Cooking information providing system, cooking information providing method, cooking information providing device, cooking information providing program, and recording medium on which the program is recorded

Abstract

This presentation system S comprises: a user information recognition unit 10 for recognizing user information; a current feeling recognition unit 12 for recognizing a current feeling of a user; a recommended food recognition unit 13 for using a prediction model, which receives an input of the user information and the current feeling and outputs a recommended food, to recognize a recommended food; and a food information presentation unit 15 for acquiring, and presenting to the user, food information related to the recommended food. The current feeling recognition unit 12 infers, as a current feeling, coordinates selected by the user in a feeling model in which the coordinates are defined on the basis of a plurality of basic feelings.

Description

[Technical Field]

[0001] The present invention relates to a cooking information providing system, a cooking information providing method, a cooking information providing device, a cooking information providing program, and a recording medium storing the program, which provide a user with information on dishes that are recommended for eating. Record Regarding the medium. [Background technology]

[0002] There is a conventional system that presents recommended dishes to a user as eating behavior. Here, "eating behavior" refers to various behaviors related to food intake, including not only the act of eating a dish, but also the act of cooking and preparing the dish, and making a reservation at a restaurant that serves the dish.

[0003] Incidentally, the recommended dishes differ not only depending on the user's personal conditions, such as the user's preferences for food, but also on environmental conditions (for example, the amount of time available for eating, the ingredients available, etc.) As such, a system of this type is known that presents recommended dishes by taking into account the user's preferences for food as well as environmental conditions (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2020 / 241693 Summary of the Invention [Problem to be solved by the invention]

[0005] The system described in Patent Document 1 only refers to parameters that are relatively fixed, such as preferences and circumstances, when recognizing recommended dishes, and does not refer to parameters that are prone to change, such as the user's mood (i.e., emotions).

[0006] However, it is known that there is a certain correlation between emotions and eating behavior (see, for example, Imada Sumio (2009). Emotions and Eating Behavior, Research in Emotional Psychology, 17, 120-128). Therefore, without taking emotions into account, it is difficult to accurately identify dishes that are likely to satisfy eating behavior.

[0007] On the other hand, it is generally difficult for users to accurately express their own emotions (and thus input them into the system). Furthermore, considering how to accurately express their emotions can be cumbersome for users, which may result in further changes in their emotions.

[0008] The present invention has been made in consideration of the above points, and provides a cooking information providing system, a cooking information providing method, a cooking information providing device, a cooking information providing program, and a recording medium storing the program, which are capable of providing a user with information on dishes that are likely to satisfy the user through eating behavior, while also taking into account the user's emotions. Record The purpose is to provide a medium. [Means for solving the problem]

[0009] The cooking information providing system of the present invention comprises: A cooking information providing system that provides a user with cooking information that is information about recommended dishes that are dishes that a user is recommended to eat, a user information recognition unit that recognizes user information including at least one of the user's biological information, preferences, and environment in the eating behavior; a current emotion recognition unit that recognizes a current emotion that the user was feeling when the user requested to acquire the recipe information; a recommended dish recognition unit that recognizes the recommended dishes using a prediction model that inputs the user information and the current emotion and outputs the recommended dishes; a dish information providing unit that acquires the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that may become the recommended dishes, and provides the dish information to the user; the current emotion recognition unit has an emotion model presentation unit that presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any of the coordinates in the emotion model can be selected; and a coordinate recognition unit that recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; The recommended dish recognition unit is characterized in that it uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, machine-learns the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

[0010] Here, "user" refers to a user of the cooking information providing system of the present invention. Therefore, users include not only the person who engages in eating behavior, but also those who make others engage in eating behavior, and those whose eating behavior is influenced by others. For example, users include parents who use the system for their children, and children whose eating behavior is managed by their parents.

[0011] In addition, "food information" here includes information about the food itself, such as the name of the food, ingredients, and recipe, as well as information about the store where the food is served and information about the means of obtaining the food, such as the names of services that can deliver the food.

[0012] Furthermore, here, "learning data" refers to data used to train a machine learning algorithm, such as features used in the training, correct answer data, or a combination thereof. More specifically, "learning data" refers to data used to determine parameters of a machine learning algorithm, such as features used to determine the parameters, correct answer data, or a combination thereof.

[0013] In this way, the cooking information providing system of the present invention employs an emotion model, which is a two-dimensional or three-dimensional model with coordinates defined based on multiple basic emotions, as an interface for inputting the user's emotions, and recognizes the coordinates selected in the emotion model as the user's emotions. This allows the user to easily and accurately express their emotions as coordinate values ​​without converting them into linguistic expressions.

[0014] This system uses these coordinates as input data. In other words, instead of the vague parameter of emotion, this system uses the clear parameter of coordinates. This eliminates ambiguity from the input data, allowing the system to obtain accurate recommended dishes as output data.

[0015] Therefore, the system of the present invention can provide users with cooking information about appropriate recommended dishes that also take emotions into account, and by eating the recommended dishes, the user can be highly satisfied.

[0016] In addition, in the cooking information providing system of the present invention, The emotion model is preferably a model including a first coordinate axis representing one or more basic emotions, and a second coordinate axis representing the strength of the basic emotions.

[0017] By adopting a model using such coordinate axes as an emotion model, users can intuitively understand what emotions the coordinates in the emotion model represent. This in turn makes it easier for users to accurately express their emotions as coordinates, further eliminating ambiguity from input data. This makes it possible to provide users with cooking information about more appropriate recommended dishes that more accurately take emotions into account.

[0018] An example of an emotion model using such coordinate axes is EmojiGrid. (registered trademark) The Plutchik Emotion Wheel and models based on them (for example, a circular model that uses the color layout of the Plutchik Emotion Wheel) can be used. The basic emotions can be, for example, joy, trust, fear, surprise, sadness, disgust, anger, and anticipation, which are used in the Plutchik Emotion Wheel, or joy, fear, surprise, sadness, disgust, and anger, which are used in Ekman's theory.

[0019] In addition, in the cooking information providing system of the present invention, the recommended dish recognition unit recognizes the plurality of recommended dishes; It is preferable that the dish information providing unit provides the user with the dish information corresponding to each of the plurality of recommended dishes.

[0020] In this way, by providing cooking information about multiple recommended dishes, the final choice of which recommended dish to target for eating behavior is left to the user. As a result, when the user actually eats the recommended dish they selected, they are given a sense of satisfaction that the dish that is the target of their eating behavior was chosen by them, making them more likely to feel satisfied with the results of their eating behavior.

[0021] Furthermore, in the case where the cooking information providing system of the present invention is configured to provide multiple pieces of cooking information, a selection status recognition unit that recognizes a selection status of each of the recommended dishes, which is a status at the time when the recommended dish is selected by the user from the recommended dishes; It is preferable that the recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selection coordinates, the user information, and the selection status as learning data, performs machine learning to determine the correlation between the candidate dishes and the selection coordinates, the user information, and the selection status, and outputs the recommended dish selected from the plurality of candidate dishes.

[0022] In this way, a prediction model that uses the selection status (the situation at the time of selecting a dish) obtained from previous selections as training data will be more in line with the user's actual situation. As a result, if the user follows the recommended dishes output by the prediction model, the user will be more likely to feel satisfied.

[0023] Here, "selection status" refers to the system status, such as the number of selections and display position (described later), as well as recommended dishes that were selected before the selected dish was selected, and the types of recommended dishes that were presented at the time the selected dish was selected. Additionally, the user status includes the time the selected dish was selected (and therefore whether it was breakfast or dinner, etc.) and the user's actions before and after the selected dish (for example, the content of operations on the mobile device).

[0024] Furthermore, in the case where the dish information providing system of the present invention is configured to use a prediction model that employs, as learning data, the selection situation at the time when the dish is selected, It is preferable that the selection status recognition unit recognizes, as the selection status for each of the plurality of recommended dishes, at least the number of times the recommended dish has been selected, which is the number of times the recommended dish has been selected.

[0025] If a dish is selected, it is highly likely that it is directly preferred by the user. Therefore, by using the number of selections as a selection status, the output recommended dishes are more likely to match the user's preferences. Ultimately, it is possible to provide the user with cooking information related to appropriate recommended dishes.

[0026] Furthermore, in the case where the dish information providing system of the present invention is configured to use a prediction model that employs, as learning data, the selection situation at the time when the dish is selected, the recommended dish recognition unit uses each of the plurality of candidate dishes, the selection coordinates, the user information, and the selection status as learning data, and performs machine learning to determine the correlation between the candidate dish and the selection coordinates, the user information, and the selection status, as well as the recommendation level, which is the strength of the correlation, as the prediction model, and outputs the recommended dish selected from the plurality of candidate dishes and the recommendation level of the recommended dish; It is preferable that the cooking information providing unit provides the user with the cooking information corresponding to each of the multiple recommended dishes together with the recommendation level, or in a format in which cooking information corresponding to the recommended dishes with higher recommendation levels is given priority.

[0027] In this way, by providing the user with cooking information corresponding to each of a plurality of recommended dishes along with their recommendation level, or in a format in which recommended dishes with higher recommendation levels are given priority, the user will naturally find it easier to select cooking information relating to recommended dishes that are highly recommended and likely to satisfy them.

[0028] In addition, the cooking information providing system of the present invention includes: A cooking information providing system that provides a user with cooking information that is information about recommended dishes that are dishes that a user is recommended to eat, a user information recognition unit that recognizes user information including at least one of the user's biological information, preferences, and environment in the eating behavior; a current emotion recognition unit that recognizes a current emotion, which is an emotion at the time when the user requests to acquire the recipe information; a recommended dish recognition unit that recognizes the recommended dish based on correlation data that represents a correlation between the user information and the current emotion and each of a plurality of candidate dishes that may become the recommended dish; a dish information providing unit that acquires the dish information from a dish information recognizing unit that recognizes information about a plurality of the candidate dishes and provides the dish information to the user; The current emotion recognition unit has an emotion model presentation unit that presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model whose coordinates are defined based on a plurality of basic emotions, in a format in which any of the coordinates in the emotion model can be selected, and a coordinate recognition unit that recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion.

[0029] In this way, the cooking information providing system of the present invention employs an emotion model, which is a two-dimensional or three-dimensional model with coordinates defined based on multiple basic emotions, as an interface for inputting the user's emotions, and recognizes the coordinates selected in the emotion model as the user's emotions. This allows the user to easily and accurately express their emotions as coordinate values ​​without converting them into linguistic expressions.

[0030] This system then uses the coordinates as the current emotion. In other words, instead of the ambiguous parameter of emotion, this system uses the clear parameter of coordinates. This eliminates ambiguity in the item of current emotion corresponding to one of the correlation data, and therefore can accurately obtain recommended dishes corresponding to the other correlation data.

[0031] Therefore, the system of the present invention can provide users with cooking information about appropriate recommended dishes that also take emotions into account, and by eating the recommended dishes, the user can be highly satisfied.

[0032] The method for providing cooking information of the present invention further comprises: A method for providing a user with recipe information, the recipe information being information about recommended recipes that are recommended for the user to eat, comprising: a step in which a user information recognition unit recognizes user information including at least one of biometric information, preferences, and an environment in the eating behavior of the user; a current emotion recognition unit recognizing a current emotion that is an emotion at the time when the user desires to acquire the cooking information; a recommended dish recognition unit recognizing the recommended dish using a prediction model that inputs the user information and the current emotion and outputs the recommended dish; a dish information providing unit acquiring the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that may become the recommended dishes, and providing the dish information to the user; the step of recognizing the current emotion includes a process in which an emotion model presentation unit of the current emotion recognition unit presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any coordinate in the emotion model can be selected; and a process in which a coordinate recognition unit of the current emotion recognition unit recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; In the step of recognizing the recommended dish, the recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, performs machine learning on the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

[0033] Furthermore, the cooking information providing device of the present invention comprises: A cooking information providing device that provides a user with cooking information that is information about recommended dishes that are dishes that a user is recommended to eat, a user information recognition unit that recognizes user information including at least one of the user's biological information, preferences, and environment in the eating behavior; a current emotion recognition unit that recognizes a current emotion, which is an emotion at the time when the user requests to acquire the recipe information; a recommended dish recognition unit that recognizes the recommended dishes using a prediction model that inputs the user information and the current emotion and outputs the recommended dishes; a dish information providing unit that obtains the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that may become the recommended dishes, and provides the dish information to the user; the current emotion recognition unit has an emotion model presentation unit that presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any of the coordinates in the emotion model can be selected; and a coordinate recognition unit that recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; The recommended dish recognition unit is characterized in that it uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, machine-learns the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

[0034] In addition, the cooking information providing program of the present invention includes: A recipe information providing program that causes a computer to execute a recipe information providing method for providing a user with recipe information that is information about recommended recipes that are recommended for the user to eat, comprising: The computer, a step in which a user information recognition unit recognizes user information including at least one of biometric information, preferences, and an environment in the eating behavior of the user; a current emotion recognition unit recognizing a current emotion that is an emotion at the time when the user desires to acquire the cooking information; a recommended dish recognition unit recognizing the recommended dish using a prediction model that inputs the user information and the current emotion and outputs the recommended dish; a dish information providing unit acquiring the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that can be the recommended dishes, and providing the dish information to the user; Execute the step of recognizing the current emotion includes a process in which an emotion model presentation unit of the current emotion recognition unit presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any coordinate in the emotion model can be selected; and a process in which a coordinate recognition unit of the current emotion recognition unit recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; In the step of recognizing the recommended dish, the recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, performs machine learning on the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

[0035] The recording medium of the present invention is The cooking information providing program is recorded and the cooking information providing program is readable by the computer. [Brief explanation of the drawings]

[0036] [Figure 1] FIG. 1 is an explanatory diagram showing a schematic configuration of a provision system according to an embodiment. [Figure 2] FIG. 2 is a block diagram showing the configuration of a processing unit of the provision system of FIG. 1; [Figure 3A] FIG. 2 is a schematic diagram showing an example of an emotion model used to acquire a current emotion in the provision system of FIG. 1; [Figure 3B] FIG. 10 is a schematic diagram showing an example of an emotion model used to acquire a current emotion in the provision system according to the first modified example. [Figure 3C] FIG. 10 is a schematic diagram showing an example of an emotion model used to acquire a current emotion in a provision system according to a second modified example. [Figure 4A] 4 is a flowchart showing the process executed by the provision system of FIG. 1 until it provides dish information. [Figure 4B] 6 is a flowchart showing a process executed by the provision system of FIG. 1 when dish information is selected. [Figure 5] 10 is an image diagram showing an example of an image displayed on a user terminal when the provision system of FIG. 1 recognizes user information. [Figure 6] FIG. 2 is an image showing an example of an image displayed on a user terminal when the providing system of FIG. 1 recognizes a current emotion. [Figure 7] FIG. 2 is an image diagram showing an example of an image displayed on a user terminal when the provision system of FIG. 1 provides dish information. [Figure 8] FIG. 2 is an image diagram showing an example of an image displayed on a user terminal when any of the dish information is selected by the provision system of FIG. 1. DETAILED DESCRIPTION OF THE INVENTION

[0037] Hereinafter, a dish information providing system (hereinafter referred to as "providing system S") according to an embodiment and a dish information providing method implemented using the same will be described with reference to the drawings.

[0038] The provision system S in this embodiment is used in a service that extracts recommended dishes, which are dishes that user U is recommended to eat, from among multiple candidate dishes in response to a request from user U, and provides cooking information regarding the recommended dishes to user U.

[0039] In the following description, "user" refers to a user of the provision system S. Therefore, the person assumed to be a user U includes not only the person who engages in eating behavior, but also a person who makes others engage in eating behavior, and a person who is forced to engage in eating behavior by others. For example, it includes parents who use the system for their children, and children whose eating behavior is managed by their parents.

[0040] Here, "food information" includes information about the food itself, such as the name of the food, ingredients, and recipe, as well as information about the store where the food is served and the means by which the food can be obtained, such as the names of services that can deliver the food.

[0041] [System Overview] The schematic configuration of the provision system S will be described below with reference to FIGS.

[0042] As shown in FIG. 1, the provision system S is a computer system for providing recipe information to a user U, and is configured by a server 1 owned by a provider of the service provided by the provision system S.

[0043] The server 1 is configured to be able to communicate information with a user terminal 2 such as a smartphone or tablet owned by a user U via the Internet network, a public line, or the like.

[0044] The cooking information providing system of the present invention is not limited to being configured by one server, but rather any of the terminals constituting the cooking information providing system may be configured to include the processing unit described below.

[0045] Therefore, for example, the entire cooking information providing system may be configured with multiple servers. Furthermore, at least one of the processing units or at least part of the functions of that processing unit may be implemented in the user terminal, and the system may be configured with the user terminal and the server working together, or with the user terminal alone. Furthermore, in this embodiment, functions equivalent to the input unit 20 and output unit 21 provided in the user terminal 2 may be provided in a terminal having a processing unit, and configured as an independent cooking information providing device.

[0046] Also, as shown in Figure 2, the user terminal 2 that can communicate with the server 1, which is the provision system S, has an input unit 20 and an output unit 21 as functions (processing units) realized by at least one of an implemented hardware configuration and a program.

[0047] In this embodiment, the input unit 20 and output unit 21 of the user terminal 2 will be described as being touch panels. However, the user terminal is not limited to such a configuration, and may be any device that can accept information input from a user and output information to present to the user. Therefore, for example, the user terminal may be configured to enable input and output using a keyboard, microphone, camera, speaker, etc. in addition to a touch panel.

[0048] [Configuration of each processing section] As shown in FIG. 2, the server 1 includes, as functions (processing units) realized by at least one of the implemented hardware configuration and the program, a user information recognition unit 10, a user information storage unit 11, a current emotion recognition unit 12, a recommended dish recognition unit 13, a dish information storage unit 14 (dish information recognition unit), a dish information provision unit 15, and a selection status recognition unit 16.

[0049] The user information recognition unit 10 recognizes information including at least one of the user U's biometric information, preferences, and eating behavior environment as user information.

[0050] Here, " living organisms "Information" refers to information about the user's body that may influence eating behavior. living organisms Examples of such information include the user's gender, age, height, and weight. Here, "preferences" refers not only to the user's preferences regarding the food or ingredients themselves, but also to preferences that may affect eating behavior. For example, this includes whether or not the user likes summer (and thus the state of appetite depending on the temperature).

[0051] In addition, here, the "eating behavior environment" includes, for example, the number of people with whom the user eats, the user's relationship with those with whom the eating behavior is shared, the planned time for the eating behavior, the location of the eating behavior, and the type of eating behavior (whether the user eats or feeds others, whether the user cooks at home or eats out, etc.).

[0052] In the provision system S, the user information recognition unit 10 first presents a predetermined questionnaire to the output unit 21 of the user terminal 2 (see FIG. 5). Thereafter, the user information recognition unit 10 refers to the answers to the questionnaire and the information stored in the user information storage unit 11, and recognizes user information about the user U who will receive the service provided by the provision system S this time.

[0053] Furthermore, if the recognized user information differs from the user information stored in the user information storage unit 11, the user information recognition unit 10 stores new user information in the user information storage unit 11.

[0054] The user information storage unit 11 stores user information corresponding to each user U. First, the user information storage unit 11 stores, as initial information, user information acquired during a registration procedure for the user U to receive services from the provision system S. Thereafter, when new user information is recognized by the user information recognition unit 10, information that has been added or changed based on the new user information is stored in the initial information.

[0055] In addition, in cases where there is no registration procedure for receiving a service, when the user information is recognized for the first time by the user information recognition unit 10, the user information may be stored as initial information.

[0056] Furthermore, when the selection situation recognition unit 16 recognizes a selection situation, which will be described later, the user information storage unit 11 stores the selection situation for each user U or for each group having user information that is the same as or corresponds to user U. Corresponding user information refers to user information that has a part of the user information that is the same as or similar to the user information.

[0057] The current emotion recognition unit 12 recognizes the current emotion that the user U felt when he or she attempted to receive a service from the provision system S (i.e., when he or she requested to obtain cooking information). The current emotion recognition unit 12 includes an emotion model presentation unit 12a and a coordinate recognition unit 12b.

[0058] The emotion model presenting unit 12a presents to the user U, via the user terminal 2, an emotion model which is a planar model whose coordinates are defined based on a plurality of basic emotions, in a format in which the user U can select any of the coordinates in the emotion model.

[0059] Here, the "basic emotions" could be, for example, joy, trust, fear, surprise, sadness, disgust, anger, and anticipation, which are used in Plutchik's wheel of emotions, or joy, fear, surprise, sadness, disgust, and anger, which are used in Ekman's theory.

[0060] The provision system S employs an emotion model including a first coordinate axis representing multiple basic emotions and a second coordinate axis representing the strength of the basic emotions. Specifically, the provision system S employs the EmojiGrid shown in Figure 3A as the emotion model (see Figure 6).

[0061] In this EmojiGrid, facial images expressing some emotion are placed at a predetermined interval in the peripheral area to define the first coordinate axis representing basic emotions. In addition, in this EmojiGrid, a facial image expressing no particular emotion is placed at the center position as the origin, and the distance from the origin to each of the peripheral images expressing some emotion is defined as the second coordinate axis representing the strength of the emotion corresponding to each image.

[0062] It should be noted that the emotion model in the present invention is not limited to such an EmojiGrid, but may be any two-dimensional or three-dimensional model in which coordinates are defined based on a plurality of basic emotions.

[0063] For example, as shown in the first modified example of Figure 3B, Plutchik's Wheel of Emotions may be used as an emotion model. Furthermore, a model that uses the color layout of Plutchik's Wheel of Emotions but without the letters may be used. Conversely, a model that uses the letter layout of Plutchik's Wheel of Emotions but without the colors may be used.

[0064] Also, for example, a face scale may be used as the emotion model, as in the second modified example shown in Fig. 3C. In the example shown in Fig. 3C, the emotion model is configured using only one type of face scale, but it may also be configured by arranging multiple types of face scales.

[0065] Furthermore, in the present embodiment, the first variant, and the second variant, a case has been described in which a planar model is used as the emotion model, but the emotion model of the present invention is not limited to such a configuration, and may also be a three-dimensional model.

[0066] The coordinate recognition unit 12b recognizes selected coordinates, which are coordinates in an emotion model selected by the user U via the input unit 20 of the user terminal 2. In the provision system S, the coordinate recognition unit 12b recognizes, as selected coordinates, coordinates specified by the user U by touching the emotion model displayed on the touch panel, which is the input unit 20 and output unit 21 of the user terminal 2.

[0067] When a face scale is employed as in the second modified example shown in Fig. 3C, only the face image (button) that constitutes the face scale may be selectable, and the coordinates of the selected face scale may be recognized as the selected coordinates. Also, any coordinates within the range in which the face scale is displayed may be selectable.

[0068] The coordinate recognition unit 12b then recognizes the recognized selected coordinates themselves as the current emotion, and therefore does not particularly identify the emotion based on the coordinates.

[0069] The recommended dish recognition unit 13 uses a predetermined prediction model to recognize a plurality of recommended dishes and the recommendation levels of the recommended dishes.

[0070] The prediction model used by the recommended dish recognition unit 13 is a machine learning model that uses each of the multiple candidate dishes, the selection coordinates recognized by the current emotion recognition unit 12, the user information recognized by the user information recognition unit 10, and the selection situation described below as learning data to determine the correlation between the candidate dish, its selection coordinates, and the user information, as well as the recommendation level, which is the strength of the correlation. but Once entered, the recommended dishes and their recommendations will be displayed. degree is output.

[0071] Here, a "candidate dish" is a dish used in the prediction model and can become a recommended dish.

[0072] Furthermore, here, "learning data" refers to data used to train a machine learning algorithm, such as features used in the training, correct answer data, or a combination thereof. More specifically, "learning data" refers to data used to determine parameters of a machine learning algorithm, such as features used to determine the parameters, correct answer data, or a combination thereof.

[0073] Furthermore, after the selection situation recognition unit 16 recognizes the selection situation described below, this prediction model is machine-learned to learn the correlation between each of the multiple candidate dishes, the selection coordinates, and the user information, as well as the recommendation level, which is the strength of the correlation, each time the selection situation is recognized or when the selection situation has been recognized a predetermined number of times, using each of the multiple candidate dishes, the selection coordinates, and the user information, as well as past selection situations, as learning data.

[0074] It is not necessary to use all past selection states as the past selection states, but rather selection states at a predetermined timing (for example, the most recent predetermined number of times, within a predetermined period, etc.) may be used.

[0075] The dish information storage unit 14 stores multiple pieces of information about candidate dishes. The types of information correspond to the dish information described above. Specifically, the stored information includes information about the dish itself, such as the name, ingredients, and recipe of the dish, as well as information about the restaurant where the dish is served and information about the means for obtaining the dish, such as the name of a service that can deliver the dish.

[0076] In the provision system S, the dish information storage unit 14, which is the dish information recognition unit, is configured as part of the provision system S. However, the dish information recognition unit of the present invention is not limited to such a configuration, and may be configured to recognize multiple pieces of information about candidate dishes.

[0077] Therefore, for example, the dish information recognition unit may be provided in a separate system independent of the dish information providing system. Furthermore, instead of a database storing dish information, the dish information recognition unit may be a processing unit that searches information on the Internet, collects and recognizes multiple pieces of dish information.

[0078] The dish information provider 15 acquires information about each of the plurality of recommended dishes recognized by the recommended dish recognition unit 13 from the dish information storage unit 14 as dish information to be presented to the user U. Then, the dish information provider 15 provides the user U with the dish information corresponding to each of the acquired plurality of recommended dishes in a selectable format via the output unit 21 of the user terminal 2.

[0079] At this time, the dish information provider 15 recognizes the recommendation level recognized by the recommended dish recognizer 13 for each of the multiple recommended dishes, and provides dish information based on the recommendation level. Specifically, in the provision system S, dish information for a recommended dish with a higher recommendation level is displayed with higher priority (for example, positioned higher) on the display screen (see FIG. 7).

[0080] Furthermore, the cooking information provider 15 may change the way cooking information is provided depending on the current user information of the user U recognized by the user information recognition unit 10. For example, if the user information includes information that the current eating behavior is related to cooking at home, the cooking information provider 15 provides a recipe for that dish as cooking information. On the other hand, if the user information includes information that the current eating behavior is related to eating out, the cooking information provider 15 does not provide a recipe for that dish as cooking information, but provides restaurants that serve that dish.

[0081] When the user U selects any of the multiple pieces of dish information provided by the dish information provider 15 via the input unit 20 of the user terminal 2, the selection status recognition unit 16 first recognizes the recommended dish corresponding to the selected dish as the selected dish. The selection status recognition unit 16 also recognizes the situation at the time the selected dish is recognized (i.e., the time when any of the recommended dishes becomes the selected dish) as the selection status.

[0082] Here, "selection status" refers to the status on the system side, such as the number of times the dish was selected, its display position, recommended dishes that were selected before the selected dish was selected, and the types of recommended dishes that were presented at the same time as the selected dish was selected.

[0083] In addition, the user's situation may include the time when the selected dish was selected (and therefore whether it was breakfast or dinner, etc.), the user's actions before and after the selected dish (for example, the content of operations on the mobile device), etc. Furthermore, the content of operations on the mobile device may include not only those that the user consciously performed on the device (for example, operations on the touch panel, etc.), but also those that the user unconsciously performed (for example, the time spent viewing dish information, etc.).

[0084] In addition, the selection status recognition unit 16 stores the recognized selection status for each of the multiple recommended dishes in the user information storage unit 11 for each user U or for each group having the same or corresponding user information as the user U.

[0085] [Processing performed in each processing section] Next, a method for providing dish information, which is a process executed by the providing system S, will be described with reference to FIGS.

[0086] In this process, first, the user information recognition unit 10 presents a questionnaire for confirming current user information to the user U via the user terminal 2 in a format that the user can answer (FIG. 4A / STEP 01).

[0087] Specifically, when a service provided by the provision system S is used for the first time, a questionnaire asking for information for identifying the user U (for example, a separately registered account name) is first presented in a responsive format.

[0088] Once the questionnaire has been answered, or when the user uses the service for the second time or later, a questionnaire will be presented in an answerable format, asking about the user U's age (biometric information), number of people living with them, whether they cook at home or eat out (eating environment), and favorite ingredients (preferences), as shown in Figure 5.

[0089] Next, when the questionnaire is answered, the user information recognition unit 10 recognizes the user information of the current user U based on the questionnaire answer and known user information (FIG. 4A / STEP 02).

[0090] Next, the user information recognition unit 10 determines whether the current user information differs from the known user information (FIG. 4A / STEP 03).

[0091] If the current user information differs from the known user information (YES in STEP 03), the user information recognition unit 10 updates the user information by storing the current user information in the user information storage unit 11 (FIG. 4A / STEP 04).

[0092] Specifically, at the time of first use, the user information recognition unit 10 recognizes the current user information of the user U based on information separately obtained at the time of account registration, etc., and the answers to the current questionnaire. At this time, the user information has not yet been stored, and the current user information is naturally different from the known user information, so the user information recognition unit 10 stores the current user information as is in the user information storage unit 11 as the user information of the user U.

[0093] Furthermore, when using the service for the second or subsequent time, the user information recognition unit 10 first compares the current user information of the recognized user U with the previous or previous user information of the user U acquired from the user information storage unit 11. If there are differences between the user information, the user information recognition unit 10 creates new user information that incorporates the differences from the current user information, and stores the new user information in the user information storage unit 11 as the user information of the user U.

[0094] If the current user information does not differ from the known user information (NO in STEP 03), or if new user information is stored in the user information storage unit 11 (when STEP 04 is executed), the current emotion recognition unit 12 presents an emotion model and coordinates in the emotion model to the user U via the user terminal 2 in a selectable format (FIG. 4A / STEP 05).

[0095] 6, the current emotion recognition unit 12 displays an emotion model (EmojiGrid) on the touch panel of the user terminal 2. When the user touches any point on this emotion model, the current emotion recognition unit 12 determines that the coordinates of that point have been selected.

[0096] Next, the current emotion recognition unit 12 determines whether any coordinates in the emotion model have been selected (FIG. 4A / STEP 06).

[0097] If no coordinates have been selected (NO in STEP 06), the current emotion recognition unit 12 repeats the determination in STEP 06 at a predetermined control cycle.

[0098] On the other hand, if any coordinates are selected (YES in STEP 06), the current emotion recognition unit 12 recognizes the coordinates as selected coordinates, and recognizes the selected coordinates as the current emotion (FIG. 4A / STEP 07).

[0099] Next, the recommended dish recognition unit 13 inputs the current user information and current emotion into the prediction model, and outputs a plurality of recommended dishes for the user U and the recommendation level of each of the plurality of recommended dishes (FIG. 4A / STEP 08).

[0100] Next, the dish information provider 15 acquires dish information for each of the plurality of recommended dishes from the dish information storage unit 14 (FIG. 4A / STEP 09).

[0101] Next, the dish information providing unit 15 provides the user U, via the user terminal 2, with the name of a dish, which is one of the acquired dish information, in a selectable format in an order based on the recommendation level of the recommended dish corresponding to the dish information (Figure 4A / STEP 10).

[0102] Specifically, as shown in Fig. 7, the cooking information provider 15 provides cooking information by displaying dish names one above the other on the touch panel of the user terminal 2. At this time, the order of the dish names is such that the higher the recommendation level of the corresponding recommended dish, the higher it is positioned. Then, when the user touches one of these dish names, the cooking information provider 15 displays the recommended dish corresponding to the dish name. but It is determined to be selected.

[0103] Next, the dish information provider 15 determines whether any dish name has been selected (FIG. 4B / STEP 11).

[0104] If no dish name has been selected (NO in STEP 11), the dish information provider 15 repeats the determination in STEP 11 at a predetermined control cycle.

[0105] On the other hand, if any of the dish names is selected (YES in STEP 11), the dish information providing unit 15 provides detailed dish information about the recommended dish corresponding to the selected dish name to the user U via the user terminal 2 (Figure 4B / STEP 12).

[0106] 8, for example, if the current user information includes information that the eating behavior is related to home cooking, the cooking information provider 15 displays a recipe for a recommended dish corresponding to the selected dish name on the touch panel of the user terminal 2. If the information includes information that the eating behavior is related to eating out, the cooking information provider 15 displays on the touch panel a restaurant or service that offers a recommended dish corresponding to the selected dish name.

[0107] Next, the selection status recognition unit 16 determines whether the selected dish information has been adopted (that is, whether it has become a selected dish) (FIG. 4B / STEP 13).

[0108] Specifically, when the cooking information providing unit 15 displays a cooking recipe on the touch panel of the user terminal 2, the selection status recognizing unit 16 displays an adopt button together with the recipe. When the adopt button is selected, the selection status recognizing unit 16 determines that the recommended dish corresponding to the recipe (i.e., cooking information) along with the adopt button has been adopted and become the selected dish.

[0109] If the selected dish information is not adopted (NO in STEP 13), the process returns to STEP 11, and the providing system S executes the processes in STEP 11 to STEP 13 again.

[0110] On the other hand, if the selected dish information is adopted (YES in STEP 13), the selection situation recognition unit 16 recognizes the selection situation (FIG. 4B / STEP 14).

[0111] Specifically, the selection status recognition unit 16 extracts and recognizes the selection status from information input or output to the user terminal 2, information about the current user U among the user information stored in the user information storage unit 11, and the time obtained from a clock provided in the provision system S.

[0112] The selection status may include, for example, the number of times the dish was selected, the display position, recommended dishes that were selected before the current selected dish was selected by the current user U, the type of recommended dish that was presented at the same time as the current selected dish, and the time of selection (and therefore whether it was breakfast, dinner, etc.).

[0113] Next, the selection status recognition unit 16 stores the selection status of the selected dishes for each user U or for each group having the same or corresponding user information as the user U in the user information storage unit 11 (FIG. 4B / STEP 15).

[0114] Next, the recommended dish recognition unit 13 adds the currently recognized selection situation to the prediction model, and uses the selected dish (i.e., the corresponding candidate dish), the selection coordinates, the user information, and the past selection situation as learning data, and performs machine learning on the correlation between the candidate dish and the selection coordinates, the user information, and the selection situation, as well as the recommendation level, which is the strength of the correlation (Figure 4B / STEP 16), and then terminates the current processing.

[0115] The recommended dish recognition unit 13 may perform machine learning incorporating this selection situation when a certain number of selection situations are collected, rather than performing the machine learning every time a selection situation is recognized.

[0116] As explained above, the recipe information providing method executed using the providing system S employs an emotion model, which is a planar model in which coordinates are defined based on a plurality of basic emotions, as an interface for inputting the emotions of the user S, and recognizes the coordinates selected in the emotion model as the emotions of the user U. This allows the user U to easily and accurately express their own emotions as coordinate values ​​without converting them into linguistic expressions, etc.

[0117] The provision system S uses these coordinates as input data. In other words, this system uses the clear parameter of coordinates instead of the vague parameter of emotion. This eliminates ambiguity from the input data, making it possible to obtain accurate recommended dishes as output data.

[0118] Therefore, the provision system S can provide the user U with cooking information about appropriate recommended dishes that also take emotions into account. By eating the recommended dishes, the user U can be highly likely to feel satisfied.

[0119] [Variations of the provision system] In this embodiment, the emotion model used is a model including a first coordinate axis representing one or more basic emotions and a second coordinate axis representing the strength of the basic emotions.

[0120] By adopting a model using such coordinate axes as an emotion model, the user U can intuitively understand what emotions the coordinates in the emotion model indicate. This in turn makes it easier for the user U to accurately express their own emotions as coordinates, further eliminating ambiguity from the input data. This makes it possible to provide the user U with cooking information about more appropriate recommended dishes that more accurately take emotions into account.

[0121] However, the emotion model of the present invention is not limited to this configuration, and may be any two-dimensional or three-dimensional model in which coordinates are defined based on a plurality of basic emotions. Therefore, for example, an emotion model that does not include coordinate axes may be used. Specifically, a model in which a plurality of colors are simply arranged in predetermined positions may be used as the emotion model.

[0122] In this embodiment, the recommended dish recognition unit 13 is configured to recognize multiple pieces of dish information, and the dish information provision unit 15 is configured to provide the user U with dish information corresponding to each of the multiple recommended dishes.

[0123] In this way, when cooking information on a plurality of recommended dishes is provided, the final choice of which recommended dish to target for eating behavior is left to the user U. As a result, when the user actually eats the selected recommended dish, the user U is given a sense of satisfaction that the dish that is the target of that eating behavior was selected by the user U himself / herself, and is more likely to feel satisfied with the results of that eating behavior.

[0124] However, the cooking information providing system of the present invention is not limited to such a configuration, and the recommended dish recognition unit may be configured to recognize only one recommended dish, and the cooking information providing unit may be configured to provide cooking information for only one of the multiple recommended dishes.

[0125] In addition, in this embodiment, when dish information is selected, the selection situation recognition unit 16 recognizes the selection situation, and the recommended dish recognition unit 13 uses the selection situation to perform machine learning of a prediction model.

[0126] In this way, a prediction model that uses as learning data the selection situation at the time of selecting a dish obtained up to that point in time for the selected dish becomes more in line with the actual situation of user U. As a result, if user U eats the recommended dishes output by the prediction model, user U will be more likely to feel satisfied.

[0127] However, the prediction model of the present invention is not limited to such a configuration, and may be configured to use each of a plurality of candidate dishes, the selected coordinates, and user information as learning data, machine-learn the correlation between the candidate dishes, the selected coordinates, and the user information, input user information and current emotions, and select and output a recommended dish from the plurality of candidate dishes.

[0128] Therefore, for example, instead of configuring the system to recognize the selection situation and perform machine learning of a predictive model using that selection situation, the predictive model introduced when the system was built may be configured to continue to be used as is without undergoing subsequent machine learning.

[0129] In addition, in this embodiment, the number of selections is used as the selection status. This is because a selected dish means that it is highly likely to be directly preferred by the user U, and so by using the number of selections as the selection status, the output recommended dishes are more likely to suit the preferences of the user U. This in turn makes it possible to provide the user with dish information related to appropriate recommended dishes.

[0130] However, the selection status of the present invention is not limited to such a configuration, and may be the status at the time when the selected dish for which dish information is provided is selected by the user and becomes the selected dish. Therefore, for example, the selection status does not need to include the number of selections.

[0131] In addition, in this embodiment, the prediction model of the recommended dish recognition unit 13 outputs a plurality of recommended dishes along with the recommendation level of each of the plurality of recommended dishes, and the dish information provision unit 15 changes the way in which the dish information is provided based on the recommendation level.

[0132] In this way, by providing user U with cooking information corresponding to each of a plurality of recommended dishes along with their recommendation level, or in a format in which recommended dishes with higher recommendation levels are given priority, user U will naturally find it easier to select cooking information relating to recommended dishes that are highly recommended and likely to satisfy the user.

[0133] However, the dish information providing unit of the present invention is not limited to such a configuration, and may be any unit that provides dish information to the user. Therefore, for example, the display order of dish information may be changed using criteria other than recommendation level (for example, alphabetical order, order by total ingredient prices, etc.). Also, the higher the priority, the larger the display size on the display screen may be. Also, the recommendation level value itself may be displayed together with the dish information.

[0134] [Other embodiments] Although the illustrated embodiment has been described above, the present invention is not limited to this embodiment.

[0135] For example, in the above embodiment, the recommended dish recognition unit 13 recognizes recommended dishes using a predetermined prediction model. However, the recommended dish recognition unit of the present invention is not limited to this configuration. Therefore, instead of using a prediction model, the recommended dish recognition unit may recognize recommended dishes based on correlation data that represents the correlation between user information and current emotions and each of multiple candidate dishes that could be recommended.

[0136] In the above embodiment, the provision system S is a single computer system. However, the present invention also includes a dish information provision program for executing the above-described dish information provision method on any one or more computers, and a recording medium on which the program is recorded and which can be read by a computer used by a user or the like. [Explanation of symbols]

[0137] 1...server, 2...user terminal, 10...user information recognition unit, 11...user information storage unit, 12...current emotion recognition unit, 12a...emotion model presentation unit, 12b...coordinate recognition unit, 13...recommended dish recognition unit, 14...dish information storage unit (dish information recognition unit), 15...dish information provision unit, 16...selection status recognition unit, 20...input unit, 21...output unit, S...provision system (dish information provision system), U...user.

Claims

1. A cooking information providing system that provides a user with cooking information that is information about recommended dishes that are dishes that a user is recommended to eat, a user information recognition unit that recognizes user information including at least one of biometric information, preferences, and an environment in the eating behavior of the user; a current emotion recognition unit that recognizes a current emotion, which is an emotion at the time when the user requests to acquire the recipe information; a recommended dish recognition unit that recognizes the recommended dishes using a prediction model that inputs the user information and the current emotion and outputs the recommended dishes; a dish information providing unit that acquires the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that may become the recommended dishes, and provides the dish information to the user; the current emotion recognition unit has an emotion model presentation unit that presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any of the coordinates in the emotion model can be selected; and a coordinate recognition unit that recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; The recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, performs machine learning to determine the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

2. The cooking information providing system according to claim 1, A cooking information providing system characterized in that the emotion model is a model including a first coordinate axis representing one or more basic emotions and a second coordinate axis representing the strength of the basic emotions.

3. In the cooking information providing system according to claim 1 or 2, the recommended dish recognition unit recognizes the plurality of recommended dishes; The cooking information providing system is characterized in that the cooking information providing unit provides the user with the cooking information corresponding to each of the plurality of recommended dishes.

4. The cooking information providing system according to claim 3, a selection status recognition unit that recognizes a selection status of each of the recommended dishes, which is a status at the time when the recommended dish is selected by the user from the recommended dishes; The recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selection coordinates, the user information, and the selection status as learning data, performs machine learning to learn the correlation between the candidate dishes and the selection coordinates, the user information, and the selection status, and outputs the recommended dish selected from the plurality of candidate dishes.

5. The cooking information providing system according to claim 4, The cooking information providing system is characterized in that the selection status recognition unit recognizes, as the selection status for each of the plurality of recommended dishes, the number of selections, which is at least the number of times the recommended dish has been selected.

6. 6. The cooking information providing system according to claim 4 or claim 5, the recommended dish recognition unit uses each of the plurality of candidate dishes, the selection coordinates, the user information, and the selection status as learning data, and performs machine learning to determine the correlation between the candidate dish and the selection coordinates, the user information, and the selection status, as well as the recommendation level, which is the strength of the correlation, as the prediction model, and outputs the recommended dish selected from the plurality of candidate dishes and the recommendation level of the recommended dish; A cooking information providing system characterized in that the cooking information providing unit provides the user with the cooking information corresponding to each of the multiple recommended dishes along with the recommendation level, or in a format in which cooking information corresponding to the recommended dishes with higher recommendation levels is given priority.

7. A cooking information providing system that provides a user with cooking information that is information about recommended dishes that are dishes that a user is recommended to eat, a user information recognition unit that recognizes user information including at least one of biometric information, preferences, and an environment in the eating behavior of the user; a current emotion recognition unit that recognizes a current emotion, which is an emotion at the time when the user requests to acquire the recipe information; a recommended dish recognition unit that recognizes the recommended dish based on correlation data that represents a correlation between the user information and the current emotion and each of a plurality of candidate dishes that may become the recommended dish; a dish information providing unit that acquires the dish information from a dish information recognizing unit that recognizes information about a plurality of the candidate dishes and provides the dish information to the user; The cooking information provision system is characterized in that the current emotion recognition unit has an emotion model presentation unit that presents an emotion model, which is a two-dimensional or three-dimensional model whose coordinates are defined based on a plurality of basic emotions, to the user in a format in which any of the coordinates in the emotion model can be selected, and a coordinate recognition unit that recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion.

8. A method for providing a user with recipe information, the recipe information being information about recommended recipes that are recommended for the user to eat, comprising: a step in which a user information recognition unit recognizes user information including at least one of biometric information, preferences, and an environment in the eating behavior of the user; a current emotion recognition unit recognizing a current emotion that is an emotion at the time when the user desires to acquire the cooking information; a recommended dish recognition unit recognizing the recommended dish using a prediction model that inputs the user information and the current emotion and outputs the recommended dish; a dish information providing unit acquiring the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that may become the recommended dishes, and providing the dish information to the user; the step of recognizing the current emotion includes a process in which an emotion model presentation unit of the current emotion recognition unit presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any coordinate in the emotion model can be selected; and a process in which a coordinate recognition unit of the current emotion recognition unit recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; A method for providing dish information, characterized in that in the step of recognizing the recommended dish, the recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, performs machine learning on the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

9. A cooking information providing device that provides a user with cooking information that is information about recommended dishes that are dishes that a user is recommended to eat, a user information recognition unit that recognizes user information including at least one of biometric information, preferences, and an environment in the eating behavior of the user; a current emotion recognition unit that recognizes a current emotion, which is an emotion at the time when the user requests to acquire the recipe information; a recommended dish recognition unit that recognizes the recommended dishes using a prediction model that inputs the user information and the current emotion and outputs the recommended dishes; a dish information providing unit that obtains the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that may become the recommended dishes, and provides the dish information to the user; the current emotion recognition unit has an emotion model presentation unit that presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any of the coordinates in the emotion model can be selected; and a coordinate recognition unit that recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; The recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, performs machine learning to determine the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

10. A recipe information providing program that causes a computer to execute a recipe information providing method for providing a user with recipe information that is information about recommended recipes that are recommended for the user to eat, comprising: The computer, a step in which a user information recognition unit recognizes user information including at least one of biometric information, preferences, and an environment in the eating behavior of the user; a current emotion recognition unit recognizing a current emotion that is an emotion at the time when the user desires to acquire the cooking information; a recommended dish recognition unit recognizing the recommended dish using a prediction model that inputs the user information and the current emotion and outputs the recommended dish; a dish information providing unit acquiring the dish information from a dish information recognizing unit that recognizes information about a plurality of candidate dishes that are dishes used in the prediction model and that can be the recommended dishes, and providing the dish information to the user; Execute the step of recognizing the current emotion includes a process in which an emotion model presentation unit of the current emotion recognition unit presents to the user an emotion model, which is a two-dimensional model or a three-dimensional model, coordinates of which are defined based on a plurality of basic emotions, in a format in which any coordinate in the emotion model can be selected; and a process in which a coordinate recognition unit of the current emotion recognition unit recognizes selected coordinates, which are coordinates in the emotion model selected by the user, as the current emotion; A dish information providing program characterized in that in the step of recognizing the recommended dish, the recommended dish recognition unit uses as the prediction model each of the plurality of candidate dishes, the selected coordinates, and the user information as learning data, machine-learns the correlation between the candidate dishes and the selected coordinates and the user information, and selects and outputs the recommended dish from the plurality of candidate dishes.

11. 11. A recording medium having the cooking information providing program according to claim 10 recorded thereon, said recording medium being readable by said computer.

Images