system

Abstract

The system according to the embodiment aims to propose recipes that match the taste preferences and mood of the user. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an input unit, and a suggestion unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and models taste preferences. The input unit inputs the user's mood. The suggestion unit suggests recipes based on the information obtained by the analysis unit and the input unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has had the problem of making it difficult to suggest recipes that match a user's individual taste preferences and moods.

[0005] The system according to the embodiment aims to propose recipes that match the taste preferences and mood of the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an input unit, and a suggestion unit. The collection unit collects user information. The analysis unit analyzes the information collected by the collection unit and models taste preferences. The input unit inputs the user's mood. The suggestion unit suggests recipes based on the information obtained by the analysis unit and the input unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest recipes that match the taste preferences and mood of the user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A recipe suggestion system according to an embodiment of the present invention creates personalized recipes tailored to a user's order. In this recipe suggestion system, the user inputs information such as their hometown and favorite restaurant dishes, and the AI ​​analyzes this information to model the user's implicit taste preferences as a base flavor. Furthermore, the user inputs their current mood, and the AI ​​proposes original dishes or adaptations of existing dishes. These suggestions are made taking into account the ingredients available in the user's refrigerator. For example, a user might input information such as their hometown, favorite restaurant dishes, and favorite dishes they've eaten in the past. For example, they might input information such as "I'm from a city, my favorite dish is ramen, especially with a rich soup." This information is input to the AI. The AI ​​then analyzes the input information and models the user's implicit taste preferences as a base flavor. For example, if a user prefers rich soups, the AI ​​would model a "rich, full-bodied flavor" as a base flavor. Furthermore, the user inputs their current mood. For example, they might input "I want something light today." This information is also input to the AI. The generative AI considers the user's base flavor and their current mood to suggest original dishes or variations on existing dishes. For example, based on the information, "I like rich, full-bodied flavors, but I want to eat something light today," the generative AI will suggest an "adjusted version of a light, rich soup." Finally, the generative AI will suggest recipes taking into account the ingredients in the user's refrigerator. For example, if there is pork and cabbage in the refrigerator, the generative AI will suggest a recipe for "light, rich soup made with pork and cabbage." In this way, users can easily create their own original recipes. This allows the recipe suggestion system to suggest individual recipes based on the user's information.

[0029] A recipe suggestion system according to an embodiment includes a collection unit, an analysis unit, an input unit, and a suggestion unit. The collection unit collects user information. The user information includes, but is not limited to, age, gender, food preferences, and allergy information. The collection unit can collect, for example, information entered by the user about their hometown, favorite restaurant dishes, and dishes they have eaten in the past. The analysis unit analyzes the information collected by the collection unit and models taste preferences. The analysis unit can analyze the user's taste preferences using, for example, data mining technology or statistical analysis techniques. The analysis unit can also analyze past meal history and reviews to model taste preferences. For example, the analysis unit models taste preferences based on information about dishes that the user has previously rated highly. The input unit inputs the user's mood. For example, the input unit provides an interface for the user to input their current mood. The input unit can also automatically estimate the user's mood using emotion recognition technology. For example, the input unit analyzes the user's facial expressions and voice to estimate their mood. The suggestion unit suggests recipes based on the information obtained by the analysis unit and the input unit. The suggestion unit suggests, for example, original dishes or adaptations of existing dishes. The suggestion unit can also suggest recipes taking into account ingredients in the user's refrigerator. For example, the suggestion unit suggests waste-free recipes based on information about ingredients in the refrigerator. The suggestion unit can also consider ingredients in the refrigerator by linking with an inventory management system. This allows the recipe suggestion system according to the embodiment to suggest individual recipes based on user information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest recipes using an AI model that uses information obtained by the analysis unit and the input unit as input and outputs recipes.

[0030] The suggestion unit can suggest recipes based on ingredients in the user's refrigerator. For example, the suggestion unit suggests recipes based on information about ingredients in the refrigerator. The suggestion unit collects information about the types, quantities, expiration dates, and other information about ingredients in the refrigerator and suggests recipes based on this information. For example, if there is pork and cabbage in the refrigerator, the suggestion unit suggests a recipe for a "refreshing, rich soup made with pork and cabbage." The suggestion unit can also take into account ingredients in the refrigerator by linking with an inventory management system. For example, the suggestion unit suggests recipes based on ingredient information obtained from the inventory management system. This makes it possible to suggest recipes that do not waste ingredients by considering the ingredients in the user's refrigerator. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can suggest recipes using an AI model that inputs information about ingredients in the refrigerator and outputs recipes.

[0031] The collection unit can collect information about the user's hometown, dishes at favorite restaurants, and dishes eaten in the past. For example, the collection unit collects information about the user's hometown, dishes at favorite restaurants, and dishes eaten in the past input by the user. The collection unit can collect information about the user's hometown using user input or a location information service. For example, the collection unit collects information about dishes related to the user's hometown based on the information about the user's hometown input. The collection unit can also collect information about dishes at favorite restaurants based on the user's reviews and visit history. For example, the collection unit collects information about dishes at favorite restaurants based on information about restaurants the user has visited in the past. The collection unit can also collect information about dishes eaten in the past based on the user's eating history and photo data. For example, the collection unit analyzes photo data of dishes eaten in the past and collects dish information. This allows for collecting information based on the user's preferences, enabling more personalized recipe suggestions. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information entered by the user into a generation AI and cause the generation AI to collect information.

[0032] The analysis unit can analyze past meal history and reviews to model taste preferences. For example, the analysis unit can analyze past meal history and reviews to model taste preferences. The analysis unit can analyze past meal history based on a food diary or app log data. For example, the analysis unit can model taste preferences based on information about dishes the user has eaten in the past. The analysis unit can also analyze reviews based on online reviews and survey results. For example, the analysis unit can model taste preferences based on reviews of dishes that the user has given high ratings in the past. Furthermore, the analysis unit can comprehensively analyze the user's meal history and reviews to model detailed taste preferences. For example, the analysis unit can combine the user's meal history and reviews to model taste preferences. This enables more accurate modeling of taste preferences by analyzing the past meal history and reviews. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on past dining history and reviews into the generation AI and have the generation AI model taste preferences.

[0033] The suggestion unit can suggest original dishes or adapted versions of existing dishes. For example, the suggestion unit suggests original dishes or adapted versions of existing dishes. The suggestion unit can suggest original dishes using a method for generating new recipes or a method for adapting existing recipes. For example, the suggestion unit generates a new recipe based on the user's taste preferences or mood. The suggestion unit can also suggest adapted versions of existing dishes by changing the ingredients or cooking methods. For example, the suggestion unit can suggest adapted versions of existing dishes based on the ingredients in the user's refrigerator. This can provide the user with new cooking ideas by suggesting original dishes or adapted versions of existing dishes. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest recipes using an AI model that inputs information about the user's taste preferences and mood and outputs recipes for original dishes or adapted versions.

[0034] The suggestion unit can take into consideration ingredients in the refrigerator by cooperating with an inventory management system. The suggestion unit, for example, cooperates with the inventory management system to propose recipes based on ingredients in the refrigerator. The suggestion unit can propose waste-free recipes based on ingredient information acquired from the inventory management system. For example, the suggestion unit proposes recipes based on information such as the type, quantity, and expiration date of ingredients acquired from the inventory management system. The suggestion unit can also acquire ingredient information in the refrigerator in real time using a data linkage method with the inventory management system and reflect this in recipe suggestions. This allows ingredients in the refrigerator to be used without waste by cooperating with the inventory management system. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose recipes using an AI model that inputs ingredient information acquired from the inventory management system and outputs recipes.

[0035] The collection unit can analyze the user's past meal history and select an optimal information collection method. For example, the collection unit prioritizes collecting information on dishes the user has frequently eaten in the past. The collection unit can analyze the user's preferences based on the past meal history. For example, the collection unit can identify a preferred food genre based on information on dishes the user has eaten in the past. The collection unit can also collect information on a specific food genre from the user's past meal history. For example, the collection unit can collect related information based on the genre of dishes the user has eaten in the past. Furthermore, the collection unit can analyze the user's past meal history and collect information based on eating patterns. For example, the collection unit selects an optimal information collection method based on the user's eating patterns. This enables more effective information collection by analyzing the past meal history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past meal history data into a generation AI and cause the generation AI to execute a process of selecting an optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current health condition and dietary restrictions. For example, if the user is on a diet, the collection unit prioritizes collecting low-calorie dish information. The collection unit can filter the information based on the user's health condition and dietary restrictions. For example, if the user has an allergy, the collection unit collects allergen-free dish information. The collection unit can also collect nutritionally balanced dish information based on the user's health condition. For example, the collection unit prioritizes collecting nutritionally balanced dish information based on the user's health condition. This makes it possible to collect information based on the user's health condition and dietary restrictions. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's health condition and dietary restrictions into the generation AI and have the generation AI perform information filtering.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit collects information on dishes using local specialties. The collection unit can collect highly relevant information based on the geographical location information. For example, when the user is traveling, the collection unit prioritizes collecting information on dishes at the travel destination. The collection unit can also collect information on dishes using local ingredients based on the user's geographical location information. For example, when the user is in their hometown, the collection unit collects information on dishes using local ingredients. This makes it possible to collect information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to execute a process of preferentially collecting highly relevant information.

[0038] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects information about dishes shared by the user on social media. The collection unit can collect related information based on the social media activities. For example, the collection unit collects information about cooking accounts the user follows. The collection unit can also collect information about cooking genres in which the user is interested from the user's social media activities. For example, the collection unit collects related information based on information about dishes shared by the user on social media. This makes it possible to collect information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data into a generation AI and cause the generation AI to execute a process of collecting related information.

[0039] During analysis, the analysis unit can improve the accuracy of modeling taste preferences by referring to the user's past dining history and reviews. The analysis unit, for example, models taste preferences based on information about dishes that the user has given high ratings to in the past. The analysis unit can model taste preferences based on the past dining history and reviews. For example, the analysis unit models taste preferences based on information about dishes the user has eaten in the past. The analysis unit can also extract and model specific taste trends from the user's past dining history. For example, the analysis unit extracts specific taste trends based on the user's dining history. Furthermore, the analysis unit can analyze the user's reviews and model detailed taste preferences. For example, the analysis unit models detailed taste preferences based on the user's reviews. In this way, by referring to the past dining history and reviews, the accuracy of modeling taste preferences is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on past dining history and reviews into the generation AI and have the generation AI perform processes to improve the accuracy of modeling taste preferences.

[0040] During analysis, the analysis unit can model taste preferences based on the user's dietary restrictions and allergy information. For example, if the user has allergies, the analysis unit models taste preferences excluding allergens. The analysis unit can model taste preferences based on the user's dietary restrictions and allergy information. For example, if the user has specific dietary restrictions, the analysis unit models taste preferences based on those restrictions. The analysis unit can also model taste preferences that take nutritional balance into account depending on the user's health condition. For example, the analysis unit models taste preferences that take nutritional balance into account based on the user's health condition. This makes it possible to model taste preferences based on the user's dietary restrictions and allergy information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's dietary restrictions and allergy information into the generation AI and cause the generation AI to model taste preferences.

[0041] During analysis, the analysis unit can model taste preferences based on the user's geographical location information. For example, if the user is in a specific region, the analysis unit models taste preferences using local specialties of that region. The analysis unit can model taste preferences based on the geographical location information. For example, if the user is traveling, the analysis unit models taste preferences for cuisine at the travel destination. The analysis unit can also model taste preferences using local ingredients based on the user's geographical location information. For example, if the user is in their hometown, the analysis unit models taste preferences using local ingredients. This makes it possible to model taste preferences based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical location information data to a generation AI and cause the generation AI to model taste preferences.

[0042] During analysis, the analysis unit can analyze the user's social media activity and model taste preferences. The analysis unit, for example, models taste preferences based on information about dishes shared by the user on social media. The analysis unit can model taste preferences based on social media activity. For example, the analysis unit models taste preferences based on information about cooking accounts the user follows. The analysis unit can also model taste preferences for cooking genres the user is interested in from the user's social media activity. For example, the analysis unit models taste preferences based on information about dishes shared by the user on social media. This makes it possible to model taste preferences based on the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social media activity data to a generation AI and cause the generation AI to model taste preferences.

[0043] The input unit can select the optimal input method by referring to the user's past mood history when inputting data. For example, the input unit automatically displays moods that the user has frequently input in the past as candidates. The input unit can select the optimal input method based on the past mood history. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest a mood to be input during a specific time period based on the user's past mood history. For example, the input unit predicts a mood to be input during a specific time period based on the user's past mood history. This allows the optimal input method to be selected by referring to the past mood history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past mood history data to a generation AI and cause the generation AI to execute a process of selecting an optimal input method.

[0044] The input unit can input a mood based on the user's current health condition and dietary restrictions at the time of input. For example, if the user is on a diet, the input unit inputs a mood appropriate for dieting. The input unit can input a mood based on the user's health condition and dietary restrictions. For example, if the user has an allergy, the input unit inputs a mood appropriate for allergen-free dishes. The input unit can also input a mood that takes nutritional balance into consideration according to the user's health condition. For example, the input unit inputs a mood that takes nutritional balance into consideration based on the user's health condition. This makes it possible to input a mood that corresponds to the user's health condition and dietary restrictions. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the user's health condition and dietary restrictions into the generation AI and cause the generation AI to execute the mood input.

[0045] The input unit can input a mood based on the user's geographical location information at the time of input. For example, if the user is in a specific area, the input unit inputs a mood related to local specialties. The input unit can input a mood based on the geographical location information. For example, if the user is traveling, the input unit inputs a mood related to cuisine at the travel destination. The input unit can also input a mood related to local ingredients based on the user's geographical location information. For example, if the user is in their hometown, the input unit inputs a mood related to local ingredients. This makes it possible to input a mood based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input geographical location information data to the generation AI and cause the generation AI to execute the mood input.

[0046] The input unit can analyze the user's social media activity and input the mood at the time of input. For example, the input unit inputs the mood based on information about dishes shared by the user on social media. The input unit can input the mood based on the social media activity. For example, the input unit inputs the mood based on information about cooking accounts the user follows. The input unit can also input moods related to cooking genres in which the user is interested from the user's social media activity. For example, the input unit inputs the mood based on information about dishes shared by the user on social media. This makes it possible to input moods based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input social media activity data to the generation AI and cause the generation AI to execute mood input.

[0047] When suggesting a recipe, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past meal history and reviews. For example, the suggestion unit suggests recipes based on information about dishes that the user has given high ratings to in the past. The suggestion unit can suggest recipes based on the user's past meal history and reviews. For example, the suggestion unit suggests recipes based on information about dishes the user has eaten in the past. The suggestion unit can also suggest recipes related to a specific cuisine genre based on the user's past meal history. For example, the suggestion unit suggests recipes related to a specific cuisine genre based on the user's meal history. Furthermore, the suggestion unit can analyze the user's reviews and suggest detailed recipes. For example, the suggestion unit suggests detailed recipes based on the user's reviews. This improves the accuracy of recipe suggestions by referring to the past meal history and reviews. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data about the user's past meal history and reviews into the generation AI and cause the generation AI to perform processing to improve the accuracy of recipe suggestions.

[0048] The suggestion unit can make a recipe suggestion taking into consideration the user's dietary restrictions and allergy information. For example, if the user has allergies, the suggestion unit can suggest recipes that exclude allergens. The suggestion unit can suggest recipes based on the user's dietary restrictions and allergy information. For example, if the user has specific dietary restrictions, the suggestion unit can suggest recipes based on those restrictions. The suggestion unit can also suggest recipes that take nutritional balance into consideration according to the user's health condition. For example, the suggestion unit can suggest recipes that take nutritional balance into consideration based on the user's health condition. This makes it possible to suggest recipes based on the user's dietary restrictions and allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's dietary restrictions and allergy information into the generation AI and cause the generation AI to suggest recipes.

[0049] When suggesting a recipe, the suggestion unit can make suggestions based on the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest recipes using local specialties. The suggestion unit can suggest recipes based on the geographical location information. For example, if the user is traveling, the suggestion unit can suggest recipes for dishes from the travel destination. The suggestion unit can also suggest recipes using local ingredients based on the user's geographical location information. For example, if the user is in their hometown, the suggestion unit can suggest recipes using local ingredients. This makes it possible to suggest recipes based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical location information data to a generation AI and cause the generation AI to execute recipe suggestions.

[0050] The suggestion unit can analyze the user's social media activity and make suggestions when suggesting recipes. For example, the suggestion unit can suggest recipes based on information about dishes shared by the user on social media. The suggestion unit can suggest recipes based on the social media activity. For example, the suggestion unit can suggest recipes based on information about cooking accounts the user follows. The suggestion unit can also suggest recipes in cooking genres that the user is interested in based on the user's social media activity. For example, the suggestion unit can suggest recipes based on information about dishes shared by the user on social media. This makes it possible to suggest recipes based on the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input social media activity data into a generation AI and cause the generation AI to suggest recipes.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The suggestion unit can also suggest recipes based on the user's mealtimes. For example, it can suggest appropriate recipes depending on the time of day for breakfast, lunch, or dinner. For breakfast, it can suggest light, nutritionally balanced recipes, and for lunch, it can suggest recipes suitable for replenishing energy. For dinner, it can suggest recipes that are easy to digest. This makes it possible to suggest recipes according to the user's mealtimes.

[0053] The suggestion unit can also suggest recipes based on the user's meal frequency. For example, it can suggest a wide variety of recipes to a user who eats frequently, and suggest nutritious recipes to a user who eats infrequently. This makes it possible to suggest recipes according to the user's meal frequency.

[0054] The suggestion unit can also suggest recipes based on the location where the user is eating. For example, if the user is eating at home, the suggestion unit suggests recipes that can be easily made at home, and if the user is eating on the go, the suggestion unit suggests recipes that are easy to carry. This makes it possible to suggest recipes according to the location where the user is eating.

[0055] The suggestion unit can also suggest recipes based on the user's dietary goals. For example, it can suggest low-calorie, nutritionally balanced recipes to a user on a diet, and high-protein recipes to a user aiming to build muscle. This makes it possible to suggest recipes that suit the user's dietary goals.

[0056] The suggestion unit can also suggest recipes based on the user's meal budget. For example, it can suggest cost-effective recipes to a user with a limited budget, and suggest luxurious recipes to a user with a larger budget. This makes it possible to suggest recipes that fit the user's meal budget.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects user information, including age, gender, food preferences, allergy information, hometown, favorite restaurant dishes, and information on dishes eaten in the past. Step 2: The analysis unit analyzes the information collected by the collection unit and models taste preferences. The analysis unit uses data mining technology and statistical analysis methods to analyze past dining history and reviews, and models taste preferences based on information about dishes that users have previously rated highly. Step 3: The input unit inputs the user's mood. The input unit provides an interface for the user to input their current mood, and can also use emotion recognition technology to analyze the user's facial expressions and voice to automatically estimate their mood. Step 4: The suggestion unit suggests recipes based on the information obtained by the analysis unit and the input unit. The suggestion unit suggests original dishes or adaptations of existing dishes, and can also suggest waste-free recipes by taking into account the ingredients in the user's refrigerator. The suggestion unit can also take into account the ingredients in the refrigerator by linking with an inventory management system. The processing in the suggestion unit can also be performed using an AI model.

[0059] (Example 2) A recipe suggestion system according to an embodiment of the present invention creates personalized recipes tailored to a user's order. In this recipe suggestion system, the user inputs information such as their hometown and favorite restaurant dishes, and the AI ​​analyzes this information to model the user's implicit taste preferences as a base flavor. Furthermore, the user inputs their current mood, and the AI ​​proposes original dishes or adaptations of existing dishes. These suggestions are made taking into account the ingredients available in the user's refrigerator. For example, a user might input information such as their hometown, favorite restaurant dishes, and favorite dishes they've eaten in the past. For example, they might input information such as "I'm from a city, my favorite dish is ramen, especially with a rich soup." This information is input to the AI. The AI ​​then analyzes the input information and models the user's implicit taste preferences as a base flavor. For example, if a user prefers rich soups, the AI ​​would model a "rich, full-bodied flavor" as a base flavor. Furthermore, the user inputs their current mood. For example, they might input "I want something light today." This information is also input to the AI. The generative AI considers the user's base flavor and their current mood to suggest original dishes or variations on existing dishes. For example, based on the information, "I like rich, full-bodied flavors, but I want to eat something light today," the generative AI will suggest an "adjusted version of a light, rich soup." Finally, the generative AI will suggest recipes taking into account the ingredients in the user's refrigerator. For example, if there is pork and cabbage in the refrigerator, the generative AI will suggest a recipe for "light, rich soup made with pork and cabbage." In this way, users can easily create their own original recipes. This allows the recipe suggestion system to suggest individual recipes based on the user's information.

[0060] A recipe suggestion system according to an embodiment includes a collection unit, an analysis unit, an input unit, and a suggestion unit. The collection unit collects user information. The user information includes, but is not limited to, age, gender, food preferences, and allergy information. The collection unit can collect, for example, information entered by the user about their hometown, favorite restaurant dishes, and dishes they have eaten in the past. The analysis unit analyzes the information collected by the collection unit and models taste preferences. The analysis unit can analyze the user's taste preferences using, for example, data mining technology or statistical analysis techniques. The analysis unit can also analyze past meal history and reviews to model taste preferences. For example, the analysis unit models taste preferences based on information about dishes that the user has previously rated highly. The input unit inputs the user's mood. For example, the input unit provides an interface for the user to input their current mood. The input unit can also automatically estimate the user's mood using emotion recognition technology. For example, the input unit analyzes the user's facial expressions and voice to estimate their mood. The suggestion unit suggests recipes based on the information obtained by the analysis unit and the input unit. The suggestion unit suggests, for example, original dishes or adaptations of existing dishes. The suggestion unit can also suggest recipes taking into account ingredients in the user's refrigerator. For example, the suggestion unit suggests waste-free recipes based on information about ingredients in the refrigerator. The suggestion unit can also consider ingredients in the refrigerator by linking with an inventory management system. This allows the recipe suggestion system according to the embodiment to suggest individual recipes based on user information. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest recipes using an AI model that uses information obtained by the analysis unit and the input unit as input and outputs recipes.

[0061] The suggestion unit can suggest recipes based on ingredients in the user's refrigerator. For example, the suggestion unit suggests recipes based on information about ingredients in the refrigerator. The suggestion unit collects information about the types, quantities, expiration dates, and other information about ingredients in the refrigerator and suggests recipes based on this information. For example, if there is pork and cabbage in the refrigerator, the suggestion unit suggests a recipe for a "refreshing, rich soup made with pork and cabbage." The suggestion unit can also take into account ingredients in the refrigerator by linking with an inventory management system. For example, the suggestion unit suggests recipes based on ingredient information obtained from the inventory management system. This makes it possible to suggest recipes that do not waste ingredients by considering the ingredients in the user's refrigerator. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can suggest recipes using an AI model that inputs information about ingredients in the refrigerator and outputs recipes.

[0062] The collection unit can collect information about the user's hometown, dishes at favorite restaurants, and dishes eaten in the past. For example, the collection unit collects information about the user's hometown, dishes at favorite restaurants, and dishes eaten in the past input by the user. The collection unit can collect information about the user's hometown using user input or a location information service. For example, the collection unit collects information about dishes related to the user's hometown based on the information about the user's hometown input. The collection unit can also collect information about dishes at favorite restaurants based on the user's reviews and visit history. For example, the collection unit collects information about dishes at favorite restaurants based on information about restaurants the user has visited in the past. The collection unit can also collect information about dishes eaten in the past based on the user's eating history and photo data. For example, the collection unit analyzes photo data of dishes eaten in the past and collects dish information. This allows for collecting information based on the user's preferences, enabling more personalized recipe suggestions. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input information entered by the user into a generation AI and cause the generation AI to collect information.

[0063] The analysis unit can analyze past meal history and reviews to model taste preferences. For example, the analysis unit can analyze past meal history and reviews to model taste preferences. The analysis unit can analyze past meal history based on a food diary or app log data. For example, the analysis unit can model taste preferences based on information about dishes the user has eaten in the past. The analysis unit can also analyze reviews based on online reviews and survey results. For example, the analysis unit can model taste preferences based on reviews of dishes that the user has given high ratings in the past. Furthermore, the analysis unit can comprehensively analyze the user's meal history and reviews to model detailed taste preferences. For example, the analysis unit can combine the user's meal history and reviews to model taste preferences. This enables more accurate modeling of taste preferences by analyzing the past meal history and reviews. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on past dining history and reviews into the generation AI and have the generation AI model taste preferences.

[0064] The suggestion unit can suggest original dishes or adapted versions of existing dishes. For example, the suggestion unit suggests original dishes or adapted versions of existing dishes. The suggestion unit can suggest original dishes using a method for generating new recipes or a method for adapting existing recipes. For example, the suggestion unit generates a new recipe based on the user's taste preferences or mood. The suggestion unit can also suggest adapted versions of existing dishes by changing the ingredients or cooking methods. For example, the suggestion unit can suggest adapted versions of existing dishes based on the ingredients in the user's refrigerator. This can provide the user with new cooking ideas by suggesting original dishes or adapted versions of existing dishes. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can suggest recipes using an AI model that inputs information about the user's taste preferences and mood and outputs recipes for original dishes or adapted versions.

[0065] The suggestion unit can take into consideration ingredients in the refrigerator by cooperating with an inventory management system. The suggestion unit, for example, cooperates with the inventory management system to propose recipes based on ingredients in the refrigerator. The suggestion unit can propose waste-free recipes based on ingredient information acquired from the inventory management system. For example, the suggestion unit proposes recipes based on information such as the type, quantity, and expiration date of ingredients acquired from the inventory management system. The suggestion unit can also acquire ingredient information in the refrigerator in real time using a data linkage method with the inventory management system and reflect this in recipe suggestions. This allows ingredients in the refrigerator to be used without waste by cooperating with the inventory management system. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can propose recipes using an AI model that inputs ingredient information acquired from the inventory management system and outputs recipes.

[0066] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects information during a time when the user is able to relax. The collection unit can estimate the user's emotions using emotion recognition technology. For example, the collection unit analyzes the user's facial expressions and voice to estimate the user's emotions. Furthermore, if the user is relaxed, the collection unit can take time to collect detailed information. For example, the collection unit collects detailed information during a time when the user is relaxed. Furthermore, if the user is in a hurry, the collection unit can quickly collect the minimum amount of information necessary. For example, the collection unit asks simple questions and collects necessary information during a time when the user is in a hurry. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input emotion data estimated using emotion recognition technology into a generation AI and cause the generation AI to execute a process of adjusting the timing of information collection.

[0067] The collection unit can analyze the user's past meal history and select an optimal information collection method. For example, the collection unit prioritizes collecting information on dishes the user has frequently eaten in the past. The collection unit can analyze the user's preferences based on the past meal history. For example, the collection unit can identify a preferred food genre based on information on dishes the user has eaten in the past. The collection unit can also collect information on a specific food genre from the user's past meal history. For example, the collection unit can collect related information based on the genre of dishes the user has eaten in the past. Furthermore, the collection unit can analyze the user's past meal history and collect information based on eating patterns. For example, the collection unit selects an optimal information collection method based on the user's eating patterns. This enables more effective information collection by analyzing the past meal history. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past meal history data into a generation AI and cause the generation AI to execute a process of selecting an optimal information collection method.

[0068] When collecting information, the collection unit can filter the information based on the user's current health condition and dietary restrictions. For example, if the user is on a diet, the collection unit prioritizes collecting low-calorie dish information. The collection unit can filter the information based on the user's health condition and dietary restrictions. For example, if the user has an allergy, the collection unit collects allergen-free dish information. The collection unit can also collect nutritionally balanced dish information based on the user's health condition. For example, the collection unit prioritizes collecting nutritionally balanced dish information based on the user's health condition. This makes it possible to collect information based on the user's health condition and dietary restrictions. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's health condition and dietary restrictions into the generation AI and have the generation AI perform information filtering.

[0069] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting information about relaxing dishes. The collection unit can estimate the user's emotions using emotion recognition technology. For example, the collection unit analyzes the user's facial expressions and voice to estimate the user's emotions. Furthermore, the collection unit can prioritize collecting information about new dishes when the user is relaxed. For example, the collection unit collects information about new dishes when the user is relaxing. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting information about easy-to-make dishes when the user is in a hurry. For example, the collection unit collects information about easy-to-make dishes when the user is in a hurry. This enables more appropriate information collection by determining the priority of information according to the user's emotions. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input emotion data estimated using emotion recognition technology into a generation AI and cause the generation AI to perform a process of determining the priority of information.

[0070] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit collects information on dishes using local specialties. The collection unit can collect highly relevant information based on the geographical location information. For example, when the user is traveling, the collection unit prioritizes collecting information on dishes at the travel destination. The collection unit can also collect information on dishes using local ingredients based on the user's geographical location information. For example, when the user is in their hometown, the collection unit collects information on dishes using local ingredients. This makes it possible to collect information based on the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input geographical location information data to the generation AI and cause the generation AI to execute a process of preferentially collecting highly relevant information.

[0071] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects information about dishes shared by the user on social media. The collection unit can collect related information based on the social media activities. For example, the collection unit collects information about cooking accounts the user follows. The collection unit can also collect information about cooking genres in which the user is interested from the user's social media activities. For example, the collection unit collects related information based on information about dishes shared by the user on social media. This makes it possible to collect information based on the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input social media activity data into a generation AI and cause the generation AI to execute a process of collecting related information.

[0072] The analysis unit can estimate the user's emotions and adjust the taste preference modeling method based on the estimated user emotions. For example, when the user is relaxed, the analysis unit models detailed taste preferences. The analysis unit can estimate the user's emotions using emotion recognition technology. For example, the analysis unit analyzes the user's facial expressions and voice to estimate the emotion. The analysis unit can also model simplified taste preferences when the user is in a hurry. For example, the analysis unit models simplified taste preferences when the user is in a hurry. Furthermore, the analysis unit can also model spicy taste preferences when the user is excited. For example, the analysis unit models spicy taste preferences when the user is excited. This makes it possible to model taste preferences according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input emotion data estimated using emotion recognition technology into the generation AI and cause the generation AI to execute processing to adjust the taste preference modeling method.

[0073] During analysis, the analysis unit can improve the accuracy of modeling taste preferences by referring to the user's past dining history and reviews. The analysis unit, for example, models taste preferences based on information about dishes that the user has given high ratings to in the past. The analysis unit can model taste preferences based on the past dining history and reviews. For example, the analysis unit models taste preferences based on information about dishes the user has eaten in the past. The analysis unit can also extract and model specific taste trends from the user's past dining history. For example, the analysis unit extracts specific taste trends based on the user's dining history. Furthermore, the analysis unit can analyze the user's reviews and model detailed taste preferences. For example, the analysis unit models detailed taste preferences based on the user's reviews. In this way, by referring to the past dining history and reviews, the accuracy of modeling taste preferences is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on past dining history and reviews into the generation AI and have the generation AI perform processes to improve the accuracy of modeling taste preferences.

[0074] During analysis, the analysis unit can model taste preferences based on the user's dietary restrictions and allergy information. For example, if the user has allergies, the analysis unit models taste preferences excluding allergens. The analysis unit can model taste preferences based on the user's dietary restrictions and allergy information. For example, if the user has specific dietary restrictions, the analysis unit models taste preferences based on those restrictions. The analysis unit can also model taste preferences that take nutritional balance into account depending on the user's health condition. For example, the analysis unit models taste preferences that take nutritional balance into account based on the user's health condition. This makes it possible to model taste preferences based on the user's dietary restrictions and allergy information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the user's dietary restrictions and allergy information into the generation AI and cause the generation AI to model taste preferences.

[0075] The analysis unit can estimate the user's emotions and determine the priority of taste preference modeling based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit prioritizes modeling of relaxing taste preferences. The analysis unit can estimate the user's emotions using emotion recognition technology. For example, the analysis unit analyzes the user's facial expressions and voice to estimate the emotion. Furthermore, the analysis unit can prioritize modeling of new taste preferences when the user is relaxed. For example, the analysis unit models new taste preferences during times when the user is relaxing. Furthermore, the analysis unit can prioritize modeling of easy-to-make taste preferences when the user is in a hurry. For example, the analysis unit models easy-to-make taste preferences during times when the user is in a hurry. This allows the priority of modeling of taste preferences according to the user's emotions to be determined. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input emotion data estimated using emotion recognition technology into the generation AI and cause the generation AI to perform a process to determine priorities for modeling taste preferences.

[0076] During analysis, the analysis unit can model taste preferences based on the user's geographical location information. For example, if the user is in a specific region, the analysis unit models taste preferences using local specialties of that region. The analysis unit can model taste preferences based on the geographical location information. For example, if the user is traveling, the analysis unit models taste preferences for cuisine at the travel destination. The analysis unit can also model taste preferences using local ingredients based on the user's geographical location information. For example, if the user is in their hometown, the analysis unit models taste preferences using local ingredients. This makes it possible to model taste preferences based on the user's geographical location information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input geographical location information data to a generation AI and cause the generation AI to model taste preferences.

[0077] During analysis, the analysis unit can analyze the user's social media activity and model taste preferences. The analysis unit, for example, models taste preferences based on information about dishes shared by the user on social media. The analysis unit can model taste preferences based on social media activity. For example, the analysis unit models taste preferences based on information about cooking accounts the user follows. The analysis unit can also model taste preferences for cooking genres the user is interested in from the user's social media activity. For example, the analysis unit models taste preferences based on information about dishes shared by the user on social media. This makes it possible to model taste preferences based on the user's social media activity. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input social media activity data to a generation AI and cause the generation AI to model taste preferences.

[0078] The input unit can estimate the user's emotion and adjust the mood input method based on the estimated user emotion. For example, when the user is feeling stressed, the input unit provides a simple interface and minimizes input procedures. The input unit can estimate the user's emotion using emotion recognition technology. For example, the input unit analyzes the user's facial expressions and voice to estimate the emotion. Furthermore, when the user is relaxed, the input unit can provide detailed input options and suggest a customizable input method. For example, the input unit provides detailed input options when the user is relaxed. Furthermore, when the user is in a hurry, the input unit can prioritize voice input to enable quick mood input. For example, the input unit prioritizes voice input when the user is in a hurry to input mood. This allows the mood input method to be adjusted according to the user's emotion. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input emotion data estimated using emotion recognition technology into the generation AI and cause the generation AI to perform a process to adjust the method of mood input.

[0079] The input unit can select the optimal input method by referring to the user's past mood history when inputting data. For example, the input unit automatically displays moods that the user has frequently input in the past as candidates. The input unit can select the optimal input method based on the past mood history. For example, the input unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The input unit can also predict and suggest a mood to be input during a specific time period based on the user's past mood history. For example, the input unit predicts a mood to be input during a specific time period based on the user's past mood history. This allows the optimal input method to be selected by referring to the past mood history. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input past mood history data to a generation AI and cause the generation AI to execute a process of selecting an optimal input method.

[0080] The input unit can input a mood based on the user's current health condition and dietary restrictions at the time of input. For example, if the user is on a diet, the input unit inputs a mood appropriate for dieting. The input unit can input a mood based on the user's health condition and dietary restrictions. For example, if the user has an allergy, the input unit inputs a mood appropriate for allergen-free dishes. The input unit can also input a mood that takes nutritional balance into consideration according to the user's health condition. For example, the input unit inputs a mood that takes nutritional balance into consideration based on the user's health condition. This makes it possible to input a mood that corresponds to the user's health condition and dietary restrictions. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input data on the user's health condition and dietary restrictions into the generation AI and cause the generation AI to execute the mood input.

[0081] The input unit can estimate the user's emotion and determine the priority of mood input based on the estimated user's emotion. For example, if the user is feeling stressed, the input unit prioritizes input of a relaxing mood. The input unit can estimate the user's emotion using emotion recognition technology. For example, the input unit analyzes the user's facial expression or voice to estimate the emotion. Furthermore, the input unit can prioritize input of a new mood when the user is relaxed. For example, the input unit inputs a new mood during a time period when the user is relaxed. Furthermore, if the user is in a hurry, the input unit can prioritize input of a mood that can be easily input. For example, the input unit inputs a mood that can be easily input during a time period when the user is in a hurry. This allows the priority of mood input to be determined according to the user's emotion. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input emotion data estimated using emotion recognition technology into the generation AI and cause the generation AI to perform a process of determining the priority of mood input.

[0082] The input unit can input a mood based on the user's geographical location information at the time of input. For example, if the user is in a specific area, the input unit inputs a mood related to local specialties. The input unit can input a mood based on the geographical location information. For example, if the user is traveling, the input unit inputs a mood related to cuisine at the travel destination. The input unit can also input a mood related to local ingredients based on the user's geographical location information. For example, if the user is in their hometown, the input unit inputs a mood related to local ingredients. This makes it possible to input a mood based on the user's geographical location information. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input geographical location information data to the generation AI and cause the generation AI to execute the mood input.

[0083] The input unit can analyze the user's social media activity and input the mood at the time of input. For example, the input unit inputs the mood based on information about dishes shared by the user on social media. The input unit can input the mood based on the social media activity. For example, the input unit inputs the mood based on information about cooking accounts the user follows. The input unit can also input moods related to cooking genres in which the user is interested from the user's social media activity. For example, the input unit inputs the mood based on information about dishes shared by the user on social media. This makes it possible to input moods based on the user's social media activity. Some or all of the above-described processing in the input unit may be performed using, or without, AI. For example, the input unit can input social media activity data to the generation AI and cause the generation AI to execute mood input.

[0084] The suggestion unit can estimate the user's emotions and adjust the presentation method of recipe suggestions based on the estimated user's emotions. For example, the suggestion unit suggests detailed recipes when the user is relaxed. The suggestion unit can estimate the user's emotions using emotion recognition technology. For example, the suggestion unit analyzes the user's facial expressions and voice to estimate the user's emotions. The suggestion unit can also suggest simplified recipes when the user is in a hurry. For example, the suggestion unit suggests simplified recipes when the user is in a hurry. The suggestion unit can also suggest visually stimulating recipes when the user is excited. For example, the suggestion unit suggests visually stimulating recipes when the user is excited. This allows the presentation method of recipe suggestions to be adjusted according to the user's emotions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input emotion data estimated using emotion recognition technology into a generation AI and cause the generation AI to execute a process of adjusting the presentation method of recipe suggestions.

[0085] When suggesting a recipe, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past meal history and reviews. For example, the suggestion unit suggests recipes based on information about dishes that the user has given high ratings to in the past. The suggestion unit can suggest recipes based on the user's past meal history and reviews. For example, the suggestion unit suggests recipes based on information about dishes the user has eaten in the past. The suggestion unit can also suggest recipes related to a specific cuisine genre based on the user's past meal history. For example, the suggestion unit suggests recipes related to a specific cuisine genre based on the user's meal history. Furthermore, the suggestion unit can analyze the user's reviews and suggest detailed recipes. For example, the suggestion unit suggests detailed recipes based on the user's reviews. This improves the accuracy of recipe suggestions by referring to the past meal history and reviews. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input data about the user's past meal history and reviews into the generation AI and cause the generation AI to perform processing to improve the accuracy of recipe suggestions.

[0086] The suggestion unit can make a recipe suggestion taking into consideration the user's dietary restrictions and allergy information. For example, if the user has allergies, the suggestion unit can suggest recipes that exclude allergens. The suggestion unit can suggest recipes based on the user's dietary restrictions and allergy information. For example, if the user has specific dietary restrictions, the suggestion unit can suggest recipes based on those restrictions. The suggestion unit can also suggest recipes that take nutritional balance into consideration according to the user's health condition. For example, the suggestion unit can suggest recipes that take nutritional balance into consideration based on the user's health condition. This makes it possible to suggest recipes based on the user's dietary restrictions and allergy information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the user's dietary restrictions and allergy information into the generation AI and cause the generation AI to suggest recipes.

[0087] The suggestion unit can estimate the user's emotions and prioritize recipe suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting relaxing recipes. The suggestion unit can estimate the user's emotions using emotion recognition technology. For example, the suggestion unit can analyze the user's facial expressions and voice to estimate the user's emotions. The suggestion unit can also prioritize suggesting new recipes when the user is relaxed. For example, the suggestion unit can suggest new recipes when the user is relaxing. Furthermore, the suggestion unit can also prioritize suggesting easy recipes when the user is in a hurry. For example, the suggestion unit can suggest easy recipes when the user is in a hurry. This allows the priority of recipe suggestions to be determined according to the user's emotions. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input emotion data estimated using emotion recognition technology into a generation AI and cause the generation AI to execute a process of determining the priority of recipe suggestions.

[0088] When suggesting a recipe, the suggestion unit can make suggestions based on the user's geographical location information. For example, if the user is in a specific region, the suggestion unit can suggest recipes using local specialties. The suggestion unit can suggest recipes based on the geographical location information. For example, if the user is traveling, the suggestion unit can suggest recipes for dishes from the travel destination. The suggestion unit can also suggest recipes using local ingredients based on the user's geographical location information. For example, if the user is in their hometown, the suggestion unit can suggest recipes using local ingredients. This makes it possible to suggest recipes based on the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input geographical location information data to a generation AI and cause the generation AI to execute recipe suggestions.

[0089] The suggestion unit can analyze the user's social media activity and make suggestions when suggesting recipes. For example, the suggestion unit can suggest recipes based on information about dishes shared by the user on social media. The suggestion unit can suggest recipes based on the social media activity. For example, the suggestion unit can suggest recipes based on information about cooking accounts the user follows. The suggestion unit can also suggest recipes in cooking genres that the user is interested in based on the user's social media activity. For example, the suggestion unit can suggest recipes based on information about dishes shared by the user on social media. This makes it possible to suggest recipes based on the user's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input social media activity data into a generation AI and cause the generation AI to suggest recipes. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, input unit, and suggestion unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user information using the reception device 38 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to model taste preferences. The input unit inputs the user's mood using the reception device 38 of the smart device 14. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes based on the information obtained by the analysis unit and the input unit. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, input unit, and suggestion unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to model taste preferences. The input unit inputs the user's mood using the microphone 238 of the smart glasses 214. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes based on the information obtained by the analysis unit and the input unit. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, input unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to model taste preferences. The input unit inputs the user's mood using the microphone 238 of the headset-type terminal 314. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes based on the information obtained by the analysis unit and the input unit. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, input unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user information using the microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information to model taste preferences. The input unit inputs the user's mood using the microphone 238 of the robot 414. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests recipes based on the information obtained by the analysis unit and the input unit.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The suggestion unit can also suggest recipes based on the user's mealtimes. For example, it can suggest appropriate recipes depending on the time of day for breakfast, lunch, or dinner. For breakfast, it can suggest light, nutritionally balanced recipes, and for lunch, it can suggest recipes suitable for replenishing energy. For dinner, it can suggest recipes that are easy to digest. This makes it possible to suggest recipes according to the user's mealtimes.

[0092] The suggestion unit can also suggest recipes based on the user's meal frequency. For example, it can suggest a wide variety of recipes to a user who eats frequently, and suggest nutritious recipes to a user who eats infrequently. This makes it possible to suggest recipes according to the user's meal frequency.

[0093] The suggestion unit can also suggest recipes based on the location where the user is eating. For example, if the user is eating at home, the suggestion unit suggests recipes that can be easily made at home, and if the user is eating on the go, the suggestion unit suggests recipes that are easy to carry. This makes it possible to suggest recipes according to the location where the user is eating.

[0094] The suggestion unit can also suggest recipes based on the user's dietary goals. For example, it can suggest low-calorie, nutritionally balanced recipes to a user on a diet, and high-protein recipes to a user aiming to build muscle. This makes it possible to suggest recipes that suit the user's dietary goals.

[0095] The suggestion unit can also suggest recipes based on the user's meal budget. For example, it can suggest cost-effective recipes to a user with a limited budget, and suggest luxurious recipes to a user with a larger budget. This makes it possible to suggest recipes that fit the user's meal budget.

[0096] The suggestion unit can estimate the user's emotions and adjust the difficulty of the recipe based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest elaborate recipes, and if the user is stressed, the suggestion unit can suggest easy recipes. This makes it possible to adjust the difficulty of the recipes according to the user's emotions.

[0097] The suggestion unit can estimate the user's emotions and adjust the portion sizes of the recipe based on the estimated user's emotions. For example, if the user is excited, the suggestion unit can suggest a recipe for a large group, and if the user is relaxed, the suggestion unit can suggest a recipe for a small group. This makes it possible to adjust the portion sizes of the recipe according to the user's emotions.

[0098] The suggestion unit can estimate the user's emotions and adjust the cooking time of a recipe based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit can suggest a recipe that can be made in a short time, and if the user is relaxed, the suggestion unit can suggest a recipe that takes more time to make. This makes it possible to adjust the cooking time of a recipe according to the user's emotions.

[0099] The suggestion unit can estimate the user's emotions and adjust the seasoning of a recipe based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a recipe with a relaxing seasoning, and if the user is feeling excited, the suggestion unit can suggest a recipe with a stimulating seasoning. This makes it possible to adjust the seasoning of a recipe according to the user's emotions.

[0100] The suggestion unit can estimate the user's emotions and adjust the appearance of the recipe based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can suggest a simple and calm-looking recipe, and if the user is excited, the suggestion unit can suggest a colorful and vibrant-looking recipe. This makes it possible to adjust the appearance of the recipe according to the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects user information, including age, gender, food preferences, allergy information, hometown, favorite restaurant dishes, and information on dishes eaten in the past. Step 2: The analysis unit analyzes the information collected by the collection unit and models taste preferences. The analysis unit uses data mining technology and statistical analysis methods to analyze past dining history and reviews, and models taste preferences based on information about dishes that users have previously rated highly. Step 3: The input unit inputs the user's mood. The input unit provides an interface for the user to input their current mood, and can also use emotion recognition technology to analyze the user's facial expressions and voice to automatically estimate their mood. Step 4: The suggestion unit suggests recipes based on the information obtained by the analysis unit and the input unit. The suggestion unit suggests original dishes or adaptations of existing dishes, and can also suggest waste-free recipes by taking into account the ingredients in the user's refrigerator. The suggestion unit can also take into account the ingredients in the refrigerator by linking with an inventory management system. The processing in the suggestion unit can also be performed using an AI model.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user information; an analysis unit that analyzes the information collected by the collection unit and models taste preferences; an input unit for inputting the user's mood; a suggestion unit that suggests a recipe based on information obtained by the analysis unit and the input unit; Equipped with A system characterized by:

2. The proposal unit Suggest recipes based on the ingredients in the user's fridge 2. The system of claim 1.

3. The collecting unit Collect information about the user's hometown, favorite restaurant dishes, and dishes they have eaten in the past 2. The system of claim 1.

4. The analysis unit Analyzing past dining history and reviews to model taste preferences 2. The system of claim 1.

5. The proposal unit Suggest a creative dish or an adaptation of an existing dish 2. The system of claim 1.

6. The proposal unit Integrate with inventory management system to consider ingredients in refrigerator 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze the user's past eating history and select the optimal information collection method 2. The system of claim 1.

9. The collecting unit When collecting information, filter it based on the user's current health status or dietary restrictions 2. The system of claim 1.

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