System

Abstract

An object of the system according to the embodiment is to find an unvisited restaurant or a hidden famous store based on the user's preference or past eating experience and create a personalized map.SOLUTION: A system according to an embodiment includes a preference analysis unit, a map creation unit, and a restaurant recommendation unit. The preference analysis unit analyzes the user's preference and past eating experience. The map creation unit finds unvisited restaurants and hidden shops based on the data analyzed by the preference analysis unit, and creates a personalized map. The restaurant recommendation unit recommends a restaurant that takes advantage of the local specialty of the area based on the map created by the map creation unit.SELECTED DRAWING: Figure 1

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 technologies have had the problem that it is difficult to efficiently discover unexplored restaurants or hidden gems based on a user's preferences and past dining experiences.

[0005] The system according to the embodiment aims to discover unexplored restaurants and hidden gems based on the user's preferences and past dining experiences, and to create a personalized map. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference analysis unit, a map creation unit, and a restaurant recommendation unit. The preference analysis unit analyzes a user's preferences and past dining experiences. The map creation unit discovers unexplored restaurants and hidden gems based on the data analyzed by the preference analysis unit and creates a personalized map. The restaurant recommendation unit recommends restaurants that utilize local specialties based on the map created by the map creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can discover unexplored restaurants and hidden gems based on the user's preferences and past dining experiences, and create a personalized map. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The gastronomic adventure providing system according to an embodiment of the present invention is a system that analyzes a user's preferences and past dining experiences, discovers unexplored restaurants and hidden gems, and creates a personalized map, thereby enabling the gastronomic adventure providing system to provide the user with a new dining experience.

[0029] A gastronomic adventure providing system according to an embodiment includes a preference analysis unit, a map creation unit, and a restaurant recommendation unit. The preference analysis unit analyzes a user's preferences and past dining experiences. For example, the preference analysis unit analyzes reviews and ratings of restaurants the user has visited in the past, the types of dishes eaten, and their taste preferences. The preference analysis unit can also analyze the user's facial expressions and voice while eating to infer emotions in real time and understand preferences. For example, the preference analysis unit uses a camera and microphone to analyze the user's facial expressions and voice in real time while the user is eating. The preference analysis unit can also analyze the nutritional value and health benefits of ingredients based on the user's diet history and make health-related recommendations. For example, the preference analysis unit analyzes the user's diet history and evaluates the nutritional value and health benefits of ingredients. The map creation unit discovers unexplored restaurants and hidden gems based on the data analyzed by the preference analysis unit and creates a personalized map. For example, the map creation unit displays restaurants the user has not yet visited but that are likely to suit the user's preferences on a map. The map creation unit can also analyze the user's travel history and recommend restaurants based on frequently visited areas. For example, the map creation unit may analyze the user's travel history and identify frequently visited areas. Furthermore, the map creation unit may analyze the user's past reviews and ratings and prioritize the display of restaurants based on specific evaluation criteria. For example, the map creation unit may analyze the user's past reviews and ratings and prioritize the display of restaurants based on specific evaluation criteria. The restaurant recommendation unit recommends restaurants that utilize local specialties based on the map created by the map creation unit. For example, the restaurant recommendation unit may recommend restaurants that serve dishes using local specialties or restaurants that serve creative dishes using local ingredients. The restaurant recommendation unit may also analyze information about producers of local specialties and recommend restaurants that collaborate with producers. For example, the restaurant recommendation unit may identify and recommend restaurants that partner with local farmers or fishermen. Furthermore, the restaurant recommendation unit may analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those specialties. For example, the restaurant recommendation unit may identify and recommend restaurants that serve dishes using traditional cooking methods and recipes.As a result, the gastronomic adventure providing system according to the embodiment can provide users with new dining experiences. For example, users can discover hidden gems they have never visited before, or enjoy dishes that make use of local specialties. Furthermore, users can enjoy discovering new flavors by trying cuisine from different cultures.

[0030] The preference analysis unit can analyze the nutritional value and health benefits of ingredients based on the user's dietary history and make health-related suggestions. For example, the preference analysis unit analyzes the user's dietary history and evaluates the nutritional value and health benefits of ingredients. For example, it analyzes the nutritional components of dishes eaten in the past and suggests balanced meals. This makes it possible to suggest meals tailored to the user's health condition.

[0031] The preference analysis unit can analyze the user's meal preferences by time of day and season based on the user's meal history and suggest optimal meal timings. The preference analysis unit, for example, analyzes the user's meal history and identifies the user's meal preferences by time of day and season. For example, it suggests dishes that the user likes in a particular season based on past data. This can optimize the user's meal timings and provide a more satisfying dining experience.

[0032] The preference analysis unit can analyze the user's eating habits with friends and family based on the user's eating history and make group meal suggestions. The preference analysis unit, for example, analyzes the user's eating history and identifies the user's eating habits with friends and family. For example, it analyzes the preferences of people who have eaten together in the past and makes group meal suggestions. This makes it possible to provide a more enjoyable dining experience by making group meal suggestions.

[0033] The map creation unit can analyze the user's movement history and recommend restaurants based on frequently visited areas. For example, the map creation unit analyzes the user's movement history and identifies frequently visited areas. For example, the map creation unit recommends restaurants located in areas that the user frequently visits. This allows the user to have a more convenient dining experience by recommending restaurants based on frequently visited areas.

[0034] The map creation unit can analyze the user's past reviews and ratings and prioritize displaying restaurants based on specific evaluation criteria. The map creation unit, for example, analyzes the user's past reviews and ratings and prioritize displaying restaurants based on specific evaluation criteria. For example, highly rated restaurants are prioritized on the map. This allows restaurants to be prioritized based on the user's evaluation criteria, providing a more satisfying dining experience.

[0035] The map creation unit can recommend restaurants at travel destinations based on the user's travel history. The map creation unit, for example, analyzes the user's travel history and recommends restaurants at travel destinations. For example, it suggests restaurants in the area based on data on travel destinations visited in the past. This allows the user to enrich their dining experience during their trip by recommending restaurants at their travel destinations.

[0036] The map creation unit can create a map that combines activities other than eating based on the user's hobbies and interests. The map creation unit, for example, analyzes the user's hobbies and interests and creates a map that combines activities other than eating. For example, a map that combines tourist spots, events, and restaurants is provided. This allows the user to have a more fulfilling experience by providing a map that combines activities other than eating based on the user's hobbies and interests.

[0037] The restaurant recommendation unit can analyze information about producers of local specialty products and recommend restaurants that collaborate with producers. For example, the restaurant recommendation unit can analyze information about producers of local specialty products and recommend restaurants that collaborate with producers. For example, it can identify and recommend restaurants that partner with local farmers or fishermen. This makes it possible to recommend restaurants that make use of local specialty products and provide a dining experience that brings out the charm of the region.

[0038] The restaurant recommendation unit can analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those. For example, the restaurant recommendation unit analyzes the history and culture of local specialties and recommends restaurants that serve dishes based on those. For example, it identifies and recommends restaurants that serve dishes using traditional cooking methods and recipes. This makes it possible to provide a deeper dining experience by recommending restaurants that serve dishes based on the history and culture of the region.

[0039] The restaurant recommendation unit can analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those. For example, the restaurant recommendation unit analyzes the history and culture of local specialties and recommends restaurants that serve dishes based on those. For example, it identifies and recommends restaurants that serve dishes using traditional cooking methods and recipes. This makes it possible to provide a deeper dining experience by recommending restaurants that serve dishes based on the history and culture of the region.

[0040] The restaurant recommendation unit can recommend cooking classes and events using local specialties. For example, the restaurant recommendation unit recommends cooking classes and events using local specialties. For example, if a local chef is holding a cooking class using local specialties, the unit provides that information to the user. This makes it possible to provide a deeper dining experience by recommending cooking classes and events using local specialties.

[0041] The restaurant recommendation unit can provide recipes for dishes using local specialties and suggest cooking at home. The restaurant recommendation unit can, for example, provide recipes for dishes using local specialties and suggest cooking at home. For example, it can provide the user with simple recipes using local specialties and encourage cooking at home. In this way, by providing recipes for dishes using local specialties, the user can enjoy the specialties at home.

[0042] The restaurant recommendation unit can recommend cuisine genres that the user has not tried based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and recommends cuisine genres that the user has not tried. For example, it can suggest ethnic cuisine or fusion cuisine that the user has not tried yet. This can provide a new dining experience by recommending cuisine genres that the user has not tried.

[0043] The restaurant recommendation unit can suggest ingredients and cooking methods from different cultures based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and suggests ingredients and cooking methods from different cultures. For example, it can suggest ingredients and cooking methods for Asian cuisine or Mediterranean cuisine that the user has not tried yet. This allows the user to have a new dining experience by suggesting ingredients and cooking methods from different cultures.

[0044] The restaurant recommendation unit can suggest entertainment to be combined with meals (for example, live music or theater) based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and suggests entertainment to be combined with meals. For example, it suggests restaurants where you can enjoy live music or restaurants with theaters. This makes it possible to provide a more fulfilling dining experience by suggesting combinations of meals and entertainment.

[0045] The restaurant recommendation unit can propose travel plans combined with meals based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and proposes travel plans combined with meals. For example, it proposes gourmet tours or gourmet trips in a specific area. This makes it possible to provide a more fulfilling travel experience by proposing plans that combine meals and travel.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The gastronomic adventure system can also analyze the calorie consumption of the user during meals and make health-related suggestions. For example, it can analyze the calories consumed by the user during meals and suggest balanced meals. It can also suggest appropriate calorie intake based on the user's exercise and activity level. This allows for a healthier eating experience by making meal suggestions tailored to the user's health condition.

[0048] The Gastronomic Adventure Providing System can also analyze the user's allergy information and make safe meal suggestions. For example, if a user is allergic to a specific ingredient, it can recommend restaurants that avoid that ingredient. It can also recommend restaurants that use safe ingredients based on the user's allergy information. This allows users to enjoy their meals with peace of mind by making safe meal suggestions based on their allergy information.

[0049] The gastronomic adventure system can also analyze the origins of the ingredients used in a user's meal and recommend restaurants that use locally produced ingredients. For example, if a user prefers ingredients from a particular region, it can recommend restaurants that use ingredients from that region. Also, if the user places importance on local production and consumption, it can recommend restaurants that are based on that philosophy. This allows the system to provide a more locally rooted dining experience by recommending restaurants based on the origins of the ingredients used by the user.

[0050] The gastronomic adventure providing system can also analyze the cooking method of the ingredients in the user's meal and recommend restaurants that serve that particular cooking method. For example, if the user prefers a particular cooking method, it can recommend restaurants that serve that cooking method. Also, if the user avoids a particular cooking method, it can recommend restaurants that avoid that cooking method. In this way, restaurant recommendations based on the user's cooking method preferences can provide a more satisfying dining experience.

[0051] The gastronomic adventure system can also analyze the nutritional value of ingredients in a user's meals and suggest nutritionally balanced meals. For example, if a user prioritizes a particular nutrient, it can recommend restaurants that use ingredients rich in that nutrient. It can also recommend restaurants that use appropriate ingredients based on the user's nutritional balance. This allows for a healthier dining experience by recommending restaurants based on the user's nutritional preferences.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The preference analysis unit analyzes the user's preferences and past dining experiences. For example, it analyzes reviews and ratings of restaurants the user has visited in the past, as well as the types of food eaten and their taste preferences. It can also analyze the user's facial expressions and voice while eating to estimate their emotions in real time and understand their preferences. It can also analyze the nutritional value and health benefits of ingredients based on the user's eating history and make health-related suggestions. Step 2: The map creation unit discovers unexplored restaurants and hidden gems based on the data analyzed by the preference analysis unit, and creates a personalized map. For example, it displays restaurants on the map that the user has not yet visited but that may suit their tastes. It can also analyze the user's travel history and recommend restaurants based on frequently visited areas. It can also analyze the user's past reviews and ratings and prioritize the display of restaurants based on specific rating criteria. Step 3: The restaurant recommendation unit recommends restaurants that utilize local specialties based on the map created by the map creation unit. For example, it can recommend restaurants that serve dishes using local specialties or restaurants that serve creative dishes using local ingredients. It can also analyze information about producers of local specialties and recommend restaurants that collaborate with producers. It can also analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those.

[0054] (Example 2) The gastronomic adventure providing system according to an embodiment of the present invention is a system that analyzes a user's preferences and past dining experiences, discovers unexplored restaurants and hidden gems, and creates a personalized map, thereby enabling the gastronomic adventure providing system to provide the user with a new dining experience.

[0055] A gastronomic adventure providing system according to an embodiment includes a preference analysis unit, a map creation unit, and a restaurant recommendation unit. The preference analysis unit analyzes a user's preferences and past dining experiences. For example, the preference analysis unit analyzes reviews and ratings of restaurants the user has visited in the past, the types of dishes eaten, and their taste preferences. The preference analysis unit can also analyze the user's facial expressions and voice while eating to infer emotions in real time and understand preferences. For example, the preference analysis unit uses a camera and microphone to analyze the user's facial expressions and voice in real time while the user is eating. The preference analysis unit can also analyze the nutritional value and health benefits of ingredients based on the user's diet history and make health-related recommendations. For example, the preference analysis unit analyzes the user's diet history and evaluates the nutritional value and health benefits of ingredients. The map creation unit discovers unexplored restaurants and hidden gems based on the data analyzed by the preference analysis unit and creates a personalized map. For example, the map creation unit displays restaurants the user has not yet visited but that are likely to suit the user's preferences on a map. The map creation unit can also analyze the user's travel history and recommend restaurants based on frequently visited areas. For example, the map creation unit may analyze the user's travel history and identify frequently visited areas. Furthermore, the map creation unit may analyze the user's past reviews and ratings and prioritize the display of restaurants based on specific evaluation criteria. For example, the map creation unit may analyze the user's past reviews and ratings and prioritize the display of restaurants based on specific evaluation criteria. The restaurant recommendation unit recommends restaurants that utilize local specialties based on the map created by the map creation unit. For example, the restaurant recommendation unit may recommend restaurants that serve dishes using local specialties or restaurants that serve creative dishes using local ingredients. The restaurant recommendation unit may also analyze information about producers of local specialties and recommend restaurants that collaborate with producers. For example, the restaurant recommendation unit may identify and recommend restaurants that partner with local farmers or fishermen. Furthermore, the restaurant recommendation unit may analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those specialties. For example, the restaurant recommendation unit may identify and recommend restaurants that serve dishes using traditional cooking methods and recipes.As a result, the gastronomic adventure providing system according to the embodiment can provide users with new dining experiences. For example, users can discover hidden gems they have never visited before, or enjoy dishes that make use of local specialties. Furthermore, users can enjoy discovering new flavors by trying cuisine from different cultures.

[0056] The preference analysis unit analyzes the user's facial expressions and voice while eating, and can estimate emotions and grasp preferences in real time. For example, while the user is eating, the preference analysis unit analyzes facial expressions and voice in real time using a camera and microphone. For example, it detects smiling faces, satisfied expressions, voices of joy, etc., and estimates the user's preferences based on this data. In this way, by analyzing the user's emotions in real time, it is possible to grasp preferences with greater accuracy.

[0057] The preference analysis unit can analyze the nutritional value and health benefits of ingredients based on the user's dietary history and make health-related suggestions. For example, the preference analysis unit analyzes the user's dietary history and evaluates the nutritional value and health benefits of ingredients. For example, it analyzes the nutritional components of dishes eaten in the past and suggests balanced meals. This makes it possible to suggest meals tailored to the user's health condition.

[0058] The preference analysis unit can use the emotion estimation function to analyze the emotions felt by the user while eating and identify dishes that elicit positive emotions. For example, the preference analysis unit analyzes the emotions felt by the user while eating and identifies dishes that elicit positive emotions. For example, based on past meal data, it identifies dishes that the user found particularly satisfying and suggests similar dishes. In this way, by identifying dishes that elicit positive emotions in the user, it is possible to provide a more satisfying dining experience.

[0059] The preference analysis unit can analyze the user's meal preferences by time of day and season based on the user's meal history and suggest optimal meal timings. The preference analysis unit, for example, analyzes the user's meal history and identifies the user's meal preferences by time of day and season. For example, it suggests dishes that the user likes in a particular season based on past data. This can optimize the user's meal timings and provide a more satisfying dining experience.

[0060] The preference analysis unit can analyze the user's eating habits with friends and family based on the user's eating history and make group meal suggestions. The preference analysis unit, for example, analyzes the user's eating history and identifies the user's eating habits with friends and family. For example, it analyzes the preferences of people who have eaten together in the past and makes group meal suggestions. This makes it possible to provide a more enjoyable dining experience by making group meal suggestions.

[0061] The map creation unit can analyze the user's movement history and recommend restaurants based on frequently visited areas. For example, the map creation unit analyzes the user's movement history and identifies frequently visited areas. For example, the map creation unit recommends restaurants located in areas that the user frequently visits. This allows the user to have a more convenient dining experience by recommending restaurants based on frequently visited areas.

[0062] The map creation unit can analyze the user's past reviews and ratings and prioritize displaying restaurants based on specific evaluation criteria. The map creation unit, for example, analyzes the user's past reviews and ratings and prioritize displaying restaurants based on specific evaluation criteria. For example, highly rated restaurants are prioritized on the map. This allows restaurants to be prioritized based on the user's evaluation criteria, providing a more satisfying dining experience.

[0063] The map creation unit can use the emotion estimation function to analyze the emotions of a user at a restaurant visited by the user and identify areas that elicit positive emotions. For example, the map creation unit uses the emotion estimation function to analyze the emotions of a user at a restaurant visited by the user and identify areas that elicit positive emotions. For example, the map creation unit can identify areas that the user is particularly satisfied with and recommend restaurants within those areas. In this way, by identifying areas that elicit positive emotions from the user, a more satisfying dining experience can be provided.

[0064] The map creation unit can recommend restaurants at travel destinations based on the user's travel history. The map creation unit, for example, analyzes the user's travel history and recommends restaurants at travel destinations. For example, it suggests restaurants in the area based on data on travel destinations visited in the past. This allows the user to enrich their dining experience during their trip by recommending restaurants at their travel destinations.

[0065] The map creation unit can create a map that combines activities other than eating based on the user's hobbies and interests. The map creation unit, for example, analyzes the user's hobbies and interests and creates a map that combines activities other than eating. For example, a map that combines tourist spots, events, and restaurants is provided. This allows the user to have a more fulfilling experience by providing a map that combines activities other than eating based on the user's hobbies and interests.

[0066] The map creation unit can use the emotion estimation function to analyze the emotion a user feels in a specific area and recommend an area based on the emotion. For example, the map creation unit can use the emotion estimation function to analyze the emotion a user feels in a specific area and recommend an area based on the emotion. For example, the map creation unit can identify an area that the user is particularly satisfied with and recommend that area. In this way, by recommending an area based on the user's emotion, a more satisfying experience can be provided.

[0067] The restaurant recommendation unit can analyze information about producers of local specialty products and recommend restaurants that collaborate with producers. For example, the restaurant recommendation unit can analyze information about producers of local specialty products and recommend restaurants that collaborate with producers. For example, it can identify and recommend restaurants that partner with local farmers or fishermen. This makes it possible to recommend restaurants that make use of local specialty products and provide a dining experience that brings out the charm of the region.

[0068] The restaurant recommendation unit can analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those. For example, the restaurant recommendation unit analyzes the history and culture of local specialties and recommends restaurants that serve dishes based on those. For example, it identifies and recommends restaurants that serve dishes using traditional cooking methods and recipes. This makes it possible to provide a deeper dining experience by recommending restaurants that serve dishes based on the history and culture of the region.

[0069] The restaurant recommendation unit can analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those. For example, the restaurant recommendation unit analyzes the history and culture of local specialties and recommends restaurants that serve dishes based on those. For example, it identifies and recommends restaurants that serve dishes using traditional cooking methods and recipes. This makes it possible to provide a deeper dining experience by recommending restaurants that serve dishes based on the history and culture of the region.

[0070] The restaurant recommendation unit can use the emotion estimation function to analyze the emotion a user feels toward local specialties and identify specialties that elicit positive emotions. For example, the restaurant recommendation unit uses the emotion estimation function to analyze the emotion a user feels toward local specialties and identify specialties that elicit positive emotions. For example, the restaurant recommendation unit suggests dishes based on local specialties that the user particularly likes. In this way, by identifying specialties that elicit positive emotions from the user, a more satisfying dining experience can be provided.

[0071] The restaurant recommendation unit can recommend cooking classes and events using local specialties. For example, the restaurant recommendation unit recommends cooking classes and events using local specialties. For example, if a local chef is holding a cooking class using local specialties, the unit provides that information to the user. This makes it possible to provide a deeper dining experience by recommending cooking classes and events using local specialties.

[0072] The restaurant recommendation unit can provide recipes for dishes using local specialties and suggest cooking at home. The restaurant recommendation unit can, for example, provide recipes for dishes using local specialties and suggest cooking at home. For example, it can provide the user with simple recipes using local specialties and encourage cooking at home. In this way, by providing recipes for dishes using local specialties, the user can enjoy the specialties at home.

[0073] The restaurant recommendation unit can use the emotion estimation function to analyze the emotion a user feels toward specific local specialties and suggest a combination of local specialties based on the emotion. For example, the restaurant recommendation unit can use the emotion estimation function to analyze the emotion a user feels toward specific local specialties and suggest a combination of local specialties based on the emotion. For example, the restaurant recommendation unit can suggest a dish that combines local specialties that the user particularly likes. In this way, by suggesting a combination of local specialties based on the user's emotion, a more satisfying dining experience can be provided.

[0074] The restaurant recommendation unit can recommend cuisine genres that the user has not tried based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and recommends cuisine genres that the user has not tried. For example, it can suggest ethnic cuisine or fusion cuisine that the user has not tried yet. This can provide a new dining experience by recommending cuisine genres that the user has not tried.

[0075] The restaurant recommendation unit can suggest ingredients and cooking methods from different cultures based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and suggests ingredients and cooking methods from different cultures. For example, it can suggest ingredients and cooking methods for Asian cuisine or Mediterranean cuisine that the user has not tried yet. This allows the user to have a new dining experience by suggesting ingredients and cooking methods from different cultures.

[0076] The restaurant recommendation unit can suggest entertainment to be combined with meals (for example, live music or theater) based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and suggests entertainment to be combined with meals. For example, it suggests restaurants where you can enjoy live music or restaurants with theaters. This makes it possible to provide a more fulfilling dining experience by suggesting combinations of meals and entertainment.

[0077] The restaurant recommendation unit can propose travel plans combined with meals based on the user's dining history. For example, the restaurant recommendation unit analyzes the user's dining history and proposes travel plans combined with meals. For example, it proposes gourmet tours or gourmet trips in a specific area. This makes it possible to provide a more fulfilling travel experience by proposing plans that combine meals and travel.

[0078] The restaurant recommendation unit can use the emotion estimation function to analyze the emotions a user feels about a particular dining experience and suggest a new dining experience based on the emotions. For example, the restaurant recommendation unit can use the emotion estimation function to analyze the emotions a user feels about a particular dining experience and suggest a new dining experience based on the emotions. For example, the restaurant recommendation unit can make a new suggestion based on a dining experience that the user found particularly satisfying. This makes it possible to provide a more satisfying dining experience by suggesting a new dining experience based on the user's emotions.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The gastronomic adventure providing system can also analyze the content of conversations users have while eating and recommend restaurants based on the topic of the conversation. For example, if a user talks about a particular dish or ingredient during a meal, it can recommend restaurants related to that topic. Also, if a user talks about a particular region or culture, it can recommend restaurants related to that region or culture. This allows for restaurant recommendations based on the content of the user's conversation, providing a more personalized dining experience.

[0081] The gastronomic adventure providing system can also analyze the user's posture and movements while eating to evaluate the comfort of the meal. For example, it can analyze whether the user is relaxed and enjoying the meal or is nervous, and recommend restaurants that offer a comfortable dining environment. In addition, if the user repeatedly adopts a certain posture or movements, it can evaluate the comfort of the meal based on those movements. This allows for a more comfortable dining experience by recommending restaurants based on the user's posture and movements.

[0082] The gastronomic adventure providing system can also analyze the user's musical preferences during meals and suggest music and food pairings. For example, if a user likes a particular genre of music, it can recommend restaurants that serve that genre of music. Also, if a user likes a particular artist or song, it can recommend restaurants that serve that artist or song. This allows for restaurant recommendations based on the user's musical preferences, providing a more enjoyable dining experience.

[0083] The gastronomic adventure system can also analyze a user's scent preferences during meals and suggest combinations of scents and food. For example, if a user likes a particular scent, it can recommend restaurants that serve that scent. Also, if a user avoids a particular scent, it can recommend restaurants that avoid that scent. This allows for restaurant recommendations based on the user's scent preferences, providing a more satisfying dining experience.

[0084] The gastronomic adventure system can also analyze a user's visual preferences while dining and suggest combinations of visual elements with food. For example, if a user likes a particular color or design, it can recommend restaurants that offer that visual element. Also, if a user avoids a particular visual element, it can recommend restaurants that avoid that element. This allows for restaurant recommendations based on the user's visual preferences, providing a more satisfying dining experience.

[0085] The gastronomic adventure system can also analyze the calorie consumption of the user during meals and make health-related suggestions. For example, it can analyze the calories consumed by the user during meals and suggest balanced meals. It can also suggest appropriate calorie intake based on the user's exercise and activity level. This allows for a healthier eating experience by making meal suggestions tailored to the user's health condition.

[0086] The Gastronomic Adventure Providing System can also analyze the user's allergy information and make safe meal suggestions. For example, if a user is allergic to a specific ingredient, it can recommend restaurants that avoid that ingredient. It can also recommend restaurants that use safe ingredients based on the user's allergy information. This allows users to enjoy their meals with peace of mind by making safe meal suggestions based on their allergy information.

[0087] The gastronomic adventure system can also analyze the origins of the ingredients used in a user's meal and recommend restaurants that use locally produced ingredients. For example, if a user prefers ingredients from a particular region, it can recommend restaurants that use ingredients from that region. Also, if the user places importance on local production and consumption, it can recommend restaurants that are based on that philosophy. This allows the system to provide a more locally rooted dining experience by recommending restaurants based on the origins of the ingredients used by the user.

[0088] The gastronomic adventure providing system can also analyze the cooking method of the ingredients in the user's meal and recommend restaurants that serve that particular cooking method. For example, if the user prefers a particular cooking method, it can recommend restaurants that serve that cooking method. Also, if the user avoids a particular cooking method, it can recommend restaurants that avoid that cooking method. In this way, restaurant recommendations based on the user's cooking method preferences can provide a more satisfying dining experience.

[0089] The gastronomic adventure system can also analyze the nutritional value of ingredients in a user's meals and suggest nutritionally balanced meals. For example, if a user prioritizes a particular nutrient, it can recommend restaurants that use ingredients rich in that nutrient. It can also recommend restaurants that use appropriate ingredients based on the user's nutritional balance. This allows for a healthier dining experience by recommending restaurants based on the user's nutritional preferences.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The preference analysis unit analyzes the user's preferences and past dining experiences. For example, it analyzes reviews and ratings of restaurants the user has visited in the past, as well as the types of food eaten and their taste preferences. It can also analyze the user's facial expressions and voice while eating to estimate their emotions in real time and understand their preferences. It can also analyze the nutritional value and health benefits of ingredients based on the user's eating history and make health-related suggestions. Step 2: The map creation unit discovers unexplored restaurants and hidden gems based on the data analyzed by the preference analysis unit, and creates a personalized map. For example, it displays restaurants on the map that the user has not yet visited but that may suit their tastes. It can also analyze the user's travel history and recommend restaurants based on frequently visited areas. It can also analyze the user's past reviews and ratings and prioritize the display of restaurants based on specific rating criteria. Step 3: The restaurant recommendation unit recommends restaurants that utilize local specialties based on the map created by the map creation unit. For example, it can recommend restaurants that serve dishes using local specialties or restaurants that serve creative dishes using local ingredients. It can also analyze information about producers of local specialties and recommend restaurants that collaborate with producers. It can also analyze the history and culture of local specialties and recommend restaurants that serve dishes based on those.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 7, the 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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 expressed, and when they approach the ideal, a state of pleasure is expressed. 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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. [Explanation of symbols]

[0159] 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 preference analysis unit that analyzes the user's preferences and past eating experiences; a map creation unit that discovers unexplored restaurants and hidden gems based on the data analyzed by the preference analysis unit and creates a personalized map; a restaurant recommendation unit that recommends restaurants that utilize local specialties based on the map created by the map creation unit. A system characterized by:

2. The preference analysis unit Analyzing the user's facial expressions and voice while eating, the system estimates their emotions in real time and understands their preferences.

2. The system of claim 1.

3. The preference analysis unit Based on the user's eating history, the system analyzes the user's eating preferences by time of day and season, and suggests optimal meal timings.

2. The system of claim 1.

4. The map creation unit Analyzing emotions at the restaurant visited by the user and identifying areas that elicit positive emotions.

2. The system of claim 1.

5. The restaurant recommendation unit Analyzing information on producers of the local specialty products and recommending restaurants that collaborate with producers 2. The system of claim 1.

6. The restaurant recommendation unit Analyzing the emotions felt by the user toward the local products and identifying the local products that elicit the positive emotions.

2. The system of claim 1.

7. The restaurant recommendation unit Recommending new food genres based on the user's eating history 2. The system of claim 1.

8. The restaurant recommendation unit Analyzing the emotions felt by the user regarding a particular eating experience and proposing a new eating experience based on the emotions 2. The system of claim 1.

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