaluation module 514 obtains the screen request received from the user terminal 402 (step 5110).
  • This screen request includes a user ID, category ID, and element ID.
  • the module obtains the post ID associated with the obtained user ID from the post table 900 (step 5120).
  • the module acquires the brand ID associated with the acquired category ID from the brand master table 800 (step 5130).
  • the module acquires the brand number associated with the acquired brand ID from the posted brand table 1000 (step 5140).
  • the module acquires the element evaluation value associated with the acquired posting ID, stock number, and element ID from the posting evaluation table 1100 (step 5150).
  • the module generates an element evaluation screen based on the acquired element evaluation value (step 5160). This modification also makes it possible to generate an element evaluation screen.
  • the user specifies the evaluation value of the brand using an icon (see FIGS. 27 to 31). That is, the user specifies the evaluation value of the stock using points. Alternatively, the user may be allowed to specify the evaluation value of the brand in terms of area (in other words, in range).
  • evaluation values are collected and accumulated for each issue.
  • evaluation values may be collected and accumulated for each group of stocks.
  • the brand group referred to here is a group classified based on product characteristics such as production area, raw materials, manufacturing method, etc.
  • evaluation values can be collected and accumulated for each group of red wines that are produced in the same region. Note that it is preferable that the evaluation values collected and accumulated at this time are specified by ranges rather than points.
  • the evaluation scheme according to this modification is aimed at writers and business operators rather than general users.
  • FIG. 52 shows an element evaluation screen 5200 as another example of the element evaluation screen.
  • the element evaluation screen 5200 shows a chart 5210 of one-axis element evaluation (degree of suitability for white fish sashimi). This chart 5210 extends in the vertical direction, unlike the chart 2901 (see FIG. 29), which extends in the horizontal direction.
  • a plurality of icons 5220 are arranged along this chart 5210. Each icon 5220 corresponds to a brand and is composed of a label image of the brand. The position of each icon 5220 represents the element evaluation value of the brand. Further, the positional relationship between the icons 5220 represents the ranking and degree of spread of the element evaluations of the corresponding brands.
  • the element evaluation screen 5200 has a plurality of cards 5230.
  • the plurality of cards 5230 are arranged vertically along the chart 5210.
  • Each card 5230 corresponds to a brand, and shows a label image 5231, brand name 5232, category 5233, production area 5234, and manufacturer 5235.
  • the height of the chart 5210 is adjusted to be approximately equal to the height of cards 5230 arranged vertically for each brand. Therefore, the height of the chart 5210 increases according to the number of target stocks, and the user can perform relative evaluations so that the evaluations do not overlap between stocks.
  • the order in which the plurality of cards 5230 are arranged is changed according to the order in which the icons 5220 are arranged.
  • the user can easily grasp the ranking and spread of the element evaluations of each brand.
  • the user can know the label image, brand name, category, production area, and manufacturer of each brand.
  • the above-mentioned icon 5220 is displayed so as to be operable.
  • the evaluation sending module 613 of the user terminal 402 sends the element evaluation value corresponding to the changed position to the management server 401.
  • the transmitted element evaluation value is acquired by the evaluation management module 512 of the management server 401.
  • the module updates the element evaluation value of the brand stored in the posted evaluation table 1100 with the acquired element evaluation value.
  • evaluation screen 5200 the user can view and re-evaluate all past evaluation results for the evaluation axis. Therefore, a large number of stocks, exceeding several dozen, can be plotted on the screen.
  • the user may create or update a list of top brands for the evaluation axis (such as the top 10 sakes that go well with white meat sashimi) by picking the top brands from the screen, or disclose the list to a third party. You can also do it.
  • chart 5210 is an element evaluation chart, it may be a comprehensive evaluation chart instead of the element evaluation chart.
  • FIG. 53 shows a comprehensive evaluation screen 5300 as another example of the comprehensive evaluation screen.
  • This comprehensive evaluation screen 5300 has a chart 5301 of one axis of comprehensive evaluation (tastiness (in other words, good or bad)).
  • This chart 5301 is composed of an evaluation axis 5302 and five lines 5303A to 5303E (hereinafter collectively referred to as "lines 5303") arranged substantially parallel to the evaluation axis 5302.
  • icons 5304 are arranged on five lines 5303. Each icon 5304 corresponds to a specific brand and represents the label of the brand. The position of each icon 5304 represents the overall evaluation value of the corresponding brand. Further, the vertical positional relationship between the icons 5304 represents the ranking and degree of spread of the overall evaluation of the corresponding brand. Note that a circle number 5305 attached to the upper left of each icon 5304 represents the ranking of the overall evaluation.
  • icons 5304 are assigned to separate lines 5303 in order of overall evaluation value.
  • the icons 5304 with the first to fifth overall evaluation values are distributed and arranged in lines 5303A to 5303E.
  • icons 5304 with comprehensive evaluation values from 6th to 10th are also distributed in lines 5303A to 5303E. Therefore, icons 5304 with similar comprehensive evaluation values are not placed on the same line 5303.
  • Each icon 5304 is displayed so that it can be operated.
  • the user can move each icon 5304 up or down on line 5303.
  • the evaluation transmission module 613 of the user terminal 402 transmits the comprehensive evaluation value corresponding to the changed position to the management server 401.
  • the transmitted comprehensive evaluation value is acquired by the evaluation management module 512 of the management server 401.
  • the module updates the overall evaluation value of the issue stored in the posted issue table 1000 with the obtained overall evaluation value.
  • icons 5304 are arranged on five lines 5303. At this time, each icon 5304 is assigned to a separate line 5303 in order of comprehensive evaluation value. Therefore, icons 5304 with similar comprehensive evaluation values are not placed on the same line 5303. As a result, it becomes easy to check the relative relationships between stocks with similar comprehensive evaluation values and to edit the comprehensive evaluation values.
  • Variations in the conductor leading to the relative evaluation screen will be explained. Variations of conductors include the following: a. Transition to the relative evaluation screen via the new post screen (a) Shoot or enter multiple stocks on the new post screen and relatively evaluate the multiple stocks on the relative evaluation screen (b) Select a single stock on the new post screen A. Take a picture or input the image, and use the relative evaluation screen to perform a relative evaluation between the single stock and the stocks evaluated in the past. Transition to the relative evaluation screen from a specific trigger such as photographing a two-dimensional code. Each variation will be explained below.
  • the user terminal 402 first displays a new posting screen.
  • the user of the user terminal 402 inputs a plurality of brands on this new posting screen.
  • the input method is manual input, photographing a brand label, photographing a two-dimensional code such as a QR code (registered trademark), or detecting an RFID tag.
  • the user can also enter a title and comment on this new post screen.
  • the user terminal 402 transmits the input information to the management server 401 as posted information.
  • the management server 401 Upon receiving the posted information, the management server 401 stores the received posted information in the posted table 900 and the posted brand table 1000. The management server 401 also generates a comprehensive evaluation screen for evaluating the plurality of input brands. For this comprehensive evaluation screen, see, for example, FIGS. 27 and 28. The management server 401 transmits the generated comprehensive evaluation screen to the user terminal 402.
  • the user terminal 402 Upon receiving the comprehensive evaluation screen, the user terminal 402 displays the received comprehensive evaluation screen. The user of the user terminal 402 performs a relative evaluation of the plurality of stocks mentioned above on this comprehensive evaluation screen. Thereafter, the user terminal 402 transmits the comprehensive evaluation value of each brand set by the user to the management server 401. The management server 401 stores the comprehensive evaluation value transmitted from the user terminal 402 in the posted brand table 1000.
  • the above is an explanation of the variation of (A).
  • a comprehensive evaluation screen is provided from the management server 401 to the user terminal 402, but an element evaluation screen may be provided in addition to or in place of this screen.
  • an element evaluation screen see, for example, FIGS. 29 to 31.
  • the user terminal 402 first displays a new posting screen.
  • the user of the user terminal 402 inputs a single brand into this new posting screen.
  • the input method is manual input, photographing a brand label, photographing a two-dimensional code such as a QR code (registered trademark), or detecting an RFID tag.
  • the user can also enter a title and comment on this new post screen.
  • the user terminal 402 transmits the input information to the management server 401 as posted information.
  • the management server 401 Upon receiving the posted information, the management server 401 stores the received posted information in the posted table 900 and the posted brand table 1000. The management server 401 also generates a comprehensive evaluation screen for evaluating the input single brand.
  • the generated comprehensive evaluation screen is a screen for performing a relative evaluation between the input single brand and the brand in the same category that was recently evaluated by the user of the user terminal 402. On this comprehensive evaluation screen, the comprehensive evaluation values of stocks in the same category that were most recently evaluated by the user are plotted based on the posted stocks table 1000. For this comprehensive evaluation screen, see, for example, FIGS. 27 and 28.
  • the management server 401 transmits the generated comprehensive evaluation screen to the user terminal 402.
  • the user terminal 402 Upon receiving the comprehensive evaluation screen, the user terminal 402 displays the received comprehensive evaluation screen. On this comprehensive evaluation screen, the user of the user terminal 402 performs a relative evaluation of the above-mentioned single brand and the brand of the same category that was recently evaluated by the user. Thereafter, the user terminal 402 transmits the comprehensive evaluation value of each brand set by the user to the management server 401.
  • the management server 401 stores the comprehensive evaluation value transmitted from the user terminal 402 in the posted brand table 1000.
  • a comprehensive evaluation screen is provided from the management server 401 to the user terminal 402, but an element evaluation screen may be provided in addition to or in place of this screen.
  • an element evaluation screen see, for example, FIGS. 29 to 31.
  • the user of the user terminal 402 makes a relative evaluation of three brands in a "drink comparison set" at a restaurant.
  • the user of the user terminal 402 photographs a two-dimensional code attached to a menu book of a restaurant.
  • the photographed two-dimensional code is, for example, a QR code (registered trademark), and is a code for requesting the management server 401 for an element evaluation screen for the three brands of the "drink comparison set".
  • the user terminal 402 requests an element evaluation screen from the management server 401.
  • the management server 401 Upon receiving this request, the management server 401 returns an element evaluation screen to the user terminal 402.
  • the user terminal 402 When the user terminal 402 receives the element evaluation screen sent from the management server 401, it displays the received screen. For the displayed element evaluation screens, see, for example, FIGS. 29 to 31. The user of the user terminal 402 performs a relative evaluation of the above three stocks on this element evaluation screen. Thereafter, the user terminal 402 transmits the element evaluation value of each brand set by the user to the management server 401. The management server 401 stores the element evaluation values sent from the user terminal 402 in the posted evaluation table 1100.
  • the management server 401 refers to the tasting scoring table 1200 to obtain scores corresponding to the received element evaluation values for each element and brand.
  • the management server 401 then generates a scoring result screen based on the obtained scores and sends it to the user terminal 402.
  • the scoring result screen (not shown) generated and transmitted here is a screen showing scores for each brand for each element.
  • the user terminal 402 displays the scoring result screen sent from the management server 401.
  • the user of the user terminal 402 can understand his or her own tasting ability by referring to this scoring result screen. By providing such services, restaurants can attract customers.
  • the tasting scoring table 1200 referred to in the above variation A may store any of the following values as a value indicating a correct answer. (1) Values registered by the makers of each brand (2) Average values of evaluation values registered by users who drank each brand (3) Values quantified based on objective measurement methods (4) Tasting issues These values are also stored in the tasting scoring table 1200 referenced in the tasting evaluation sequence 2200 described above, and are referenced as values indicating correct answers. It's okay.
  • the present invention is not limited to the above-described embodiments, and includes various modifications.
  • the embodiments described above are described in detail to explain the present invention in an easy-to-understand manner, and the present invention is not necessarily limited to having all the configurations described.
  • it is possible to replace a part of the configuration of one embodiment with the configuration of another embodiment and it is also possible to add the configuration of another embodiment to the configuration of one embodiment.
  • each of the above-mentioned configurations, functions, processing units, processing means, etc. may be partially or entirely realized by hardware, for example, by designing an integrated circuit.
  • each of the above configurations, functions, etc. may be realized by software by a processor interpreting and executing a program for realizing each function.
  • Information such as programs, tables, files, etc. that realize each function can be stored in a memory, a recording device such as a hard disk, an SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.
  • control lines and information lines are shown to be necessary for explanation purposes, and not all control lines and information lines are necessarily shown in the product. In reality, almost all components may be considered to be interconnected. Note that the above-described embodiments disclose at least the configuration described in the claims.
  • 400...Product evaluation system 401...Management server, 402...User terminal, 511...Posting management module, 512...Evaluation management module, 513...Comprehensive evaluation module, 514...Element evaluation module, 515...First preference grasping module, 516 ...Second preference understanding module, 517...First taste difference understanding module, 518...Second taste difference understanding module, 519...Model generation module, 520...Correction module, 521...First product extraction module, 522...
  • Second product extraction module 523...Scoring module, 524...Transmission module, 531...Master DB, 532...Transaction DB, 533...Analysis data DB, 612...Display module, 613...Evaluation transmission module, 614...Reception module, 621 ...User terminal data

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Abstract

La présente invention concerne un mécanisme qui permet l'utilisation d'évaluations relatives de produits.　L'invention concerne un système d'évaluation de produit caractérisé en ce qu'un serveur de gestion comprend un moyen de gestion qui gère des valeurs d'évaluation qui représentent des évaluations relatives des produits et un moyen de transmission qui transmet les valeurs d'évaluation de la pluralité de produits au terminal utilisateur et un terminal utilisateur comprend un moyen d'affichage qui affiche les valeurs d'évaluation de la pluralité de produits.
PCT/JP2023/011553 2022-03-23 2023-03-23 Système d'évaluation de produit, serveur de gestion, terminal utilisateur et programme WO2023182437A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08235152A (ja) * 1995-02-24 1996-09-13 Matsushita Electric Ind Co Ltd アンケート処理装置
WO2007097423A1 (fr) * 2006-02-27 2007-08-30 Sapporo Breweries Limited Systeme de decision/prise en charge d'un procede de production
JP2016091363A (ja) * 2014-11-06 2016-05-23 東色ピグメント株式会社 商品アイコンの多次元マップを表示する装置、方法及びコンピュータプログラム
JP2018124671A (ja) * 2017-01-30 2018-08-09 株式会社オリサンキュ 情報処理装置、情報処理システム、情報処理方法、及びプログラム
JP2019082781A (ja) * 2017-10-30 2019-05-30 卓 中川 情報提供システム、情報提供方法および情報提供プログラム
JP2021509750A (ja) * 2018-01-05 2021-04-01 コラヴァン，インコーポレイテッド アイテムとモーメントとの間の関係を特徴付け、決定するための方法及び装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08235152A (ja) * 1995-02-24 1996-09-13 Matsushita Electric Ind Co Ltd アンケート処理装置
WO2007097423A1 (fr) * 2006-02-27 2007-08-30 Sapporo Breweries Limited Systeme de decision/prise en charge d'un procede de production
JP2016091363A (ja) * 2014-11-06 2016-05-23 東色ピグメント株式会社 商品アイコンの多次元マップを表示する装置、方法及びコンピュータプログラム
JP2018124671A (ja) * 2017-01-30 2018-08-09 株式会社オリサンキュ 情報処理装置、情報処理システム、情報処理方法、及びプログラム
JP2019082781A (ja) * 2017-10-30 2019-05-30 卓 中川 情報提供システム、情報提供方法および情報提供プログラム
JP2021509750A (ja) * 2018-01-05 2021-04-01 コラヴァン，インコーポレイテッド アイテムとモーメントとの間の関係を特徴付け、決定するための方法及び装置

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