Taste preference grasping method, food and drink providing method, food and drink proposing method, restaurant proposing method, dating candidate proposing method, taste preference grasping program, food and drink proposing program, restaurant proposing program, dating candidate proposing program, and food and drink set for grasping taste preference

Abstract

To easily know a tendency of a taste preference of each person.SOLUTION: A method for knowing a taste preference of the present invention includes at least, evaluation (S11) and derivation (S12). At least three types of food or drink models are evaluated by a person or by a device, and an evaluation score for a taste that a user gives for each food or drink model is obtained. Concentration of salt in each of the at least three types of food or drink models is different. A taste preference score and / or a taste preference class of the user is derived by a person or by a device. In the derivation, at least the concentration of the salt in the food or drink models and the taste evaluation scores can be used.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a method for determining taste preferences, a method for providing food and beverages, a method for suggesting food and beverages, a method for suggesting restaurants and dating candidates, a program for determining taste preferences, a program for suggesting food and beverages, a program for suggesting restaurants and dating candidates, and a food and beverage set for determining taste preferences. [Background technology]

[0002] Individuals have diverse taste preferences (also referred to as "taste preferences" in this article). Taste preferences are shaped by various factors, including gender, upbringing, and eating experience. Non-Patent Document 1 discloses that (1) gender differences determine the framework for taste preferences, and (2) diversifying eating habits change this framework.

[0003] Taste preferences affect not only the choice of food and drink and the choice of restaurant, but also human relationships. One of the most common arguments between couples is over the seasoning of food. For example, if one spouse prefers strong flavors and the other prefers lighter flavors, the relationship can deteriorate due to such differences in taste. In other words, one factor that supports a marital relationship is having matching taste preferences.

[0004] One method for evaluating taste preferences is analytical evaluation. Only a few analytical evaluation methods are known, but examples are as follows. Patent Document 1 discloses a method for evaluating preference phenotypes. The purpose of this evaluation method is to accurately grasp the preference phenotypes of individuals and groups. This evaluation method uses multiple model foods. Each model food is defined by a combination of one taste element and one flavor element. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Publication No. 2017-147967 [Non-patent literature]

[0006] [Non-Patent Document 1] Taguchi Takuko, Okamoto Yoko, "Study of high school students' taste preferences using multivariate analysis (Part 1)," Cooking Science, Vol. 21 (1988), No. 4, pp. 279-289 Summary of the Invention [Problem to be solved by the invention]

[0007] The problems that this invention aims to solve are to easily grasp each person's taste preference trends (hereinafter referred to as "taste preferences") (hereinafter referred to as "first perspective"), to easily select foods, drinks, or restaurants with similar taste preferences (hereinafter referred to as "second perspective"), and to easily select dating candidates with similar taste preferences (hereinafter referred to as "third perspective"). [Means for solving the problem]

[0008] Based on the above, the inventors of the present application have conducted extensive research and discovered that taste preferences for various foods can be predicted by evaluating preferences for salt concentrations of 3 or more. Based on this finding, the present invention is defined as follows.

[0009] <First Aspect> A taste preference grasping method comprises at least evaluation and derivation. At least three or more model foods and beverages are evaluated by a person or a device, and the result is a user's taste evaluation score for each model food and beverage. Here, the three or more model foods and beverages have different salt concentrations. Next, the person or device derives the user's taste preference score and / or taste preference class, and at least the salt concentrations and the taste evaluation scores of the model foods and beverages are used in this process.

[0010] The processing executed by the computer using the taste preference grasping program is at least input and output. The input to the computer is the user's taste evaluation data for each model food or beverage. The output from the computer is the user's taste preference score data and / or taste preference class data. To do this, the computer references at least the salt concentration data and the taste evaluation data for the model food or beverage.

[0011] The food and beverage set for assessing taste preferences comprises at least three or more foods and beverages. The three or more foods and beverages contain at least salt. The three or more foods and beverages have different salt concentrations.

[0012] <Second Aspect> A food and drink serving method comprises at least designation and serving. What is designated by a person or a device is a taste preference score and / or a taste preference class. Next, what is served by a person or a device is a food or drink. What is associated with the food or drink is the designated taste preference score and / or taste preference class.

[0013] The food and beverage suggestion method comprises at least a designation and a suggestion. A person or a device designates a taste preference score and / or a taste preference class. Then, a person or a device suggests a food or beverage. The designated taste preference score and / or taste preference class is associated with the food or beverage.

[0014] The restaurant suggestion method comprises at least a designation and a suggestion. A person or a device designates a taste preference score and / or a taste preference class. Next, a person or a device suggests a restaurant. The restaurant is associated with the designated taste preference score and / or taste preference class.

[0015] The processing executed by the computer by the food and drink recommendation program is at least input and output. What is input to the computer is taste preference score data and / or taste preference class data. What is output by the computer is food and drink data. What is associated with the food and drink data is the input taste preference score data and / or taste preference class.

[0016] The processing executed by the computer by the restaurant recommendation program is at least input and output. What is input to the computer is taste preference score data and / or taste preference class data. What is output by the computer is restaurant data. What is associated with the restaurant data is the input taste preference score data and / or taste preference class.

[0017] <Third Aspect> The method for proposing dating candidates comprises at least designation and proposal. A person or device designates a taste preference score and / or taste preference class. Next, a person or device proposes a dating candidate. Associated with the dating candidate is the designated taste preference score and / or taste preference class.

[0018] The processing executed by the computer by the dating candidate suggestion program is at least input and output. What is input to the computer is taste preference score data and / or taste preference class data. What is output by the computer is dating candidate suggestion data. What is associated with the dating candidate suggestion data is the input taste preference score data and / or taste preference class. [Effects of the Invention]

[0019] What the present invention makes possible at least is the simple and easy understanding of each person's taste preferences. [Brief explanation of the drawings]

[0020] [Figure 1]FIG. 1 shows the flow of the taste preference determination method of this embodiment. [Figure 2] Figure 2 shows an example of an evaluation form. [Figure 3] Figure 3 shows the results of the cluster analysis. [Figure 4] Figure 4 shows an example of an evaluation form. [Figure 5] Figure 5 shows the analysis results of each subject's taste evaluation score for ramen. [Figure 6] Figure 6 shows the analysis results of each subject's taste evaluation scores for soba noodles. [Figure 7] Figure 7 shows the analysis results of the taste evaluation scores of each subject for the beef bowl toppings. [Figure 8] FIG. 8 shows the flow of the food and drink providing method of this embodiment. [Figure 9] FIG. 9 shows the flow of the food and drink suggestion method of this embodiment. [Figure 10] FIG. 10 shows the flow of the restaurant suggestion method according to this embodiment. [Figure 11] FIG. 11 shows the flow of the method for proposing dating candidates according to this embodiment. [Figure 12] FIG. 12 shows the flow of processing of the taste preference grasping program of this embodiment. [Figure 13] FIG. 13 shows an example of an input screen according to this embodiment. [Figure 14] FIG. 14 shows an example of an output screen according to this embodiment. [Figure 15] FIG. 15 shows the flow of processing of the food and drink suggestion program of this embodiment. [Figure 16] FIG. 16 shows the structure of the food and drink database of this embodiment. [Figure 17] FIG. 17 shows the flow of processing of the restaurant recommendation program according to this embodiment. [Figure 18] FIG. 18 shows the configuration of the restaurant database of this embodiment. [Figure 19] FIG. 19 shows the processing flow of the association candidate suggestion program of this embodiment. [Figure 20] FIG. 20 shows the configuration of the dating candidate database of this embodiment. [Figure 21] FIG. 21 shows the analysis results of taste evaluation scores (when using model foods) for soba noodles by each subject. [Figure 22] FIG. 22 shows the analysis results of the taste evaluation scores (using model foods) of each subject for the beef bowl ingredients. DETAILED DESCRIPTION OF THE INVENTION

[0021] <How to understand taste preference> Figure 1 shows the flow of the taste preference assessment method of this embodiment (hereinafter referred to as "this taste preference assessment method"). This taste preference assessment method consists of evaluation (S11) and derivation (S12). The details of these are as follows.

[0022] <Evaluation (S11)> The model foods and beverages that are rated by the user are rated 3 or higher. This evaluation results in a taste evaluation score for each model food and beverage. Details of the model foods and beverages will be described later.

[0023] <Derivation (S12)> The user's taste preference score and / or taste preference class are derived by a person or a device. The salt concentration of the model food or beverage and the user's taste evaluation score for each model food or beverage obtained in the evaluation (S11) are used to derive the user's taste preference score. These variables are substituted into a regression equation, which will be described later.

[0024] <Terminology> (1) A taste evaluation score is a score that quantifies a taste evaluation. (2) Taste preference score is a score that quantifies taste preference. (3) A taste preference class is a classification whose classification criterion is taste preference.

[0025] <Model food and drink and model food and drink set> A model food or beverage is a food or beverage that contains at least salt and whose purpose is to assess taste preferences. A model food or beverage set is a combination of three or more model food or beverages whose purpose is to assess taste preferences. The main component of salt is sodium chloride. Examples of salt include table salt (refined salt), sea salt, rock salt, lake salt, solar salt, roasted salt, seaweed salt, and flake salt. The number of model food or beverage items is at least three or more, preferably four or more, and more preferably five or more. The salt concentrations of the model food or beverage items are different from each other. The salt concentrations of the model food or beverage items are not particularly limited, but are preferably as follows: The lowest concentration is 0.13%. If the salt concentration is less than 0.13%, the taste is weak and it is difficult to detect the difference. The highest concentration is 3.00%. If the salt concentration is more than 3.00%, the taste is too strong and it is difficult to detect the difference. The difference in salt concentration between the model foods and beverages is not particularly limited as long as the difference in taste is noticeable, but is preferably 0.06% or more, more preferably 0.13% or more, and most preferably 0.20% or more. In addition to sodium chloride, the model foods and beverages may contain consommé, bouillon, soup stock, dashi, etc.

[0026] <Salt concentration> The method for determining the salt concentration is not limited. The value used here may be various, specifically, the amount of salt, the amount of sodium, the amount of chloride ions, or a combination thereof.

[0027] <How to create a regression equation> The objective variable of the regression equation is the user's taste preference score or taste preference class. The explanatory variable of the regression equation is the user's taste evaluation score for each model food or beverage. The regression equation is created as follows.

[0028] <Evaluator's rating> The model foods and beverages are scored by the evaluators as 3 or higher. This scoring results in a taste evaluation score for each model food and beverage.

[0029] <Evaluator classification> The person or device that performs the classification is the rater. Here, the rater is the user who performed the scoring. At least the salt concentration of the model food or beverage and the taste evaluation score obtained by the rater's scoring are used in the classification. This classification results in taste preference clusters. The raters are classified hierarchically. The classification method is not particularly limited, but examples include the Ward method (also known as the minimum variance method), the single link method (also known as the shortest distance method), the complete link method (also known as the longest distance method), the group average method, the centroid method (also known as the center of gravity method), the weighted average method, and the median method. The Ward method and the single link method are preferred. The number of taste preference clusters is 2 or more, preferably 3 or more, more preferably 4 or more, and most preferably 5 or more.

[0030] <Points awarded> Scores are assigned to the taste preference clusters obtained by the evaluator classification. Scores are assigned by a person or a device. Scores are assigned in a gradual or stepped manner, and the scores are based on the taste evaluation scores of the raters belonging to each taste preference cluster for each model food or beverage. Specifically, in this example, a taste preference cluster in which a relatively high salt concentration is preferred is assigned a relatively high score, while a cluster in which a relatively low salt concentration is preferred is assigned a relatively low score, although the reverse is also acceptable.

[0031] <Regression analysis> The regression equation is created using at least the taste evaluation scores for each model food and beverage obtained by the evaluators' scoring and the scores for the taste preference clusters obtained by assigning scores. The regression equation requires multiple explanatory variables. There are no particular limitations on the method for obtaining the regression equation, but examples include multiple regression analysis (least squares method) and multiple regression analysis (stepwise method).

[0032] <Supporting Tests> If taste evaluations of model foods and beverages can be obtained, taste preferences for general foods and beverages can also be understood. This point is supported by the following experiment.

[0033] <Preparation of model solution> Model solutions 1 to 5 were prepared. Each model solution is an example of a model food or beverage. Table 1 shows the amounts of ingredients and salt equivalents in each model solution. Model solution 1 was formulated with commercially available consomme (Ajinomoto KK Consomme, manufactured by Ajinomoto Co., Inc., salt equivalent: 47.1%) and water. Model solutions 2 to 5 were formulated with commercially available consomme (Ajinomoto KK Consomme, manufactured by Ajinomoto Co., Inc., salt equivalent: 47.1%), commercially available salt, and water. The total weight of each model solution was made the same, 20.00 g.

[0034] [Table 1]

[0035] <Evaluation of model liquid> Each model liquid was evaluated by 53 employees of Kagome Co., Ltd. (hereinafter referred to as "subjects"). First, the subjects drank model liquids 1 to 5. These model liquids were assigned three-digit numbers in random order. This resulted in an irregular order in the evaluation of the model liquids. Next, the subjects judged their preference for the model liquids.

[0036] FIG. 2 shows an example of an evaluation form. Written on the form 10 are the model liquid number 11 and a number line 12. One end 12a of the number line indicates "dislike," and the other end 12b of the number line indicates "like." The subject fills in a line 13 somewhere on the number line. In other words, this line 13 indicates the subject's evaluation of the model liquid.

[0037] <Quantification of evaluation of model liquid> The evaluation of each model liquid was quantified. The score set at one end of the number line, "dislike," was "0." The score set at the other end, "like," was "100." In other words, the score indicated by the filled-in line ranged from "0" to "100." In this way, a taste evaluation score was obtained for each model liquid.

[0038] <Cluster analysis> Figure 3 shows the results of the cluster analysis. The taste evaluation scores of each subject for each model liquid were subjected to cluster analysis (Ward method). The software used for the cluster analysis was JMP14.0 (SAS Institute Japan). The variables used in this analysis were the taste evaluation scores of model liquids 1 to 5. Five clusters 22 were obtained by classifying the subjects 21. Specifically, subjects in cluster 5 were those who most preferred the model liquid with a high salt concentration and / or those who least preferred the model liquid with a low salt concentration. Subjects in cluster 4 were those who second most preferred the model liquid with a high salt concentration and / or those who second least preferred the model liquid with a low salt concentration. Subjects in cluster 3 were those who least preferred the model liquid with a high salt concentration and / or those who most preferred the model liquid with a low salt concentration. Those who were classified into Cluster 2 were those who had the second least preference for the model solution with a high salt concentration and / or those who had the second most preference for the model solution with a low salt concentration. Those who were classified into Cluster 1 were those who did not fit into either of these categories.

[0039] <Giving points to clusters> A score was assigned to each cluster. The five clusters were ranked. The ranking was based on the taste evaluation scores of the model liquid by the subjects belonging to each cluster. As a result of the ranking, the five clusters were assigned scores ranging from 0 to 100. Specifically, the score assigned to Cluster 5 was "100." The score assigned to Cluster 4 was "75." The score assigned to Cluster 1 was "50." The score assigned to Cluster 3 was "0." The score assigned to Cluster 2 was "25."

[0040] <Multiple regression analysis> The software used for the multiple regression analysis (least squares method) was JMP14.0 (manufactured by SAS Institute Japan). The dependent variables used were the scores assigned to the five clusters. On the other hand, the explanatory variables used were the taste evaluation scores for each model liquid. As a result of the multiple regression analysis, a regression equation was obtained.

[0041] <Understanding taste preference scores> The taste preference score of each subject was determined. Specifically, to obtain the taste preference score of each subject, the taste evaluation score of the model liquid of that subject was substituted into the regression equation.

[0042] <Understanding taste preference classes> The taste preference classes of the subjects were determined. After determining each subject's taste preference score, the subjects were classified as follows: Subjects who had a taste preference score of less than 25 were classified into the "ultra-light taste preference group." Subjects who had a taste preference score of between 25 and less than 50 were classified into the "slightly light taste preference group." Subjects who had a taste preference score of between 50 and less than 75 were classified into the "slightly strong taste preference group." Subjects who had a taste preference score of 75 or more were classified into the "ultra-strong taste preference group."

[0043] Hereinafter, these groups will be collectively referred to as the "taste preference group." One of these groups will be referred to as the "each taste preference group." The "ultra-light taste preference group" and the "slightly light taste preference group" will be collectively referred to as the "light taste preference group." The "ultra-strong taste preference group" and the "slightly strong taste preference group" will be collectively referred to as the "strong taste preference group."

[0044] <Preparing Ramen> Ramen 1 to 5 were prepared. Ramen soup was prepared. Table 2 shows the amount of ingredients (used) and the amount of salt equivalent in the ramen soup for each ramen. (The amount of salt equivalent derived from the noodles is excluded.) For the ramen soup for Ramen 1 to 5, powdered soup from commercially available instant noodles (Sapporo Ichiban Shio Ramen, manufactured by Sanyo Foods Co., Ltd.) and water were blended. Next, dried noodles from the same commercially available instant noodles were boiled. Each ramen soup and the boiled noodles were mixed. In this way, Ramen 1 to 5 were obtained.

[0045] [Table 2]

[0046] <Ramen review> Sixteen of the 53 subjects mentioned above rated ramen 1 to 5. Specifically, four people were randomly selected from each of the taste preference groups: "very light taste preference group," "slightly light taste preference group," "slightly strong taste preference group," and "very strong taste preference group." First, the 16 subjects ate ramen 1 to 5. These ramen were assigned three-digit numbers in no particular order. This makes the ramen evaluation order irregular. Next, the 16 subjects evaluated the taste of the ramen.

[0047] FIG. 4 shows an example of an evaluation form. Written on the form 30 are the ramen number 31 and a check box 32. The leftmost check box 32a indicates "I really dislike it," and the rightmost check box 32b indicates "I really like it." The subject writes a check mark 33 (for example, a tick) in either check box. In other words, the checked check box indicates the subject's evaluation of the ramen's taste.

[0048] <Quantifying ramen evaluations> The evaluation of each ramen was quantified. The scores assigned to the check boxes "Really dislike," "Dislike," "Slightly dislike," "Slightly dislike," "Average," "Slightly like," "Slightly like," "Like," and "Really like" were "1," "2," "3," "4," "5," "6," "7," "8," and "9," respectively. This gave a taste evaluation score for each ramen.

[0049] <Analysis of ramen taste evaluation scores> The taste evaluation scores of ramen were analyzed. The average taste evaluation scores of the group that prefers light ramen flavors were calculated. The average taste evaluation scores of the group that prefers strong ramen flavors were also calculated. The taste evaluation scores were standardized because the values ​​of the taste evaluation scores between groups do not necessarily match.

[0050] Figure 5 shows the analysis results of the taste evaluation scores for ramen by taste preference group. The results show that (1) Ramen 2 had the highest taste evaluation score (standardized) among the light-flavor preference group 41, (2) Ramen 4 had the highest taste evaluation score (standardized) among the strong-flavor preference group 42, (3) Ramen 5 had the lowest taste evaluation score (standardized) among the light-flavor preference group 41, and (4) Ramen 1 had the lowest taste evaluation score (standardized) among the strong-flavor preference group 42. In other words, the taste preference for the model liquid and the taste preference for the actual menu item "ramen" are approximately the same. In other words, by evaluating the model liquid, it is possible to predict the taste preference for "ramen" without evaluating the actual menu item "ramen."

[0051] <Preparing soba noodles> Soba noodles 1 to 5 were prepared. The sauce was prepared. Table 3 shows the amount of ingredients used and the salt equivalent amount in each soba noodle soup (excluding the amount of salt derived from the soba noodles). The sauce for Soba noodles 1 was made with a commercially available mentsuyu (1000 mL of mentsuyu, manufactured by Yamaki Co., Ltd.) and water. The sauce for Soba noodles 2 to 5 was made with the same commercially available mentsuyu, water, and salt. Next, commercially available frozen soba noodles (stone-ground Japanese soba noodles, manufactured by TableMark Co., Ltd.) were boiled. Each sauce and the boiled soba noodles were mixed together. As a result, soba noodles 1 to 5 were obtained.

[0052] [Table 3]

[0053] <Soba evaluation> Twenty of the 53 subjects rated soba noodles 1 to 5. Four or more people were randomly selected from each of the taste preference groups: "very light taste preference group," "slightly light taste preference group," "slightly strong taste preference group," and "very strong taste preference group." The method for evaluating soba noodles was the same as that for ramen noodles.

[0054] <Quantifying the taste evaluation of soba noodles> The taste of each soba was quantified. The method for obtaining a taste score of 1 to 5 for soba was the same as that for ramen.

[0055] <Analysis of soba taste evaluation scores> Figure 6 shows the analysis results of each subject's taste evaluation score for soba noodles. The method used to analyze the taste evaluation scores was the same as that used for ramen noodles. The results show that (1) soba noodles 2 had the highest standardized taste evaluation score in the light-flavor preference group 51, (2) soba noodles 3 had the highest standardized taste evaluation score in the strong-flavor preference group 52, (3) soba noodles 5 had the lowest standardized taste evaluation score in the light-flavor preference group 51, and (4) soba noodles 1 had the lowest standardized taste evaluation score in the strong-flavor preference group 52. In other words, the taste preference for the model liquid and the taste preference for the actual menu item "soba noodles" are approximately the same. In other words, by evaluating the model liquid, it is possible to predict the taste preference for "soba noodles" without evaluating the actual menu item "soba noodles."

[0056] <Preparing beef bowl ingredients> Beef bowl ingredients 1 to 5 were prepared. Beef bowl ingredient sauces were prepared. Table 4 shows the amount of ingredients (used) and the salt equivalent in each beef bowl ingredient sauce. For beef bowl ingredient 1 sauce, a commercially available beef bowl seasoning (Donburi Kuitei Gyudon no Moto, manufactured by Ebara Corporation) and water were blended. For beef bowl ingredients 2 to 5 sauces, the same commercially available beef bowl seasoning, water, and salt were blended. Each sauce, 25g of beef, and 25g of onion were mixed and heated. As a result, beef bowl ingredients 1 to 5 were obtained.

[0057] [Table 4]

[0058] <Evaluation of beef bowl ingredients> Of the 53 subjects mentioned above, 20 people evaluated beef bowl toppings 1 to 5. Four or more people were randomly selected from each taste preference group: "very light taste preference group," "slightly light taste preference group," "slightly strong taste preference group," and "very strong taste preference group." The method for evaluating beef bowl toppings was the same as that for ramen.

[0059] <Quantifying the taste evaluation of beef bowl toppings> The taste evaluation of each beef bowl topping was quantified. The method for obtaining a taste evaluation score of 1 to 5 for beef bowl toppings was the same as that for ramen.

[0060] <Analysis of taste evaluation scores for beef bowl toppings> Figure 7 shows the analysis results of the taste evaluation scores of each subject for the beef bowl toppings. The method used to analyze the taste evaluation scores was the same as that used for ramen. The results show that (1) beef bowl topping 2 had the highest standardized taste evaluation score in the light-flavor preference group 61, (2) beef bowl topping 5 had the highest standardized taste evaluation score in the strong-flavor preference group 62, (3) beef bowl toppings 1 and 5 had the lowest standardized taste evaluation scores in the light-flavor preference group 61, and (4) beef bowl topping 1 had the lowest standardized taste evaluation score in the strong-flavor preference group 62. In other words, the taste preference for the model liquid and the taste preference for the actual menu item "beef bowl topping" are approximately the same. In other words, by evaluating the model liquid, it is possible to predict the taste preference for "beef bowl topping" without evaluating the actual menu item "beef bowl topping."

[0061] It is speculation, but the reason why the taste evaluation score (standardized) of beef bowl ingredient 1 was low in the light-flavor preference group is that the salt equivalent of beef bowl ingredient 1 was too low. In other words, the salt equivalent of beef bowl ingredient 1, "approximately 0.14%," probably did not achieve the minimum flavor intensity required to feel satisfied with the meal.

[0062] <Food and beverage provision method> Figure 8 shows the flow of the food and drink providing method of this embodiment (hereinafter referred to as "the food and drink providing method"). The steps that make up the food and drink providing method are designation (S21) and provision (S22). These steps are explained in detail as follows.

[0063] <Designation (S21)> The user or the device specifies the user's taste preference score and / or taste preference class. The method for determining these indices is the same as the taste preference determination method (S11, S12) described above.

[0064] <Provided (S22)> Food and beverages are provided by a user or device. The number of food and beverages provided is not limited and can be 0, 1, or 2 or more. A number of "0" provided means that there is no applicable item. The specified taste preference score and / or taste preference class are associated with the food and beverage. A correspondence table is referenced when providing the food and beverage. This correspondence table shows the relationship between the food and beverage and the taste preference score and / or taste preference class. The manner in which the food and beverage is provided may be any known manner, and is not particularly limited, but examples include face-to-face delivery, mail delivery, home delivery, and display.

[0065] <Food and beverages> Food and beverages refer to beverages, foods, or a combination of these that contain at least salt. The salt concentration of food and beverages is 0.20% or more. If the salt concentration of food and beverages is less than 0.20%, the food and beverage will not be evaluated, regardless of taste preference. Examples of food and beverages include noodles, rice bowls, and soups.

[0066] <Noodles> Noodles refer to foods (or meals) whose main ingredient is noodles. Examples of noodles include udon, soba, somen, hiyamugi, Chinese noodles, pasta, gyoza, shumai, wonton, etc., with udon, soba, somen, hiyamugi, Chinese noodles, and pasta being preferred.

[0067] <Rice bowls> Rice bowls are foods (or meals) in which rice and ingredients are served in a bowl. Examples of rice bowls include beef bowls, pork bowls, oyakodon, egg bowls, katsudon, Chinese rice bowls, tempura bowls, eel bowls, and seafood bowls, with beef bowls, pork bowls, oyakodon, egg bowls, katsudon, and Chinese rice bowls being preferred.

[0068] <Soup> Soup is a food (or meal) (excluding noodle dishes) in which ingredients are submerged in soup. Examples of soup include miso soup, pork soup, kenchin soup, clear soup, shio soup, chilled soup, soup, hot pot, etc.

[0069] <Food and beverage proposal method> Figure 9 shows the flow of the food and drink recommendation method of this embodiment (hereinafter referred to as "this food and drink recommendation method"). The steps that make up this food and drink recommendation method are designation (S31) and recommendation (S32). These steps are explained in detail as follows.

[0070] <Specification (S31)> The user or the device specifies the user's taste preference score and / or taste preference class. The method for determining these indices is the same as the taste preference determination method (S11, S12) described above.

[0071] <Proposal (S32)> Food and beverages are suggested by the user or device. The number of suggested food and beverages is not limited and can be 0, 1, or 2 or more. A suggestion number of "0" means that there is no match. The specified taste preference score and / or taste preference class are associated with the food and beverage. A correspondence table is referenced when suggesting food and beverages. This correspondence table shows the relationship between the food and beverages and the taste preference score and / or taste preference class. The manner in which food and beverages are suggested may be any known manner, and is not particularly limited, but examples include face-to-face, in writing, by telephone, by email, via the Internet, SNS, etc. The explanation of food and beverages is as described above.

[0072] <Restaurant proposal method> Figure 10 shows the flow of the restaurant suggestion method of this embodiment (hereinafter referred to as "this restaurant suggestion method"). The steps that make up this restaurant suggestion method are designation (S41) and suggestion (S42). These steps are explained in detail as follows.

[0073] <Designation (S41)> The user or the device specifies the user's taste preference score and / or taste preference class. The method for determining these indices is the same as the taste preference determination method (S11, S12) described above.

[0074] <Proposal (S42)> Restaurants are suggested by the user or device. The number of suggested restaurants is not limited and can be 0, 1, or 2 or more. A suggestion number of "0" means that there is no match. The restaurant is associated with the specified taste preference score and / or taste preference class. A correspondence table is referenced when suggesting restaurants. This correspondence table shows the relationship between restaurants and taste preference scores and / or taste preference classes. The method of suggesting restaurants may be any known method, and is not particularly limited, but examples include face-to-face, in writing, by telephone, by email, the Internet, and SNS.

[0075] <Restaurant> A restaurant is a store that serves food and drinks. Examples of restaurants include ramen shops, soba shops, Japanese restaurants, beef bowl restaurants, Italian restaurants, Chinese restaurants, and soup stands.

[0076] <How to propose dating candidates> Figure 11 shows the flow of the method for proposing a dating candidate according to this embodiment (hereinafter referred to as "this dating candidate proposing method"). The steps that make up this dating candidate proposing method are designation (S51) and proposal (S52). These steps are explained in detail as follows:

[0077] <Designation (S51)> The user or the device specifies the user's taste preference score and / or taste preference class. The method for determining these indices is the same as the taste preference determination method (S11, S12) described above.

[0078] <Proposal (S52)> The user or device proposes dating candidates. The number of proposed dating candidates is not important and can be 0, 1, or 2 or more. A proposal number of "0" means that there is no match. The specified taste preference score and / or taste preference class are associated with the dating candidate. A correspondence table is referenced when proposing dating candidates. This correspondence table shows the relationship between dating candidates and taste preference scores and / or taste preference classes. The method of proposing dating candidates may be any known method, and is not particularly limited, but examples include face-to-face, written, telephone, email, the Internet, SNS, etc.

[0079] <Dating candidate> A dating candidate is a natural person with the possibility of dating. Dating refers to a relationship between people. The purpose of the relationship is not important, and examples include friendship, romantic relationship, and marriage. However, if what is required of a dating candidate is a match in tastes and preferences, the purpose of the relationship is often marriage.

[0080] <Taste preference assessment program> 12 shows the processing flow of the taste preference grasping program of this embodiment (hereinafter referred to as "this taste preference grasping program"). The taste preference grasping program causes a computer to execute input (S61), derivation (S62), and output (S63). Details of these processes will be described later.

[0081] <Computer> A computer is a data processing device. A computer generally comprises an input / output port, a processing device, a program storage device (e.g., HDD, SSD, ROM, etc.), and a temporary storage device (e.g., RAM, etc.). Connected to the input / output port are an input device (e.g., keyboard, mouse, etc.) and an output device (e.g., display, speaker, etc.), or an input / output device (e.g., touch panel, etc.). Various programs are stored in the program storage device. Various deployed programs and various input / output data are stored in the temporary storage device.

[0082] <Input (S61)> Taste evaluation data is input by the computer. The taste evaluation data indicates an evaluation of the taste of each model food or beverage. At this time, an input screen is displayed by the computer. This display screen prompts the user to input the user's evaluation of the taste of the model food or beverage. Once the taste evaluation is input, the taste evaluation data is stored in a temporary storage device.

[0083] FIG. 13 shows an example of an input screen according to this embodiment. Input screen 70 is configured with model food or beverage name 71 and drop box 72. Model food or beverage names 71a, 71b, and 71c correspond to drop boxes 72a, 72b, and 72c, respectively. Model food name 71 indicates the model food or beverage eaten by the user. Drop box 72 provides options. The options indicate evaluations of the model food or beverage, such as "like," "somewhat like," "average," "somewhat dislike," and "dislike." For example, if the food or beverage eaten by the user is model food or beverage 1, drop box 72a is operated to select an evaluation of the taste of model food or beverage 1.

[0084] <Derivation (S62)> When taste evaluation data is input, the computer derives a taste preference score or a taste preference class. At that time, the computer refers to the input taste evaluation data. In the derivation, the taste evaluation is first quantified. Then, the quantified taste evaluation is subjected to regression analysis. The descriptions in the above-mentioned <Quantification of evaluation of model liquid> column, <Cluster analysis> column, <Scoring of clusters> column, and <Multiple regression analysis> column are useful for creating such quantification and regression analysis algorithms.

[0085] <Output (S63)> The computer outputs the derived taste preference score data and / or taste preference class data. Here, the taste preference score data indicates the user's taste preference score. Also, the taste preference class data indicates the user's taste preference class. At this time, the computer displays an output screen.

[0086] 14 shows an example of an output screen of this embodiment. An output screen 80 is made up of item names 81 and display boxes 82. Each item name 81 indicates a taste preference score 81a and a taste preference class 81b. Display box 82a displays the user's taste preference score. Display box 82b displays the user's taste preference class.

[0087] <Food and Beverage Proposal Program> FIG. 15 shows the processing flow of the food and drink recommendation program of this embodiment (hereinafter referred to as "this food and drink recommendation program"). This food and drink recommendation program causes the computer to execute input (S71), search (S72), and output (S73). The computer has been described above. Details of these processes are as follows.

[0088] <Input (S71)> The taste preference score data and / or taste preference class data are input by the computer. The taste preference score data indicates the user's taste preference score. The taste preference class data indicates the user's taste preference class. The method for determining the taste preference score and / or taste preference class is the same as the taste preference determination method (S11, S12) or taste preference determination program (S61 to S63) described above.

[0089] <Search (S72)> The computer searches the output food and drink data, and at that time, the computer refers to the input taste preference score data and / or taste preference class data, and the food and drink database.

[0090] 16 shows the configuration of the food and drink database of this embodiment. This food and drink database 90 shows the relationship between food and drink ID 91, taste preference score 92, taste preference class 93, and food and drink details (preferably a pointer to another database) 94. Examples of the contents of the food and drink details include name, price, and a photo of the appearance.

[0091] <Output (S73)> The computer outputs food and drink data. Associated with the food and drink data is the taste preference score data and / or taste preference class data input in S71. The food and drink data indicates details of the food and drink (such as name, price, and exterior photo).

[0092] <Restaurant Proposal Program> FIG. 17 shows the processing flow of the restaurant recommendation program of this embodiment (hereinafter referred to as "this restaurant recommendation program"). This restaurant recommendation program causes a computer to execute input (S81), search (S82), and output (S83). The computer has been described above. Details of these processes are as follows.

[0093] <Input (S81)> The taste preference score data and / or taste preference class data are input by the computer. The taste preference score data indicates the user's taste preference score. The taste preference class data indicates the user's taste preference class. The method for determining the taste preference score and / or taste preference class is the same as the taste preference determination method (S11, S12) or taste preference determination program (S61 to S63) described above.

[0094] <Search (S82)> The computer searches the output restaurant data, and at that time, the computer refers to the input taste preference score data and / or taste preference class data, and the restaurant database.

[0095] 18 shows the configuration of a restaurant database according to this embodiment. This restaurant database 100 shows restaurant ID 101, taste preference score 102, taste preference class 103, and restaurant details (preferably a pointer to another database) 104. Examples of restaurant details include the name, address, business hours, telephone number, menu, appearance, and rating.

[0096] <Output (S83)> What is output by the computer is restaurant data. Associated with the restaurant data is the taste preference score data and / or taste preference class data input in S81. The restaurant data indicates restaurant details (such as name, address, business hours, phone number, menu, appearance, and rating). Associated with the output restaurant data are ratings by other users. The taste preference score and / or taste preference class of the input user matches or is close to the taste preference score and / or taste preference class of the other users.

[0097] <Dating candidate suggestion program> Figure 19 shows the processing flow of the dating candidate suggestion program of this embodiment (hereinafter referred to as "this dating candidate suggestion program"). This dating candidate suggestion program causes the computer to execute input (S91), search (S92), and output (S93). The computer is described above. Details of these processes are as follows.

[0098] <Input (S91)> The taste preference score data and / or taste preference class data are input by the computer. The taste preference score data indicates the user's taste preference score. The taste preference class data indicates the user's taste preference class. The method for determining the taste preference score and / or taste preference class is the same as the taste preference determination method (S11, S12) or taste preference determination program (S61 to S63) described above.

[0099] <Search (S92)> The computer searches for the output dating candidate data, and references the input taste preference score data and / or taste preference class data, and the dating candidate database.

[0100] 20 shows the configuration of the dating candidate database of this embodiment. This dating candidate database 110 shows a dating candidate ID 111, a taste preference score 112, a taste preference class 113, and dating candidate details (preferably a pointer to another database) 114. Examples of the contents of the dating candidate details include name, place of residence, occupation, contact information, appearance, and gender.

[0101] <Output (S93)> The computer outputs the relationship candidate data. The taste preference score data and / or taste preference class data entered in S91 are associated with the relationship candidate data. The relationship candidate data indicates the relationship candidate's details (such as name, place of residence, occupation, contact information, appearance, and gender). In other words, the user's taste preferences match or are similar to the relationship candidate's taste preferences.

[0102] <Effects of this embodiment> The effects of this embodiment are as follows: Namely, each person's taste preferences can be easily grasped. As a result, foods and drinks or restaurants with similar taste preferences can be easily selected. Dating candidates with similar taste preferences can be easily selected.

[0103] <Supporting Test 2> If taste evaluations of model foods and beverages can be obtained, taste preferences for general foods and beverages can also be understood. This point is further supported by the following experiment.

[0104] <Preparation of model foods> Model foods 1 to 5 were prepared. Each model food is an example of a model food or beverage. For each model food, commercially available popcorn beans (manufactured by Koda Foods Co., Ltd.), salad oil, and salt were used. 50g of popcorn beans and 10g of salad oil were placed in a pot, covered, and heated over medium heat to expand. Salt water was sprayed onto the heated popcorn and heated again to remove moisture, creating model foods. The salt concentration of model food 1 was 0.4%. The salt concentration of model food 2 was 1.0%. The salt concentration of model food 3 was 1.8%. The salt concentration of model food 4 was 2.0%. The salt concentration of model food 5 was 2.6%.

[0105] <Method for measuring salt concentration> The method used to measure salt content in this study was the Mohr method. 0.5 ml of 5% potassium chromate solution was added as an indicator to a 10-fold diluted model food, and titration was performed with 0.1 mol / L silver nitrate solution. The endpoint was determined when the test solution turned slightly orange.

[0106] <Understanding taste preference classes using model foods> The evaluation of the model foods, quantification of the evaluations of the model foods, cluster analysis, assignment of points to clusters, multiple regression analysis, determination of taste preference scores, and determination of taste preference classes were carried out in the same manner as for the model liquid described above.

[0107] <Analysis of each subject's taste evaluation score for soba noodles (when using model foods)> The taste evaluation scores for soba noodles were analyzed using the taste preference classes identified using the model foods. The soba noodles' taste evaluation scores were calculated using scores from 7 to 9 subjects randomly selected from the results of the aforementioned test. The average taste evaluation scores for the group with a mild soba noodle taste preference (when the model foods were used) were calculated. The average taste evaluation scores for the group with a strong soba noodle taste preference (when the model foods were used) were also calculated. The taste evaluation scores were standardized in this case, as the values ​​of the taste evaluation scores between groups do not necessarily match.

[0108] Figure 21 shows the analysis results of each subject's taste evaluation score for soba noodles. These results indicate that (1) soba noodles 2 had the highest standardized taste evaluation score in the light-flavor preference group 121, (2) soba noodles 3 had the highest standardized taste evaluation score in the strong-flavor preference group 122, (3) soba noodles 5 had the lowest standardized taste evaluation score in the light-flavor preference group 121, and (4) soba noodles 1 had the lowest standardized taste evaluation score in the strong-flavor preference group 122. In other words, the taste preferences for the model foods and the actual menu item "soba noodles" are substantially the same. In other words, by evaluating the model foods, it is possible to predict the taste preference for "soba noodles" without evaluating the actual menu item "soba noodles." Furthermore, these results are similar to those obtained by analyzing the taste evaluation scores for soba noodles using taste preference classes determined using a model liquid. In other words, the model foods that can be used to determine taste preference classes are not limited to model liquids.

[0109] <Analysis of each subject's taste evaluation score for beef bowl toppings (using model foods)> The taste evaluation scores for beef bowl ingredients were analyzed using the taste preference classes identified using model foods. The taste evaluation scores for beef bowl ingredients were calculated using scores from 7 to 9 subjects randomly selected from the results of the aforementioned test. The average taste evaluation scores for the group with a light taste preference for beef bowl ingredients (when model foods were used) were calculated. The average taste evaluation scores for the group with a strong taste preference for beef bowl ingredients (when model foods were used) were also calculated. The taste evaluation scores were standardized in this case, as the values ​​of the taste evaluation scores between groups do not necessarily match.

[0110] Figure 22 shows the analysis results of the taste evaluation scores of each subject for beef bowl toppings. These results show that (1) beef bowl topping 2 had the highest standardized taste evaluation score in the light-flavor preference group 131, (2) beef bowl toppings 4 and 5 had the highest standardized taste evaluation scores in the strong-flavor preference group 132, (3) beef bowl toppings 1 and 5 had the lowest standardized taste evaluation scores in the light-flavor preference group 131, and (4) beef bowl topping 1 had the lowest standardized taste evaluation score in the strong-flavor preference group 132. In other words, the taste preference for the model food is essentially the same as the taste preference for the actual menu item, "beef bowl topping." In other words, by evaluating the model food, it is possible to predict the taste preference for "beef bowl topping" without evaluating the actual menu item, "beef bowl topping." Furthermore, these results are similar to the results of analyzing the taste evaluation scores of soba noodles using taste preference classes identified using the model liquid. In other words, the model food and drink that can be used to grasp taste preference classes is not limited to model liquids. [Industrial Applicability]

[0111] The fields in which the present invention is useful include food and drink provision services, food and drink suggestion services, restaurant suggestion services, and dating candidate suggestion services.

Claims

1. A method for determining taste preferences, comprising at least the following: Evaluation: At least three or more model foods and beverages are evaluated, and the result is a user's taste evaluation score for each model food and beverage, and The three or more model foods and beverages contain at least salt, and the salt concentrations of the three or more model foods and beverages are different from one another, and the blending amounts of ingredients other than salt and water contained in each of the three or more model foods and beverages are the same; and Deriving: Here, the computer derives the user's taste preference score and / or taste preference class, using the user's taste evaluation score for each of the model foods and beverages, and the relationship between the taste evaluation score for each of the model foods and beverages and the taste preference class data and / or taste preference score data. The user's taste preference score is derived by substituting the user's taste evaluation score for each model food or beverage into a regression equation in which the taste evaluation score for each model food or beverage is used as an explanatory variable and the user's taste preference score is used as a response variable; A taste preference class of the user is derived using the derived taste preference score of the user; The regression equation is specific to the three or more model foods and beverages.

2. 10. The method of claim 1, The salt concentration of the model food or drink is 0.13% or more and 3.00% or less.

3. 3. The method of claim 1 or 2, The model food and drink contains consommé, bouillon, soup stock or dashi.

4. A taste preference determination program that causes a computer to execute at least the following processes: Input: What is input is user taste evaluation data for each model food or beverage, and the number of model food or beverage is three or more, and the three or more model food or beverages contain at least salt, and the salt concentrations of the three or more model food or beverages are different from one another, and the blending amounts of ingredients other than salt and water contained in each of the three or more model food or beverages are the same, and Output: The user's taste preference score data and / or taste preference class data are output, and the user's taste evaluation data for each model food or beverage that has been input, as well as the relationship between the taste evaluation data for each model food or beverage and the taste preference class data and / or taste preference score data, are used for this output. the user's taste preference score data is derived by substituting the user's taste evaluation score data for the three or more model foods and beverages into a regression equation in which the taste evaluation data for each model food and beverage is an explanatory variable and the user's taste preference score data is an objective variable; the user's taste preference class data is derived using the derived user's taste preference score data; The regression equation is specific to the three or more model foods and beverages.

Images