System

Abstract

The system according to the embodiment aims to propose an optimal combination of food and drink to a user.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives an input of a user. The analysis unit analyzes the information received by the reception unit and proposes a combination of a dish and a drink. The providing unit provides the combination proposed by the analyzing 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 do not adequately suggest optimal food and drink pairings when ordering at restaurants, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest optimal combinations of food and drink to a user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit and suggests food and drink combinations. The provision unit provides the combinations suggested by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal combinations of food and drink to 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) An AI system according to an embodiment of the present invention recommends appropriate food and drink combinations when a user is unsure what to order at a restaurant. When a user enters a restaurant and browses the menu, the AI ​​system inputs the desired food and drink options. This information is then input into an AI system, which analyzes the information and proposes optimal food and drink combinations. For example, the AI ​​makes specific suggestions, such as red wine pairings with steak or sake pairings with sushi. Furthermore, the AI ​​also takes into account the user's preferences and allergies. For example, if a user likes spicy food, the AI ​​recommends drinks that go well with spicy food. Similarly, if a user has allergies, the AI ​​recommends food and drinks that address the allergy. This system allows users to enjoy optimal food and drink combinations without having to worry about ordering. For example, if a user orders pasta, the AI ​​recommends a wine that pairs well with the pasta. Similarly, if a user orders dessert, the AI ​​recommends coffee or tea that pairs well with the dessert. In this way, using AI streamlines ordering at restaurants and improves user satisfaction. Furthermore, restaurants can efficiently serve food and drinks based on the AI's suggestions. For example, serving food and drinks together based on combinations suggested by AI can improve the quality of service. This allows the AI ​​system to make ordering smoother for users and increase satisfaction. For example, users can enjoy the optimal food and drink combination without having to worry about what to order. Restaurants can also serve food and drinks efficiently based on AI suggestions. For example, serving food and drinks together based on combinations suggested by AI can improve the quality of service.

[0029] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit provides an interface for the user to input food and drink candidates. The reception unit can also collect user preferences and allergy information. For example, the reception unit can collect user preferences based on questionnaires and past selection history, and allergy information based on medical data and self-reporting. The analysis unit analyzes the information received by the reception unit and proposes optimal food and drink combinations. The analysis can be performed using, for example, a machine learning algorithm, but is not limited to, an example. For example, the analysis unit can calculate optimal combinations based on past order history and user feedback. The analysis unit can also propose optimal combinations based on the characteristics of the food and the drink. For example, the analysis unit can analyze the taste, nutritional value, and cooking method of the food, and the taste, alcohol content, and ingredients of the drink. The provision unit provides the combinations proposed by the analysis unit. The provision can be performed, for example, by displaying the proposal results to the user, but is not limited to, an example. For example, the serving unit displays the suggestion results on the user's smartphone or tablet. The serving unit can also serve food and drinks together. For example, the serving unit adjusts the timing and format for serving food and drinks simultaneously. This allows the AI ​​system according to the embodiment to suggest and serve optimal food and drink combinations based on user input. For example, users can enjoy optimal food and drink combinations without having to worry about what to order. Restaurants can also efficiently serve food and drinks based on AI suggestions. For example, serving food and drinks together based on combinations suggested by AI improves the quality of service.

[0030] The reception unit may include a specific method for collecting user preference and allergy information. The reception unit may collect user preferences using, for example, a questionnaire. For example, the reception unit may provide a questionnaire in the form of the user selecting their favorite food and drink. The reception unit may also collect user preferences based on past selection history. For example, the reception unit may analyze the history of food and drink orders the user has made in the past to identify the user's preferences. The reception unit may also collect allergy information based on medical data or self-reporting. For example, the reception unit may provide an input in the form of the user self-reporting ingredients to which they are allergic. This enables suggestions that take the user's preferences and allergy information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's questionnaire results into a generation AI and have the generation AI analyze the preference and allergy information.

[0031] The analysis unit can learn from past data and calculate combinations. The analysis unit, for example, learns from past order histories and calculates optimal combinations. For example, the analysis unit extracts past order histories from a database and analyzes them using a machine learning algorithm. The analysis unit can also calculate optimal combinations based on user feedback. For example, the analysis unit collects user feedback and reflects it in the analysis algorithm. The analysis unit can also suggest optimal combinations based on the characteristics of dishes and drinks. For example, the analysis unit analyzes the taste, nutritional value, and cooking method of dishes, and the taste, alcohol content, and ingredients of drinks. This improves the accuracy of suggesting optimal combinations based on past data. 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 past order histories into a generation AI and have the generation AI calculate the optimal combination.

[0032] The providing unit can provide the suggestion results to the user. For example, the providing unit displays the suggestion results on the user's smartphone or tablet. For example, the providing unit provides the suggestion results in the form of a notification to the user's device. The providing unit can also display the suggestion results on a display at the restaurant. For example, the providing unit displays the suggestion results on a display at the restaurant so that the user can check them. The providing unit can also serve food and drinks together. For example, the providing unit adjusts the timing and format for serving the food and drink simultaneously. This allows the suggestion results to be quickly provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the suggestion results to a generation AI and cause the generation AI to execute an optimal method for providing the results to the user.

[0033] The analysis unit can suggest combinations based on the characteristics of the food and the characteristics of the drink. The analysis unit, for example, analyzes the taste, nutritional value, and cooking method of the food. For example, the analysis unit analyzes the taste characteristics of the food and suggests the optimal drink. The analysis unit can also analyze the taste, alcohol content, and ingredients of the drink. For example, the analysis unit analyzes the taste characteristics of the drink and suggests the optimal food. The analysis unit can also comprehensively analyze the characteristics of the food and the drink and suggest the optimal combination. For example, the analysis unit analyzes the compatibility of the tastes of the food and the drink and suggests the optimal combination. This makes it possible to make suggestions that take the characteristics of the food and the drink into consideration. 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 characteristic data of the food and the drink into the generation AI and have the generation AI suggest the optimal combination.

[0034] The serving unit may be equipped with a specific method for simultaneously serving food and drinks. The serving unit, for example, adjusts the timing for simultaneously serving food and drinks. For example, the serving unit serves drinks according to the timing when the food is ready. The serving unit may also adjust the format for serving food and drinks together. For example, the serving unit serves food and drinks as a set menu. The serving unit may also build a system for simultaneously serving food and drinks. For example, the serving unit builds a system that automatically adjusts the timing for serving food and drinks. As a result, serving food and drinks together improves the quality of service. Some or all of the above-described processing in the serving unit may be performed using, for example, AI, or may be performed without using AI. For example, the serving unit may input the timing for serving food and drinks into a generating AI and cause the generating AI to execute the optimal serving method.

[0035] The reception unit can analyze the user's past order history and select an input method. For example, the reception unit extracts the user's past order history from a database and analyzes it using an analysis algorithm. For example, the reception unit automatically displays dishes and drinks that the user has frequently ordered in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests dishes and drinks that the user will order in a specific time period based on the user's past order history. This makes it possible to provide the optimal input method based on the user's past order history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past order history data into a generation AI and have the generation AI select the optimal input method.

[0036] The reception unit can perform filtering based on the user's current physical condition and mood at the time of input. The reception unit collects the user's physical condition and mood, for example, based on the user's self-reporting or sensor data. For example, if the user is feeling unwell, the reception unit can prioritize displaying foods and drinks that are easy to digest. The reception unit can also suggest refreshing drinks if the user is tired. For example, if the user wants to relax, the reception unit can suggest foods and drinks that have a relaxing effect. This makes it possible to make suggestions based on the user's physical condition and mood. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's physical condition and mood data into the generation AI and have the generation AI perform filtering.

[0037] The reception unit can select an input means depending on the user's input method at the time of input. For example, when a user inputs an order by voice, the reception unit supports the input using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software. Furthermore, when a user inputs an order by text, the reception unit can also provide an interface that supports text input. For example, when a user inputs an order by image, the reception unit supports the input using image recognition technology. For example, the reception unit analyzes the user's image using image recognition software to identify the order content. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0038] The reception unit can prioritize displaying highly relevant menu items based on the user's geographical location information when inputting the user's geographical location information. The reception unit, for example, collects the user's geographical location information using GPS data or location information services. For example, if the user is in a specific area, the reception unit can prioritize displaying menu items including local specialties. Furthermore, if the user is in a tourist spot, the reception unit can also suggest local specialty dishes. For example, if the user is at a specific event venue, the reception unit can prioritize displaying menu items related to the event. This makes it possible to provide highly relevant menu items based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant menu items.

[0039] The reception unit can analyze the user's social media activity at the time of input and suggest related menus. The reception unit, for example, collects the user's social media activity and analyzes it using an analysis algorithm. For example, the reception unit can suggest menus related to places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related dishes and drinks. For example, the reception unit can suggest related menus based on the user's social media activities. This makes it possible to provide related menus based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related menus.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The reception unit, for example, collects the user's past feedback and analyzes it using an analysis algorithm. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and customizes the input interface. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data to a generation AI and have the generation AI customize the input method.

[0041] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationship between food and drink during analysis. The analysis unit performs the analysis, for example, by taking into account the compatibility of the flavors of the food and drink. For example, the analysis unit analyzes the taste characteristics of the food and the drink and suggests an optimal combination. The analysis unit can also perform the analysis by taking into account the nutritional balance of the food and drink. For example, the analysis unit analyzes the nutritional components of the food and drink and suggests a balanced combination. The analysis unit can also perform the analysis by taking into account the timing of serving the food and drink. For example, the analysis unit adjusts the timing of serving the food and drink and suggests an optimal combination. This makes it possible to perform an analysis that takes into account the interrelationship between food and drink. 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 data on the interrelationship between food and drink into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0042] During analysis, the analysis unit can calculate combinations by referring to the user's past order history. The analysis unit, for example, extracts the user's past order history from a database and analyzes it using an analysis algorithm. For example, the analysis unit suggests optimal combinations based on the food and drink combinations the user has ordered in the past. The analysis unit can also prioritize suggesting specific food and drink combinations based on the user's past order history. For example, the analysis unit analyzes the user's past order history and suggests the combination that provides the highest satisfaction. This makes it possible to provide optimal combinations based on the user's past order history. 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 may input the user's past order history data into a generation AI and have the generation AI calculate the optimal combination.

[0043] During analysis, the analysis unit can propose combinations taking into account the seasonality of the food and drink. The analysis unit, for example, proposes combinations of food and drink using seasonal ingredients. For example, the analysis unit analyzes the characteristics of seasonal ingredients and proposes optimal combinations. The analysis unit can also propose combinations of food and drink that match seasonal events. For example, the analysis unit proposes food and drink related to seasonal events. The analysis unit can also propose combinations of food and drink that match the climate of the season. For example, the analysis unit proposes food and drink that match the climate of the season. This makes it possible to make proposals that take into account the seasonality of the food and drink. 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 seasonal ingredient data into the generation AI and cause the generation AI to propose optimal combinations.

[0044] During analysis, the analysis unit can propose combinations taking into account the user's geographical location information. The analysis unit, for example, collects the user's geographical location information using GPS data or location information services. For example, if the user is in a specific area, the analysis unit can propose a combination of food and drink that includes local specialties. Furthermore, if the user is in a tourist destination, the analysis unit can also propose a combination of a local specialty dish and drink. For example, if the user is at a specific event venue, the analysis unit can propose a combination of food and drink related to the event. This makes it possible to provide optimal combinations based on the user's geographical location information. 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 the user's geographical location information to a generation AI and cause the generation AI to propose optimal combinations.

[0045] During the analysis, the analysis unit can analyze the user's social media activity and suggest related combinations. The analysis unit, for example, collects the user's social media activity and analyzes it using an analysis algorithm. For example, the analysis unit can suggest food and drink combinations related to places the user has checked in to on social media. The analysis unit can also analyze the content of the user's social media posts to suggest related food and drink combinations. For example, the analysis unit can refer to the activities of the user's friends on social media to suggest related food and drink combinations. This makes it possible to provide related combinations based on the user's social media activity. 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 the user's social media data into a generation AI and cause the generation AI to suggest related combinations.

[0046] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, collects the user's past feedback and analyzes it using an analysis algorithm. For example, the analysis unit proposes an optimal analysis algorithm based on feedback provided by the user in the past. The analysis unit can also preferentially propose a specific analysis algorithm based on the user's past feedback. For example, the analysis unit analyzes the user's past feedback and customizes the analysis algorithm. This makes it possible to provide an optimal analysis algorithm based on the user's past feedback. 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 the user's feedback data into a generation AI and have the generation AI customize the analysis algorithm.

[0047] When providing food, the provision unit can select a provision method by referring to the user's past order history. The provision unit, for example, extracts the user's past order history from a database and analyzes it using an analytical algorithm. For example, the provision unit proposes an optimal provision method based on the combination of food and drink ordered by the user in the past. The provision unit can also preferentially propose a provision method for a specific food or drink based on the user's past order history. For example, the provision unit analyzes the user's past order history and proposes the provision method that provides the highest satisfaction. This makes it possible to provide the optimal provision method based on the user's past order history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's past order history data into a generation AI and have the generation AI select the optimal provision method.

[0048] The providing unit can customize the content to be provided based on the user's current physical condition and mood at the time of provision. The providing unit collects the user's physical condition and mood, for example, based on the user's self-reporting or sensor data. For example, if the user is feeling unwell, the providing unit can prioritize providing easy-to-digest foods and drinks. Furthermore, if the user is tired, the providing unit can also provide refreshing drinks. For example, if the user wants to relax, the providing unit provides foods and drinks with a relaxing effect. This makes it possible to provide content according to the user's physical condition and mood. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's physical condition and mood data into the generation AI and cause the generation AI to customize the content to be provided.

[0049] The providing unit can improve the delivery method by reflecting user feedback when providing the service. The providing unit, for example, collects user feedback and analyzes it using an analysis algorithm. For example, the providing unit suggests an optimal delivery method based on feedback previously provided by the user. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and customizes the delivery method. This makes it possible to provide an optimal delivery method based on the user's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the delivery method.

[0050] The providing unit can select a delivery method taking into account the user's geographical location information when providing the information. The providing unit, for example, collects the user's geographical location information using GPS data or a location information service. For example, if the user is in a specific area, the providing unit can prioritize providing dishes and drinks containing local specialties. Furthermore, if the user is in a tourist destination, the providing unit can also provide local specialty dishes. For example, if the user is at a specific event venue, the providing unit can prioritize providing dishes and drinks related to the event. This makes it possible to provide the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal delivery method.

[0051] The providing unit can analyze the user's social media activity and suggest related offerings at the time of provision. The providing unit, for example, collects the user's social media activity and analyzes it using an analysis algorithm. For example, the providing unit can provide food and drinks related to places the user has checked in to on social media. The providing unit can also analyze the user's social media posts and suggest related food and drinks. For example, the providing unit can refer to the activity of the user's friends on social media to suggest related food and drinks. This makes it possible to provide related offerings based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest related offerings.

[0052] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The providing unit, for example, collects the user's past feedback and analyzes it using an analysis algorithm. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and customizes the delivery method. This makes it possible to provide an optimal delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generation AI and cause the generation AI to customize the delivery method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The analysis unit can take the user's social media activity into consideration when analyzing the user's past order history. For example, it can analyze the locations where the user has checked in and the content of posts on social media, and suggest optimal food and drink combinations based on that information. It can also suggest related food and drinks based on the social media activity of the user's friends. It can also prioritize suggestions of food and drinks that the user has given high ratings on social media. This makes it possible to make personalized suggestions based on the user's social media activity.

[0055] The analysis unit can take seasonal events and special occasions into consideration when suggesting food and drink pairings. For example, it can suggest food and drink pairings that are suited to special events such as Christmas or Valentine's Day. It can also suggest food and drink pairings that use seasonal ingredients. It can also suggest food and drink pairings that take into account the climate of each season. This makes it possible to make optimal suggestions according to the season and event.

[0056] When accepting user input, the accepting unit can customize the input interface by reflecting the user's past feedback. For example, the accepting unit can suggest an optimal input method based on feedback provided by the user in the past. It can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, it can analyze the user's past feedback and customize the input interface. This makes it possible to provide an optimal input experience based on the user's past feedback.

[0057] When simultaneously serving food and drinks, the serving unit can select a serving method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, food and drinks containing local specialties can be served preferentially. Also, if the user is in a tourist spot, local specialty dishes can be served. Furthermore, if the user is at a specific event venue, food and drinks related to the event can be served preferentially. This makes it possible to provide the optimal serving method based on the user's geographical location information.

[0058] When suggesting food and drink pairings, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. For example, the analysis unit can suggest the optimal analysis algorithm based on the user's past feedback. It can also preferentially suggest a specific analysis algorithm based on the user's past feedback. Furthermore, it can analyze the user's past feedback and customize the analysis algorithm. This makes it possible to provide the optimal analysis algorithm based on the user's past feedback.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit accepts user input. User input includes text input, voice input, image input, etc. The reception unit provides an interface for the user to input candidates for food and drink they would like to eat. It can also collect information on user preferences and allergies. For example, it collects user preferences based on questionnaires and past selection history, and allergy information based on medical data and self-reporting. Step 2: The analysis unit analyzes the information received by the reception unit and suggests optimal food and drink combinations. The analysis is performed using a machine learning algorithm. For example, it calculates the optimal combination based on past order history and user feedback. It can also suggest optimal combinations based on the characteristics of the food and drink. For example, it analyzes the taste, nutritional value, and cooking method of the food, and the taste, alcohol content, and ingredients of the drink. Step 3: The serving unit serves the combination suggested by the analysis unit. The serving unit serves the combination by displaying the suggested results to the user. For example, the serving unit displays the suggested results on the user's smartphone or tablet. The serving unit can also serve food and drinks together. For example, the serving unit adjusts the timing and format for serving the food and drink at the same time.

[0061] (Example 2) An AI system according to an embodiment of the present invention recommends appropriate food and drink combinations when a user is unsure what to order at a restaurant. When a user enters a restaurant and browses the menu, the AI ​​system inputs the desired food and drink options. This information is then input into an AI system, which analyzes the information and proposes optimal food and drink combinations. For example, the AI ​​makes specific suggestions, such as red wine pairings with steak or sake pairings with sushi. Furthermore, the AI ​​also takes into account the user's preferences and allergies. For example, if a user likes spicy food, the AI ​​recommends drinks that go well with spicy food. Similarly, if a user has allergies, the AI ​​recommends food and drinks that address the allergy. This system allows users to enjoy optimal food and drink combinations without having to worry about ordering. For example, if a user orders pasta, the AI ​​recommends a wine that pairs well with the pasta. Similarly, if a user orders dessert, the AI ​​recommends coffee or tea that pairs well with the dessert. In this way, using AI streamlines ordering at restaurants and improves user satisfaction. Furthermore, restaurants can efficiently serve food and drinks based on the AI's suggestions. For example, serving food and drinks together based on combinations suggested by AI can improve the quality of service. This allows the AI ​​system to make ordering smoother for users and increase satisfaction. For example, users can enjoy the optimal food and drink combination without having to worry about what to order. Restaurants can also serve food and drinks efficiently based on AI suggestions. For example, serving food and drinks together based on combinations suggested by AI can improve the quality of service.

[0062] The AI ​​system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit provides an interface for the user to input food and drink candidates. The reception unit can also collect user preferences and allergy information. For example, the reception unit can collect user preferences based on questionnaires and past selection history, and allergy information based on medical data and self-reporting. The analysis unit analyzes the information received by the reception unit and proposes optimal food and drink combinations. The analysis can be performed using, for example, a machine learning algorithm, but is not limited to, an example. For example, the analysis unit can calculate optimal combinations based on past order history and user feedback. The analysis unit can also propose optimal combinations based on the characteristics of the food and the drink. For example, the analysis unit can analyze the taste, nutritional value, and cooking method of the food, and the taste, alcohol content, and ingredients of the drink. The provision unit provides the combinations proposed by the analysis unit. The provision can be performed, for example, by displaying the proposal results to the user, but is not limited to, an example. For example, the serving unit displays the suggestion results on the user's smartphone or tablet. The serving unit can also serve food and drinks together. For example, the serving unit adjusts the timing and format for serving food and drinks simultaneously. This allows the AI ​​system according to the embodiment to suggest and serve optimal food and drink combinations based on user input. For example, users can enjoy optimal food and drink combinations without having to worry about what to order. Restaurants can also efficiently serve food and drinks based on AI suggestions. For example, serving food and drinks together based on combinations suggested by AI improves the quality of service.

[0063] The reception unit may include a specific method for collecting user preference and allergy information. The reception unit may collect user preferences using, for example, a questionnaire. For example, the reception unit may provide a questionnaire in the form of the user selecting their favorite food and drink. The reception unit may also collect user preferences based on past selection history. For example, the reception unit may analyze the history of food and drink orders the user has made in the past to identify the user's preferences. The reception unit may also collect allergy information based on medical data or self-reporting. For example, the reception unit may provide an input in the form of the user self-reporting ingredients to which they are allergic. This enables suggestions that take the user's preferences and allergy information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit may input the user's questionnaire results into a generation AI and have the generation AI analyze the preference and allergy information.

[0064] The analysis unit can learn from past data and calculate combinations. The analysis unit, for example, learns from past order histories and calculates optimal combinations. For example, the analysis unit extracts past order histories from a database and analyzes them using a machine learning algorithm. The analysis unit can also calculate optimal combinations based on user feedback. For example, the analysis unit collects user feedback and reflects it in the analysis algorithm. The analysis unit can also suggest optimal combinations based on the characteristics of dishes and drinks. For example, the analysis unit analyzes the taste, nutritional value, and cooking method of dishes, and the taste, alcohol content, and ingredients of drinks. This improves the accuracy of suggesting optimal combinations based on past data. 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 past order histories into a generation AI and have the generation AI calculate the optimal combination.

[0065] The providing unit can provide the suggestion results to the user. For example, the providing unit displays the suggestion results on the user's smartphone or tablet. For example, the providing unit provides the suggestion results in the form of a notification to the user's device. The providing unit can also display the suggestion results on a display at the restaurant. For example, the providing unit displays the suggestion results on a display at the restaurant so that the user can check them. The providing unit can also serve food and drinks together. For example, the providing unit adjusts the timing and format for serving the food and drink simultaneously. This allows the suggestion results to be quickly provided to the user. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the suggestion results to a generation AI and cause the generation AI to execute an optimal method for providing the results to the user.

[0066] The analysis unit can suggest combinations based on the characteristics of the food and the characteristics of the drink. The analysis unit, for example, analyzes the taste, nutritional value, and cooking method of the food. For example, the analysis unit analyzes the taste characteristics of the food and suggests the optimal drink. The analysis unit can also analyze the taste, alcohol content, and ingredients of the drink. For example, the analysis unit analyzes the taste characteristics of the drink and suggests the optimal food. The analysis unit can also comprehensively analyze the characteristics of the food and the drink and suggest the optimal combination. For example, the analysis unit analyzes the compatibility of the tastes of the food and the drink and suggests the optimal combination. This makes it possible to make suggestions that take the characteristics of the food and the drink into consideration. 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 characteristic data of the food and the drink into the generation AI and have the generation AI suggest the optimal combination.

[0067] The serving unit may be equipped with a specific method for simultaneously serving food and drinks. The serving unit, for example, adjusts the timing for simultaneously serving food and drinks. For example, the serving unit serves drinks according to the timing when the food is ready. The serving unit may also adjust the format for serving food and drinks together. For example, the serving unit serves food and drinks as a set menu. The serving unit may also build a system for simultaneously serving food and drinks. For example, the serving unit builds a system that automatically adjusts the timing for serving food and drinks. As a result, serving food and drinks together improves the quality of service. Some or all of the above-described processing in the serving unit may be performed using, for example, AI, or may be performed without using AI. For example, the serving unit may input the timing for serving food and drinks into a generating AI and cause the generating AI to execute the optimal serving method.

[0068] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This improves user satisfaction by providing an interface that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions. Furthermore, the reception unit can adjust the display method of the input interface based on the estimated user's emotions. For example, if the user is nervous, the reception unit can provide an interface with subdued colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make input work more enjoyable. If the user is tired, the reception unit can provide a simple, highly visible interface to make input work easier. This improves user satisfaction by providing an interface that suits the user's emotions.

[0069] The reception unit can analyze the user's past order history and select an input method. For example, the reception unit extracts the user's past order history from a database and analyzes it using an analysis algorithm. For example, the reception unit automatically displays dishes and drinks that the user has frequently ordered in the past as candidates. The reception unit can also prioritize and suggest input methods (voice, text, etc.) that the user has used in the past. For example, the reception unit predicts and suggests dishes and drinks that the user will order in a specific time period based on the user's past order history. This makes it possible to provide the optimal input method based on the user's past order history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past order history data into a generation AI and have the generation AI select the optimal input method.

[0070] The reception unit can perform filtering based on the user's current physical condition and mood at the time of input. The reception unit collects the user's physical condition and mood, for example, based on the user's self-reporting or sensor data. For example, if the user is feeling unwell, the reception unit can prioritize displaying foods and drinks that are easy to digest. The reception unit can also suggest refreshing drinks if the user is tired. For example, if the user wants to relax, the reception unit can suggest foods and drinks that have a relaxing effect. This makes it possible to make suggestions based on the user's physical condition and mood. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's physical condition and mood data into the generation AI and have the generation AI perform filtering.

[0071] The reception unit can select an input means depending on the user's input method at the time of input. For example, when a user inputs an order by voice, the reception unit supports the input using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software. Furthermore, when a user inputs an order by text, the reception unit can also provide an interface that supports text input. For example, when a user inputs an order by image, the reception unit supports the input using image recognition technology. For example, the reception unit analyzes the user's image using image recognition software to identify the order content. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input data to a generation AI and have the generation AI select the optimal input means.

[0072] The reception unit can estimate the user's emotion and prioritize input content based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide a priority order for input content according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions. Furthermore, the reception unit determines the priority of the input content based on the estimated user's emotions. For example, if the user is in a hurry, it can prioritize and display dishes and drinks that can be easily ordered. If the user is relaxed, it can suggest dishes and drinks that can be enjoyed over time. If the user is unsure, it can prioritize and display popular dishes and drinks. This makes it possible to provide a priority of the input content according to the user's emotions.

[0073] The reception unit can prioritize displaying highly relevant menu items based on the user's geographical location information when inputting the user's geographical location information. The reception unit, for example, collects the user's geographical location information using GPS data or location information services. For example, if the user is in a specific area, the reception unit can prioritize displaying menu items including local specialties. Furthermore, if the user is in a tourist spot, the reception unit can also suggest local specialty dishes. For example, if the user is at a specific event venue, the reception unit can prioritize displaying menu items related to the event. This makes it possible to provide highly relevant menu items based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to a generation AI and cause the generation AI to select highly relevant menu items.

[0074] The reception unit can analyze the user's social media activity at the time of input and suggest related menus. The reception unit, for example, collects the user's social media activity and analyzes it using an analysis algorithm. For example, the reception unit can suggest menus related to places the user has checked in to on social media. The reception unit can also analyze the content of the user's social media posts and suggest related dishes and drinks. For example, the reception unit can suggest related menus based on the user's social media activities. This makes it possible to provide related menus based on the user's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related menus.

[0075] The reception unit can customize the input method by reflecting the user's past feedback when inputting data. The reception unit, for example, collects the user's past feedback and analyzes it using an analysis algorithm. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and customizes the input interface. This makes it possible to provide an optimal input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data to a generation AI and have the generation AI customize the input method.

[0076] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an analysis algorithm according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions. Furthermore, the analysis unit adjusts the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, it will prioritize analyzing dishes and drinks that have a relaxing effect. If the user is in a hurry, it will prioritize analyzing dishes and drinks that can be served quickly. If the user is enjoying themselves, it will prioritize analyzing dishes and drinks that will increase their enjoyment. This makes it possible to provide an analysis algorithm that corresponds to the user's emotions.

[0077] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationship between food and drink during analysis. The analysis unit performs the analysis, for example, by taking into account the compatibility of the flavors of the food and drink. For example, the analysis unit analyzes the taste characteristics of the food and the drink and suggests an optimal combination. The analysis unit can also perform the analysis by taking into account the nutritional balance of the food and drink. For example, the analysis unit analyzes the nutritional components of the food and drink and suggests a balanced combination. The analysis unit can also perform the analysis by taking into account the timing of serving the food and drink. For example, the analysis unit adjusts the timing of serving the food and drink and suggests an optimal combination. This makes it possible to perform an analysis that takes into account the interrelationship between food and drink. 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 data on the interrelationship between food and drink into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0078] During analysis, the analysis unit can calculate combinations by referring to the user's past order history. The analysis unit, for example, extracts the user's past order history from a database and analyzes it using an analysis algorithm. For example, the analysis unit suggests optimal combinations based on the food and drink combinations the user has ordered in the past. The analysis unit can also prioritize suggesting specific food and drink combinations based on the user's past order history. For example, the analysis unit analyzes the user's past order history and suggests the combination that provides the highest satisfaction. This makes it possible to provide optimal combinations based on the user's past order history. 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 may input the user's past order history data into a generation AI and have the generation AI calculate the optimal combination.

[0079] During analysis, the analysis unit can propose combinations taking into account the seasonality of the food and drink. The analysis unit, for example, proposes combinations of food and drink using seasonal ingredients. For example, the analysis unit analyzes the characteristics of seasonal ingredients and proposes optimal combinations. The analysis unit can also propose combinations of food and drink that match seasonal events. For example, the analysis unit proposes food and drink related to seasonal events. The analysis unit can also propose combinations of food and drink that match the climate of the season. For example, the analysis unit proposes food and drink that match the climate of the season. This makes it possible to make proposals that take into account the seasonality of the food and drink. 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 seasonal ingredient data into the generation AI and cause the generation AI to propose optimal combinations.

[0080] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice and calculates an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide a display method of the analysis results according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions. Furthermore, the analysis unit can adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, it can provide a simple, highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can provide a display method that focuses on the main points. This makes it possible to provide a display method of the analysis results that corresponds to the user's emotions.

[0081] During analysis, the analysis unit can propose combinations taking into account the user's geographical location information. The analysis unit, for example, collects the user's geographical location information using GPS data or location information services. For example, if the user is in a specific area, the analysis unit can propose a combination of food and drink that includes local specialties. Furthermore, if the user is in a tourist destination, the analysis unit can also propose a combination of a local specialty dish and drink. For example, if the user is at a specific event venue, the analysis unit can propose a combination of food and drink related to the event. This makes it possible to provide optimal combinations based on the user's geographical location information. 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 the user's geographical location information to a generation AI and cause the generation AI to propose optimal combinations.

[0082] During the analysis, the analysis unit can analyze the user's social media activity and suggest related combinations. The analysis unit, for example, collects the user's social media activity and analyzes it using an analysis algorithm. For example, the analysis unit can suggest food and drink combinations related to places the user has checked in to on social media. The analysis unit can also analyze the content of the user's social media posts to suggest related food and drink combinations. For example, the analysis unit can refer to the activities of the user's friends on social media to suggest related food and drink combinations. This makes it possible to provide related combinations based on the user's social media activity. 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 the user's social media data into a generation AI and cause the generation AI to suggest related combinations.

[0083] During analysis, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. The analysis unit, for example, collects the user's past feedback and analyzes it using an analysis algorithm. For example, the analysis unit proposes an optimal analysis algorithm based on feedback provided by the user in the past. The analysis unit can also preferentially propose a specific analysis algorithm based on the user's past feedback. For example, the analysis unit analyzes the user's past feedback and customizes the analysis algorithm. This makes it possible to provide an optimal analysis algorithm based on the user's past feedback. 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 the user's feedback data into a generation AI and have the generation AI customize the analysis algorithm.

[0084] The providing unit can estimate the user's emotion and adjust the delivery method based on the estimated user's emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide a delivery method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions. Furthermore, the providing unit can adjust the method of providing information based on the estimated user's emotions. For example, if the user is nervous, the information is provided in a calm voice. If the user is relaxed, the information is provided in a cheerful voice. If the user is in a hurry, the information is provided in a quick and concise voice. This makes it possible to provide a method of providing information that matches the user's emotions.

[0085] When providing food, the provision unit can select a provision method by referring to the user's past order history. The provision unit, for example, extracts the user's past order history from a database and analyzes it using an analytical algorithm. For example, the provision unit proposes an optimal provision method based on the combination of food and drink ordered by the user in the past. The provision unit can also preferentially propose a provision method for a specific food or drink based on the user's past order history. For example, the provision unit analyzes the user's past order history and proposes the provision method that provides the highest satisfaction. This makes it possible to provide the optimal provision method based on the user's past order history. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's past order history data into a generation AI and have the generation AI select the optimal provision method.

[0086] The providing unit can customize the content to be provided based on the user's current physical condition and mood at the time of provision. The providing unit collects the user's physical condition and mood, for example, based on the user's self-reporting or sensor data. For example, if the user is feeling unwell, the providing unit can prioritize providing easy-to-digest foods and drinks. Furthermore, if the user is tired, the providing unit can also provide refreshing drinks. For example, if the user wants to relax, the providing unit provides foods and drinks with a relaxing effect. This makes it possible to provide content according to the user's physical condition and mood. Some or all of the above-described processing in the providing unit may be performed using, or without, AI, for example. For example, the providing unit can input the user's physical condition and mood data into the generation AI and cause the generation AI to customize the content to be provided.

[0087] The providing unit can improve the delivery method by reflecting user feedback when providing the service. The providing unit, for example, collects user feedback and analyzes it using an analysis algorithm. For example, the providing unit suggests an optimal delivery method based on feedback previously provided by the user. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and customizes the delivery method. This makes it possible to provide an optimal delivery method based on the user's feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the delivery method.

[0088] The providing unit can estimate the user's emotions and prioritize the content to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide a priority order of content to be provided based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or without AI. For example, the provision unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions. Furthermore, the provision unit determines the priority of the content to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the provision unit can prioritize food and drinks that can be provided quickly. If the user is relaxed, the provision unit can prioritize food and drinks that can be enjoyed over time. If the user is unsure, the provision unit can prioritize popular food and drinks. This makes it possible to prioritize the content to be provided according to the user's emotions.

[0089] The providing unit can select a delivery method taking into account the user's geographical location information when providing the information. The providing unit, for example, collects the user's geographical location information using GPS data or a location information service. For example, if the user is in a specific area, the providing unit can prioritize providing dishes and drinks containing local specialties. Furthermore, if the user is in a tourist destination, the providing unit can also provide local specialty dishes. For example, if the user is at a specific event venue, the providing unit can prioritize providing dishes and drinks related to the event. This makes it possible to provide the optimal delivery method based on the user's geographical location information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to a generation AI and cause the generation AI to select the optimal delivery method.

[0090] The providing unit can analyze the user's social media activity and suggest related offerings at the time of provision. The providing unit, for example, collects the user's social media activity and analyzes it using an analysis algorithm. For example, the providing unit can provide food and drinks related to places the user has checked in to on social media. The providing unit can also analyze the user's social media posts and suggest related food and drinks. For example, the providing unit can refer to the activity of the user's friends on social media to suggest related food and drinks. This makes it possible to provide related offerings based on the user's social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media data into a generation AI and cause the generation AI to suggest related offerings.

[0091] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the service. The providing unit, for example, collects the user's past feedback and analyzes it using an analysis algorithm. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. For example, the providing unit analyzes the user's past feedback and customizes the delivery method. This makes it possible to provide an optimal delivery method based on the user's past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's feedback data into a generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives user input using a touch panel 38A or a microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal combinations of food and drink using a machine learning algorithm. The provision unit displays the suggestion results to the user using a display 40A or a speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and suggests optimal combinations of food and drink using a machine learning algorithm. The provision unit provides the suggestion results to the user by voice using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and suggests optimal combinations of food and drink using a machine learning algorithm. The provision unit displays the suggestion results to the user using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input from the user using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and suggests optimal combinations of food and drink using a machine learning algorithm. The provision unit provides the suggestion results to the user by voice using the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] When accepting user input, the accepting unit can customize the input interface based on the user's current mood and physical condition. For example, if the user is tired, a simple, highly visible interface can be provided. Alternatively, if the user is relaxed, a colorful, fun interface can be provided. Furthermore, if the user is in a hurry, a simple interface can be provided to enable quick input. This makes it possible to provide an optimal input experience according to the user's physical condition and mood.

[0094] The analysis unit can take the user's social media activity into consideration when analyzing the user's past order history. For example, it can analyze the locations where the user has checked in and the content of posts on social media, and suggest optimal food and drink combinations based on that information. It can also suggest related food and drinks based on the social media activity of the user's friends. It can also prioritize suggestions of food and drinks that the user has given high ratings on social media. This makes it possible to make personalized suggestions based on the user's social media activity.

[0095] When providing the user with the suggestion results, the suggestion unit can estimate the user's emotions and adjust the suggestion method based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide guidance in a calm voice. If the user is relaxed, the suggestion unit can provide guidance in a cheerful voice. Furthermore, if the user is in a hurry, the suggestion unit can provide quick and concise guidance. This makes it possible to provide the optimal suggestion method according to the user's emotions.

[0096] The analysis unit can take seasonal events and special occasions into consideration when suggesting food and drink pairings. For example, it can suggest food and drink pairings that are suited to special events such as Christmas or Valentine's Day. It can also suggest food and drink pairings that use seasonal ingredients. It can also suggest food and drink pairings that take into account the climate of each season. This makes it possible to make optimal suggestions according to the season and event.

[0097] When accepting user input, the accepting unit can customize the input interface by reflecting the user's past feedback. For example, the accepting unit can suggest an optimal input method based on feedback provided by the user in the past. It can also preferentially suggest a specific input method based on the user's past feedback. Furthermore, it can analyze the user's past feedback and customize the input interface. This makes it possible to provide an optimal input experience based on the user's past feedback.

[0098] When suggesting food and drink combinations, the analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, it can prioritize analyzing foods and drinks that have a relaxing effect. Also, if the user is in a hurry, it can prioritize analyzing foods and drinks that can be served quickly. Furthermore, if the user is enjoying themselves, it can prioritize analyzing foods and drinks that enhance enjoyment. This makes it possible to provide an optimal analysis algorithm according to the user's emotions.

[0099] When simultaneously serving food and drinks, the serving unit can select a serving method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, food and drinks containing local specialties can be served preferentially. Also, if the user is in a tourist spot, local specialty dishes can be served. Furthermore, if the user is at a specific event venue, food and drinks related to the event can be served preferentially. This makes it possible to provide the optimal serving method based on the user's geographical location information.

[0100] When accepting a user's input, the accepting unit can estimate the user's emotions and prioritize the input content based on the estimated emotions. For example, if the user is in a hurry, it can prioritize displaying dishes and drinks that can be easily ordered. Also, if the user is relaxed, it can suggest dishes and drinks that can be enjoyed over time. Furthermore, if the user is unsure, it can prioritize displaying popular dishes and drinks. This makes it possible to provide optimal priorities for the input content according to the user's emotions.

[0101] When suggesting food and drink pairings, the analysis unit can customize the analysis algorithm by reflecting the user's past feedback. For example, the analysis unit can suggest the optimal analysis algorithm based on the user's past feedback. It can also preferentially suggest a specific analysis algorithm based on the user's past feedback. Furthermore, it can analyze the user's past feedback and customize the analysis algorithm. This makes it possible to provide the optimal analysis algorithm based on the user's past feedback.

[0102] When simultaneously serving food and drinks, the serving unit can estimate the user's emotions and determine the priority of the contents to be served based on the estimated emotions. For example, if the user is in a hurry, the serving unit can prioritize dishes and drinks that can be served quickly. Also, if the user is relaxed, the serving unit can prioritize dishes and drinks that can be enjoyed over time. Furthermore, if the user is unsure, the serving unit can prioritize popular dishes and drinks. This makes it possible to provide the optimal priority of the contents to be served according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit accepts user input. User input includes text input, voice input, image input, etc. The reception unit provides an interface for the user to input candidates for food and drink they would like to eat. It can also collect information on user preferences and allergies. For example, it collects user preferences based on questionnaires and past selection history, and allergy information based on medical data and self-reporting. Step 2: The analysis unit analyzes the information received by the reception unit and suggests optimal food and drink combinations. The analysis is performed using a machine learning algorithm. For example, it calculates the optimal combination based on past order history and user feedback. It can also suggest optimal combinations based on the characteristics of the food and drink. For example, it analyzes the taste, nutritional value, and cooking method of the food, and the taste, alcohol content, and ingredients of the drink. Step 3: The serving unit serves the combination suggested by the analysis unit. The serving unit serves the combination by displaying the suggested results to the user. For example, the serving unit displays the suggested results on the user's smartphone or tablet. The serving unit can also serve food and drinks together. For example, the serving unit adjusts the timing and format for serving the food and drink at the same time.

[0105] 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.

[0106] 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 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[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 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.

[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 (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).

[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] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit and suggests food and drink pairings; a providing unit that provides the combinations proposed by the analysis unit. A system characterized by:

2. The reception unit Have a specific method for collecting user preferences and allergies 2. The system of claim 1.

3. The analysis unit Learn from past data and calculate combinations 2. The system of claim 1.

4. The providing unit Providing the proposed results to the user 2. The system of claim 1.

5. The analysis unit Suggesting pairings based on the characteristics of food and drinks 2. The system of claim 1.

6. The providing unit Have a specific method for serving food and drinks at the same time 2. The system of claim 1.

7. The reception unit The system has a specific method for estimating a user's emotions and adjusts the display method of the input interface based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past order history and select the input method 2. The system of claim 1.

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