system

Abstract

We provide the system. [Solution] A device equipped with sensors for collecting meal data provides a means for identifying the contents of a meal, A device equipped with a camera for analyzing human facial expressions provides a means for detecting emotional states, It includes an output device for presenting generated comments to the user, and means for generating and providing short comments in a timely manner. A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] At the dining table, it is required to quickly utter a polite word. However, many people cannot immediately come up with an appropriate word, which may spoil the atmosphere of the occasion. In such a situation, it is an issue to take appropriate communication to smooth out human relations and soften the atmosphere of the place.

Means for Solving the Problems

[0005] This invention provides a system that accurately identifies the contents of a meal using a device equipped with sensors for collecting meal data, and further detects emotional states from a person's facial expressions using a camera. The system then uses a generating AI to instantly produce a phrase appropriate to the situation and present it to the user via an output device. This system allows users to obtain timely and appropriate comments, facilitating smooth communication.

[0006] A "sensor" is a device that detects the user's movements, pressure, and vibrations in order to collect dietary data.

[0007] "Dietary content" refers to information about the ingredients and dishes that the user consumes.

[0008] A "camera" is an image acquisition device that captures the faces of people in its surroundings and records their expressions.

[0009] "Emotional state" refers to a psychological or emotional state determined based on facial expressions.

[0010] "Generative AI" is an artificial intelligence system that generates appropriate comments based on data on meal content and emotional state.

[0011] An "output device" is an audio or visual information presentation device used to convey the generated comments to the user.

[0012] A "one-line comment" is a short, appropriate statement tailored to a specific situation, intended to lighten the mood or liven things up. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3]It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0015] First, the language used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface that includes a communication processor, an antenna, and the like. 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), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the 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.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is implemented as a system that provides thoughtful remarks in real time to facilitate smooth communication during meals. The system incorporates a device equipped with sensors for collecting meal data and a camera for detecting emotional states.

[0035] First, when the user eats, the device uses sensors attached to the teeth to detect chewing movements and acquire information about the food consumed. This information is then digitized as data related to the ingredients and dishes used.

[0036] Next, the device uses its camera to capture the facial expressions of the people present in real time. Based on this video data, facial recognition technology is applied to analyze each person's emotional state.

[0037] The analyzed data is sent to a server, where a generative AI generates the most appropriate one-word comment based on the meal content and emotional state. This generative AI has the ability to select appropriate words based on the context by accumulating past data through machine learning.

[0038] The generated short comment is provided to the user via the device. This output is transmitted as audio using the user's usual ear device, allowing the user to use it naturally in conversation.

[0039] For example, if a user is enjoying a pasta dish and those around them appear relaxed, the generated comment might be something like, "This pasta has an exquisite sauce!" In this way, the present invention always provides the user with a conversation starter appropriate to the situation, creating a pleasant atmosphere during the meal.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The device uses sensors to detect the user's chewing movements and collects meal data. The data is processed in real time and recorded as characteristic information to identify ingredients and dishes.

[0043] Step 2:

[0044] The device uses its camera to capture images of the faces of people around it. The captured video data is input into a facial expression analysis module, and the emotional state is determined based on the information obtained from it.

[0045] Step 3:

[0046] The device transmits collected meal and emotional data to the server. Communication takes place securely and quickly via the internet.

[0047] Step 4:

[0048] The server matches meal data against a database to identify ingredients and dish names. The ingredient database contains information on various dishes, and the meal content is classified in detail based on the matching results.

[0049] Step 5:

[0050] The server analyzes emotional data and maps the resulting facial expression information to emotional categories. Using facial recognition technology, it instantly classifies emotional states such as "smiling," "surprised," and "serious."

[0051] Step 6:

[0052] The server uses a generative AI to generate a short comment based on the identified meal content and emotional state. The generative AI utilizes a contextual model to select the most appropriate expression.

[0053] Step 7:

[0054] The server sends the generated comment to the terminal. This comment is then converted into a format that the user can receive via their understanding device.

[0055] Step 8:

[0056] The device transmits a generated short comment as audio through the user's ear device. The user can hear this and naturally incorporate it into the conversation.

[0057] This entire process allows users to receive a brief comment at the appropriate time and with the right content during a meal, thus facilitating smooth communication.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In modern society, smooth communication during meals is crucial for enhancing individual social satisfaction and well-being. However, many people struggle to find appropriate conversation starters. This challenge is particularly pronounced when people from different generations and cultural backgrounds share a meal. Therefore, there is a need for a system that naturally facilitates communication during meals and provides appropriate comments for the situation.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for sensing and identifying the contents of a meal using a device equipped with sensors for collecting information about the meal, means for extracting emotional information using a device equipped with a camera for analyzing a person's emotional state, and system means for generating and presenting a timely comment using an artificial intelligence model and the emotional information and meal information. This facilitates smooth communication during meals and makes it possible to naturally provide conversation appropriate to the atmosphere of the situation.

[0063] A "sensor" is a device that detects physical movements or states in a specific environment and converts them into electrical signals.

[0064] A "device" is a combination of hardware and software designed to achieve a specific function or purpose.

[0065] A "recording device" is a device equipped with technology for recording video or images.

[0066] "Emotional information" refers to data about a person's psychological state obtained by analyzing their facial expressions and behavior.

[0067] An "artificial intelligence model" is an algorithm and its implementation designed to learn from data and perform a task.

[0068] A "timely remark" is the act of making a short comment or statement that is immediately relevant to the situation.

[0069] An "output device" is a device that converts digital information into a format that humans can recognize and present it.

[0070] This invention is a system designed to facilitate smooth communication among users during meals. This system is realized through the collaborative efforts of several devices and algorithms.

[0071] The device first uses sensors attached to the teeth to collect information about the meal. These sensors physically sense the user's chewing movements and detect the type of food and dishes. Specific chewing patterns are digitized as data to identify different meal contents.

[0072] The terminal also uses a camera to capture the facial expressions of each person present in real time. The video data is analyzed using facial recognition technology, and individual emotional states are extracted. This provides people's psychological states as numerical data.

[0073] This information is sent to the server. The server integrates the meal information and emotional information and sends a prompt to the generative AI model. This prompt functions as an instruction to generate a contextually appropriate one-word comment. For example, it might be in the format of, "Generate a one-word comment about having pasta as a meal and being relaxed."

[0074] The generative AI model utilizes accumulated data and learned knowledge to generate the most appropriate one-word response, which is then received by the server. The generated one-word comment is transmitted as audio to the ear device via the terminal and presented to the user.

[0075] For example, if a user is enjoying a pasta dish and the people around them are in a relaxed mood, the generated comment might be, "This pasta has an exquisite sauce!" In this way, the present invention provides users with conversation starters appropriate to the situation and promotes natural communication.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The device recognizes that the user has started eating. Sensors attached to the teeth detect the chewing pattern and acquire the signal as digital data. The input is the physical signal from the sensor, and the output is digitized food content data. This data indicates the type of ingredients and dishes.

[0079] Step 2:

[0080] The device uses a camera to capture the facial expressions of people sitting together during a meal in real time. The input is video data obtained through the camera, and the output is analyzed emotional information. Using facial recognition technology, each person's emotions are extracted as numerical data from the video data.

[0081] Step 3:

[0082] The server receives meal content data and emotional information sent from the terminal and integrates them. The input is meal content data and emotional information, and the output is a prompt statement for analyzing them together. This prompt statement serves as a command to run the generative AI model.

[0083] Step 4:

[0084] The server sends prompt messages to the AI ​​model based on the integrated data. These prompt messages specify a concrete situation. For example, "Generate a one-word comment describing a relaxed atmosphere while eating pasta." The input is the prompt message, and the output is the generated one-word comment.

[0085] Step 5:

[0086] A short comment generated from the server is sent to the terminal and transmitted to the user as audio through an ear device. The input is the generated short comment, and the output is audio information for the user to use in conversation. This allows the user to participate in the conversation in a natural way.

[0087] (Application Example 1)

[0088] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] Conversation during meals can sometimes be difficult, and natural communication between staff and customers is especially important in brick-and-mortar restaurants. However, it is difficult for staff to always provide the right comment, which can lead to decreased customer satisfaction. A system is needed to solve this problem and provide a better customer experience.

[0090] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0091] In this invention, the server includes means for identifying meal information using a device equipped with an instrument for collecting meal data, means for detecting emotional states using a device equipped with a camera for analyzing a person's facial expressions, means for generating and providing short comments in a timely manner using an output device for providing generated comments to the user, and means for providing conversation support for the user using a glasses-type device in a physical store. This makes it possible to facilitate natural and smooth conversation during meals.

[0092] A "device for collecting meal data" is a device that senses chewing and eating actions during a meal and obtains information about the content of the meal.

[0093] "Photography equipment for analyzing human facial expressions" refers to a device that uses a camera or sensor to photograph a person's face and analyze their facial expressions.

[0094] An "output device" is a device that transmits generated information or comments to the user, and has the function of providing information in the form of audio or visuals.

[0095] A "glasses-type device" is a confidential device that can provide information within the wearer's field of vision, and generally utilizes technologies such as AR (augmented reality).

[0096] A "physical store" is a commercial facility that provides goods and services in a physical location, and includes restaurants, cafes, and other similar establishments.

[0097] This invention provides a system to support natural and smooth communication among users during meals. This system is achieved by collecting meal data, analyzing emotional states, and generating appropriate short comments.

[0098] The server first processes information sent from a terminal equipped with sensors that detect chewing and eating actions in order to collect meal data. This terminal identifies information containing the contents of the meal as digital data and sends it to the server. Next, a camera is used to capture video data in order to analyze the facial expressions of the people present. Based on this video data, the server analyzes the emotional state using an expression recognition algorithm and classifies it into an emotional category.

[0099] Furthermore, the server uses a generative AI model to generate the most appropriate one-word comment based on meal data and emotional data. This AI model has the ability to store past data through machine learning and select contextually appropriate words. The generated comment is either visually displayed via glasses-type output devices worn by the user, or transmitted audibly via ear devices. For example, if a user is eating a steak and their emotional state is relaxed, the system will generate a comment such as, "The steak looks perfectly cooked!"

[0100] An example of a prompt is, "Steak, generate a short comment suitable for a customer in a relaxed state." Based on this prompt, the AI ​​model constructs the optimal comment.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The device uses sensors to detect chewing and eating actions when the user is eating. The detected action data is collected as digital signals to identify the contents of the meal. In this process, the input is information about the physical chewing action, and the output is sent to the server as "meal content data."

[0104] Step 2:

[0105] The device uses a camera to capture the facial expressions of the people present in real time. Based on the video data acquired by the camera, an expression recognition algorithm is applied to analyze their emotional state. The input here is camera video data, and the output is interpreted as "emotional state data" and sent to the server.

[0106] Step 3:

[0107] The server integrates the received meal content data and emotional state data, and uses a generative AI model to create a prompt. A concrete example of this prompt might be, "Steak, generate a short comment suitable for a customer in a relaxed state." Based on this prompt, the AI ​​model utilizes past data and contextual understanding to generate the optimal comment. The output is "short comment data" to be presented to the user.

[0108] Step 4:

[0109] The server sends back the generated one-word comment data to the terminal. The terminal either displays this data visually via a glasses-type output device or conveys it to the user as audio via an ear device. The user can then naturally incorporate this comment into their conversation. The input is one-word comment data, and the output is "the user's natural integration into conversation."

[0110] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0111] This invention relates to a system that aims to recognize the emotions of the user and those around them during a meal and to offer an appropriate comment based on that recognition. This system is implemented by combining the collection of meal data, a device for analyzing emotions, a device for providing the generated comment, and an emotion engine.

[0112] First, the device uses sensors to detect the user's chewing movements during a meal and identifies the contents of the meal. The sensors are used to acquire information about the texture and taste of the ingredients and dishes, and the acquired data is digitized.

[0113] Next, the device uses its camera to capture the facial expressions of people around it and analyzes that facial expression data. Using facial recognition technology, it determines emotions from subtle facial movements. Furthermore, the emotion engine analyzes the emotional state of the user and those present in detail. The emotion engine analyzes the tone of voice and language patterns that match the facial expression data to derive an overall emotional state.

[0114] This allows the server to integrate the meal content and each person's emotional state to generate the most appropriate one-word comment from the AI. This AI utilizes an advanced model that combines diverse meal scenes and emotional patterns, enabling it to produce a contextually accurate comment.

[0115] For example, in a scene where a user is enjoying a steak, if the emotion engine determines that the user's emotional state at that moment is "relaxed and smiling," the generating AI will produce a phrase like, "This steak is cooked perfectly, and everyone looks even brighter!" This phrase is transmitted audibly from the device to the user through the earpiece, allowing the user to naturally incorporate it into their conversation.

[0116] This allows users to communicate more smoothly during meals and avoid awkward situations.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The device uses sensors to acquire data while the user is chewing their food. The sensors detect chewing patterns and pressure, and use this information to acquire data that identifies the contents of the food.

[0120] Step 2:

[0121] The device uses its camera to capture images of the faces of people around the table. The captured images are analyzed in real time and recorded as facial expression data.

[0122] Step 3:

[0123] The device sends facial expression data to the emotion engine for analysis. The emotion engine analyzes this data and determines the emotional state of each person.

[0124] Step 4:

[0125] The emotion engine analyzes voice tone and language patterns based on the user's and those around them' facial expression data, and comprehensively evaluates their emotional state. This enables more precise emotional assessment.

[0126] Step 5:

[0127] The device transmits meal data acquired by sensors and emotional states analyzed by an emotion engine to a server. Data transmission is fast and secure.

[0128] Step 6:

[0129] The server refers to a database of ingredients and dishes to identify ingredients and dishes from the submitted meal data. This allows for detailed classification of the meal content.

[0130] Step 7:

[0131] The server uses a generative AI to generate the most appropriate one-word comment based on the analyzed meal content and emotional state. The AI ​​derives words that are appropriate to the context and people's current emotions.

[0132] Step 8:

[0133] The server generates a short comment and transmits it to the terminal. The terminal then converts the received comment into a format that is easy for the user to understand.

[0134] Step 9:

[0135] The device transmits a short comment as audio through the user's ear device. The user can hear this audio and naturally incorporate it into the conversation.

[0136] This series of steps allows users to receive support in facilitating smooth communication during meals.

[0137] (Example 2)

[0138] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0139] In dining settings, there is a need to support smooth communication by accurately understanding the emotional states of users and those around them and providing appropriate comments based on that understanding. However, conventional technologies often fail to adequately analyze emotions or generate appropriate comments, which can actually make communication more awkward.

[0140] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0141] In this invention, the server includes means for identifying the contents of a meal using a device equipped with sensors for collecting meal information, means for detecting an emotional state using a device equipped with a camera for analyzing a person's facial expressions, and means for using an emotion engine that analyzes the tone and language patterns of a voice to derive an overall emotional state. This makes it possible to generate a short comment based on the emotions and meal contents analyzed using a generative AI model and provide it to the user as natural-sounding audio.

[0142] "Meal information" refers to information about the user's meals, including details about ingredients, the texture of the dishes, and their taste.

[0143] A "sensor" is a device that collects food information, and has functions to detect the user's chewing movements and measure the characteristics of the food.

[0144] A "device" refers to hardware designed to perform a specific function, such as sensors or cameras used to collect and process data.

[0145] A "camera" is a device that acquires image or video data in order to analyze a person's facial expressions.

[0146] "Emotional state" refers to the psychological and emotional state of the user and those around them, and is derived from various facial expressions and tones of voice.

[0147] An "emotion engine" refers to a program or algorithm that analyzes voice tone and language patterns based on collected biometric data to determine an overall emotional state.

[0148] A "generative AI model" is an artificial intelligence-based algorithm that generates short comments based on meal content and emotional state, and produces appropriate output based on the training data.

[0149] A "one-line comment" is a short sentence or phrase generated in response to the emotional state of the user or those around them, and is used to facilitate communication in a dining setting.

[0150] An "output device" is a device that presents the generated short comment to the user and has the function of providing it as audio via an ear device or similar.

[0151] This invention is a system that aims to recognize the emotions of the user during a meal and the emotions of those around them, and to offer an appropriate comment based on that. The system consists of a terminal worn by the user and a server that serves as the system's central hub.

[0152] The device is equipped with sensors that detect the user's chewing movements during meals, thereby collecting meal information. The sensors measure the characteristics of ingredients and dishes from chewing sounds and movements, convert them into digital data, and transmit it. In addition, a camera is used to capture the facial expressions of the user and those around them. This facial expression data is sent to a server and analyzed by a facial recognition algorithm. At this time, voice tone and language patterns are also collected and used in an emotion engine that determines the overall emotional state.

[0153] The server integrates collected meal information and emotional data, and uses an advanced generative AI model to generate the most appropriate one-word comment. The generative AI model operates based on a dataset of various meal scenes and emotional patterns, outputting the most contextually accurate comment.

[0154] As a concrete example, let's imagine a scene where a user is enjoying a steak with a close friend. In this case, if the emotion engine determines the emotional state to be "relaxed and smiling," the generating AI model can produce a comment such as, "This steak is cooked perfectly. Everyone looks even brighter!"

[0155] The generated comments are delivered to the user via audio through an ear device. This allows users to naturally incorporate comments into conversations, making communication during meals smoother.

[0156] An example of a prompt message is as follows: "The user is enjoying a steak dinner at home with close friends in a relaxed atmosphere. They are smiling and in a laid-back mood. Generate a short comment that fits this scene."

[0157] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0158] Step 1:

[0159] The device uses sensors to collect food information and detect the user's chewing movements. The input is the user's chewing sounds and movements, which the sensors detect and convert into digital data. The resulting data includes information such as the texture of the ingredients and dishes. This data is then sent to the server.

[0160] Step 2:

[0161] The device uses its camera to capture the facial expressions of the user and those around them. The input is the acquired image and video data, which is then analyzed to extract facial expression data. Specifically, the camera identifies expressions such as smiles and surprised faces, and quantifies their characteristics. This processed data is then sent to the server.

[0162] Step 3:

[0163] The server integrates the received meal information and facial expression data. The input is the data obtained from steps 1 and 2, and the emotion engine determines the overall emotional state by analyzing this data along with voice tone and language patterns. The output is data representing the emotions of the user and those around them. Specifically, it determines that a user is relaxed based on their relaxed tone of voice and the degree of their smile.

[0164] Step 4:

[0165] The server uses a generative AI model to generate a short comment based on the analyzed emotional state and meal information. The input is the overall emotional state data and meal information obtained in step 3. Based on this information, the generative AI model generates a contextual comment by referring to a large amount of case data. The output is a short comment optimized for the user.

[0166] Step 5:

[0167] The device provides the user with a generated short comment via voice. The input is the data of the short comment created in step 4. Specifically, the device plays the comment near the user's ear using an ear device. The output is the short comment delivered to the user as voice. This process allows the user to naturally incorporate the comment into their conversation.

[0168] (Application Example 2)

[0169] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0170] In today's restaurant environment, there is a need for new services that facilitate smoother communication during meals and improve the customer experience. However, providing appropriate comments and information in real time that are in line with the customer's emotions and the atmosphere at the table is difficult. Therefore, the introduction of sophisticated dialogue technologies to enhance customer satisfaction is desired.

[0171] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0172] In this invention, the server includes means for identifying ingested food using an information processing device equipped with a sensor for collecting meal data, means for recognizing emotional states using an information processing device equipped with a camera for analyzing a person's facial expressions, means for creating and providing short comments appropriate to the user's situation using an audio output device for presenting generated comments to the user, and means for communicating with a store's database to obtain information related to ingested food. This makes it possible to provide appropriate information in accordance with the customer's emotions and the atmosphere at the dining place.

[0173] A "detector" is a device used to collect data about food intake under specific circumstances and to identify the substances consumed.

[0174] An "information processing device" is a device that analyzes data obtained from sensors and imaging devices and generates necessary information.

[0175] A "photography device" is a device used to capture a person's facial expressions and movements, and is used to recognize their emotional state.

[0176] An "audio output device" is a device that provides audio output for presenting generated information or comments to the user.

[0177] A "database" is a collection of data used to store related information and to search for and retrieve that information as needed.

[0178] A "server" is a computer system that plays a central role in aggregating, processing, and distributing information over a network.

[0179] This invention aims to improve the customer experience in a dining environment within a specific restaurant setting. The system is implemented using a server, terminals (smartphones and tablets), and various sensors and acoustic output devices.

[0180] The server identifies ingested food using an information processing device equipped with sensors for collecting meal data. The technology used includes physical sensors for food identification and database communication for managing menu information. A camera captures a person's facial expressions in real time, and the information processing device analyzes this data to recognize their emotional state. This allows for understanding customer reactions and the atmosphere during the meal.

[0181] Using a generative AI model, a short comment is generated based on collected emotional states and dining information. The generated comment is delivered via the user's smartphone or through an audio output device in the restaurant. This allows customers to incorporate the comment into their actual conversation, promoting natural communication. For example, if a positive reaction is detected from the customer's facial expression when the chef's special dish is served, the server can provide a comment such as, "This special dish really showcases the chef's skill!"

[0182] An example of a prompt might be: "There is a group of people looking at a plate of [dish name]. They are all smiling. Please think of a short comment to brighten the mood."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The terminal uses sensors to collect meal data. This provides raw data on each user's chewing movements and food intake. The terminal analyzes this data, identifies the type and quantity of food in digital format, and transmits it to the server.

[0186] Step 2:

[0187] The terminal captures the customer's facial expressions via a camera. The captured video data is input, and the terminal analyzes this data using a facial recognition algorithm to classify the emotional state. This information is also output to the server.

[0188] Step 3:

[0189] The server integrates the received meal data and emotional data. This generates a dataset showing the relationship between meal content and emotional state. Based on this, the server prepares to generate appropriate comments.

[0190] Step 4:

[0191] The server uses a generative AI model to take integrated data as input and attempts to generate comments through prompts. The generative AI outputs a one-word comment based on the context of the meal and emotions. The prompt used is, "Consider what comment would be appropriate based on the customer's expression as they enjoy this dish."

[0192] Step 5:

[0193] The generated comments are sent from the server to the terminal. The terminal then provides the received comments to the customer using an audio output device. This ensures that a comment related to the food and emotions is shared within the customer's environment.

[0194] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0195] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0196] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0197] [Second Embodiment]

[0198] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0199] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0200] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0201] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0202] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0203] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0204] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0205] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0206] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0207] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0208] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0209] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0210] This invention is implemented as a system that provides thoughtful remarks in real time to facilitate smooth communication during meals. The system incorporates a device equipped with sensors for collecting meal data and a camera for detecting emotional states.

[0211] First, when the user eats, the device uses sensors attached to the teeth to detect chewing movements and acquire information about the food consumed. This information is then digitized as data related to the ingredients and dishes used.

[0212] Next, the device uses its camera to capture the facial expressions of the people present in real time. Based on this video data, facial recognition technology is applied to analyze each person's emotional state.

[0213] The analyzed data is sent to a server, where a generative AI generates the most appropriate one-word comment based on the meal content and emotional state. This generative AI has the ability to select appropriate words based on the context by accumulating past data through machine learning.

[0214] The generated short comment is provided to the user via the device. This output is transmitted as audio using the user's usual ear device, allowing the user to use it naturally in conversation.

[0215] For example, if a user is enjoying a pasta dish and those around them appear relaxed, the generated comment might be something like, "This pasta has an exquisite sauce!" In this way, the present invention always provides the user with a conversation starter appropriate to the situation, creating a pleasant atmosphere during the meal.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The device uses sensors to detect the user's chewing movements and collects meal data. The data is processed in real time and recorded as characteristic information to identify ingredients and dishes.

[0219] Step 2:

[0220] The device uses its camera to capture images of the faces of people around it. The captured video data is input into a facial expression analysis module, and the emotional state is determined based on the information obtained from it.

[0221] Step 3:

[0222] The device transmits collected meal and emotional data to the server. Communication takes place securely and quickly via the internet.

[0223] Step 4:

[0224] The server matches meal data against a database to identify ingredients and dish names. The ingredient database contains information on various dishes, and the meal content is classified in detail based on the matching results.

[0225] Step 5:

[0226] The server analyzes emotional data and maps the resulting facial expression information to emotional categories. Using facial recognition technology, it instantly classifies emotional states such as "smiling," "surprised," and "serious."

[0227] Step 6:

[0228] The server uses a generative AI to generate a short comment based on the identified meal content and emotional state. The generative AI utilizes a contextual model to select the most appropriate expression.

[0229] Step 7:

[0230] The server sends the generated comment to the terminal. This comment is then converted into a format that the user can receive via their understanding device.

[0231] Step 8:

[0232] The device transmits a generated short comment as audio through the user's ear device. The user can hear this and naturally incorporate it into the conversation.

[0233] This entire process allows users to receive a brief comment at the appropriate time and with the right content during a meal, thus facilitating smooth communication.

[0234] (Example 1)

[0235] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0236] In modern society, smooth communication during meals is crucial for enhancing individual social satisfaction and well-being. However, many people struggle to find appropriate conversation starters. This challenge is particularly pronounced when people from different generations and cultural backgrounds share a meal. Therefore, there is a need for a system that naturally facilitates communication during meals and provides appropriate comments for the situation.

[0237] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0238] In this invention, the server includes means for sensing and identifying the contents of a meal using a device equipped with sensors for collecting information about the meal, means for extracting emotional information using a device equipped with a camera for analyzing a person's emotional state, and system means for generating and presenting a timely comment using an artificial intelligence model and the emotional information and meal information. This facilitates smooth communication during meals and makes it possible to naturally provide conversation appropriate to the atmosphere of the situation.

[0239] A "sensor" is a device that detects physical movements or states in a specific environment and converts them into electrical signals.

[0240] A "device" is a combination of hardware and software designed to achieve a specific function or purpose.

[0241] A "recording device" is a device equipped with technology for recording video or images.

[0242] "Emotional information" refers to data about a person's psychological state obtained by analyzing their facial expressions and behavior.

[0243] An "artificial intelligence model" is an algorithm and its implementation designed to learn from data and perform a task.

[0244] A "timely remark" is the act of making a short comment or statement that is immediately relevant to the situation.

[0245] An "output device" is a device that converts digital information into a format that humans can recognize and present it.

[0246] This invention is a system designed to facilitate smooth communication among users during meals. This system is realized through the collaborative efforts of several devices and algorithms.

[0247] The device first uses sensors attached to the teeth to collect information about the meal. These sensors physically sense the user's chewing movements and detect the type of food and dishes. Specific chewing patterns are digitized as data to identify different meal contents.

[0248] The terminal also uses a camera to capture the facial expressions of each person present in real time. The video data is analyzed using facial recognition technology, and individual emotional states are extracted. This provides people's psychological states as numerical data.

[0249] This information is sent to the server. The server integrates the meal information and emotional information and sends a prompt to the generative AI model. This prompt functions as an instruction to generate a contextually appropriate one-word comment. For example, it might be in the format of, "Generate a one-word comment about having pasta as a meal and being relaxed."

[0250] The generative AI model utilizes accumulated data and learned knowledge to generate the most appropriate one-word response, which is then received by the server. The generated one-word comment is transmitted as audio to the ear device via the terminal and presented to the user.

[0251] For example, if a user is enjoying a pasta dish and the people around them are in a relaxed mood, the generated comment might be, "This pasta has an exquisite sauce!" In this way, the present invention provides users with conversation starters appropriate to the situation and promotes natural communication.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The device recognizes that the user has started eating. Sensors attached to the teeth detect the chewing pattern and acquire the signal as digital data. The input is the physical signal from the sensor, and the output is digitized food content data. This data indicates the type of ingredients and dishes.

[0255] Step 2:

[0256] The device uses a camera to capture the facial expressions of people sitting together during a meal in real time. The input is video data obtained through the camera, and the output is analyzed emotional information. Using facial recognition technology, each person's emotions are extracted as numerical data from the video data.

[0257] Step 3:

[0258] The server receives meal content data and emotional information sent from the terminal and integrates them. The input is meal content data and emotional information, and the output is a prompt statement for analyzing them together. This prompt statement serves as a command to run the generative AI model.

[0259] Step 4:

[0260] The server sends prompt messages to the AI ​​model based on the integrated data. These prompt messages specify a concrete situation. For example, "Generate a one-word comment describing a relaxed atmosphere while eating pasta." The input is the prompt message, and the output is the generated one-word comment.

[0261] Step 5:

[0262] A short comment generated from the server is sent to the terminal and transmitted to the user as audio through an ear device. The input is the generated short comment, and the output is audio information for the user to use in conversation. This allows the user to participate in the conversation in a natural way.

[0263] (Application Example 1)

[0264] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] Conversation during meals can sometimes be difficult, and natural communication between staff and customers is especially important in brick-and-mortar restaurants. However, it is difficult for staff to always provide the right comment, which can lead to decreased customer satisfaction. A system is needed to solve this problem and provide a better customer experience.

[0266] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0267] In this invention, the server includes means for identifying meal information using a device equipped with an instrument for collecting meal data, means for detecting emotional states using a device equipped with a camera for analyzing a person's facial expressions, means for generating and providing short comments in a timely manner using an output device for providing generated comments to the user, and means for providing conversation support for the user using a glasses-type device in a physical store. This makes it possible to facilitate natural and smooth conversation during meals.

[0268] A "device for collecting meal data" is a device that senses chewing and eating actions during a meal and obtains information about the content of the meal.

[0269] "Photography equipment for analyzing human facial expressions" refers to a device that uses a camera or sensor to photograph a person's face and analyze their facial expressions.

[0270] An "output device" is a device that transmits generated information or comments to the user, and has the function of providing information in the form of audio or visuals.

[0271] A "glasses-type device" is a confidential device that can provide information within the wearer's field of vision, and generally utilizes technologies such as AR (augmented reality).

[0272] A "physical store" is a commercial facility that provides goods and services in a physical location, and includes restaurants, cafes, and other similar establishments.

[0273] This invention provides a system to support natural and smooth communication among users during meals. This system is achieved by collecting meal data, analyzing emotional states, and generating appropriate short comments.

[0274] The server first processes information sent from a terminal equipped with sensors that detect chewing and eating actions in order to collect meal data. This terminal identifies information containing the contents of the meal as digital data and sends it to the server. Next, a camera is used to capture video data in order to analyze the facial expressions of the people present. Based on this video data, the server analyzes the emotional state using an expression recognition algorithm and classifies it into an emotional category.

[0275] Furthermore, the server uses a generative AI model to generate the most appropriate one-word comment based on meal data and emotional data. This AI model has the ability to store past data through machine learning and select contextually appropriate words. The generated comment is either visually displayed via glasses-type output devices worn by the user, or transmitted audibly via ear devices. For example, if a user is eating a steak and their emotional state is relaxed, the system will generate a comment such as, "The steak looks perfectly cooked!"

[0276] An example of a prompt is, "Steak, generate a short comment suitable for a customer in a relaxed state." Based on this prompt, the AI ​​model constructs the optimal comment.

[0277] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0278] Step 1:

[0279] The device uses sensors to detect chewing and eating actions when the user is eating. The detected action data is collected as digital signals to identify the contents of the meal. In this process, the input is information about the physical chewing action, and the output is sent to the server as "meal content data."

[0280] Step 2:

[0281] The terminal uses a camera to capture the expressions of the people sitting together in real time. Based on the video data obtained by the camera, an emotion recognition algorithm is applied to analyze the emotional state. The input here is the camera video data, and the output is interpreted as "emotional state data" and sent to the server.

[0282] Step 3:

[0283] The server integrates the received meal content data and emotional state data, and uses a generated AI model to create a prompt sentence. This prompt sentence is, for example, something like "Steak, please generate a one-line comment suitable for a customer in a relaxed state." Based on this prompt, the AI model utilizes past data and context understanding to generate an optimal comment. The output is "one-line comment data" for presenting to the user.

[0284] Step 4:

[0285] The server sends the generated one-line comment data back to the terminal. The terminal visually displays this data via a glasses-type output device or conveys it to the user as audio using an ear device. The user can naturally incorporate this comment into the conversation. The input is the one-line comment data, and the output is "incorporation into the user's natural conversation."

[0286] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion specific model 59 and perform specific processing using the user's emotion.

[0287] The present invention is a system aimed at recognizing the emotions of the user and the people around during a meal and uttering an appropriate one-line comment based on that. This system is implemented by combining the collection of meal data, a device for analyzing emotions, a device for providing the generated one-line comment, and an emotion engine.

[0288] First, the device uses sensors to detect the user's chewing movements during a meal and identifies the contents of the meal. The sensors are used to acquire information about the texture and taste of the ingredients and dishes, and the acquired data is digitized.

[0289] Next, the device uses its camera to capture the facial expressions of people around it and analyzes that facial expression data. Using facial recognition technology, it determines emotions from subtle facial movements. Furthermore, the emotion engine analyzes the emotional state of the user and those present in detail. The emotion engine analyzes the tone of voice and language patterns that match the facial expression data to derive an overall emotional state.

[0290] This allows the server to integrate the meal content and each person's emotional state to generate the most appropriate one-word comment from the AI. This AI utilizes an advanced model that combines diverse meal scenes and emotional patterns, enabling it to produce a contextually accurate comment.

[0291] For example, in a scene where a user is enjoying a steak, if the emotion engine determines that the user's emotional state at that moment is "relaxed and smiling," the generating AI will produce a phrase like, "This steak is cooked perfectly, and everyone looks even brighter!" This phrase is transmitted audibly from the device to the user through the earpiece, allowing the user to naturally incorporate it into their conversation.

[0292] This allows users to communicate more smoothly during meals and avoid awkward situations.

[0293] The following describes the processing flow.

[0294] Step 1:

[0295] The device uses sensors to acquire data while the user is chewing their food. The sensors detect chewing patterns and pressure, and use this information to acquire data that identifies the contents of the food.

[0296] Step 2:

[0297] The terminal uses the camera to take pictures of the faces of people around the table. The captured images are analyzed in real time and recorded as expression data.

[0298] Step 3:

[0299] The terminal sends the expression data to the emotion engine for analysis. The emotion engine analyzes the data and determines the emotional state of each person.

[0300] Step 4:

[0301] Based on the expression data of the user and the people around, the emotion engine also analyzes the voice tone and language pattern, and comprehensively evaluates the emotional state. This enables a more refined emotional evaluation.

[0302] Step 5:

[0303] The terminal sends the meal data obtained by the sensor and the emotional state analyzed by the emotion engine to the server. The data transmission is carried out quickly and securely.

[0304] Step 6:

[0305] The server refers to the database of ingredients and dishes, and identifies the ingredients and dishes from the transmitted meal data. As a result, the meal content is classified in detail.

[0306] Step 7:

[0307] Based on the analyzed meal content and emotional state, the server uses the generative AI to generate an optimal one-line comment. The AI derives words according to the context and people's current emotions.

[0308] Step 8:

[0309] The server generates a short comment and transmits it to the terminal. The terminal then converts the received comment into a format that is easy for the user to understand.

[0310] Step 9:

[0311] The device transmits a short comment as audio through the user's ear device. The user can hear this audio and naturally incorporate it into the conversation.

[0312] This series of steps allows users to receive support in facilitating smooth communication during meals.

[0313] (Example 2)

[0314] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0315] In dining settings, there is a need to support smooth communication by accurately understanding the emotional states of users and those around them and providing appropriate comments based on that understanding. However, conventional technologies often fail to adequately analyze emotions or generate appropriate comments, which can actually make communication more awkward.

[0316] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0317] In this invention, the server includes means for identifying the contents of a meal using a device equipped with sensors for collecting meal information, means for detecting an emotional state using a device equipped with a camera for analyzing a person's facial expressions, and means for using an emotion engine that analyzes the tone and language patterns of a voice to derive an overall emotional state. This makes it possible to generate a short comment based on the emotions and meal contents analyzed using a generative AI model and provide it to the user as natural-sounding audio.

[0318] "Meal information" refers to information about the user's meals, including details about ingredients, the texture of the dishes, and their taste.

[0319] A "sensor" is a device that collects food information, and has functions to detect the user's chewing movements and measure the characteristics of the food.

[0320] A "device" refers to hardware designed to perform a specific function, such as sensors or cameras used to collect and process data.

[0321] A "camera" is a device that acquires image or video data in order to analyze a person's facial expressions.

[0322] "Emotional state" refers to the psychological and emotional state of the user and those around them, and is derived from various facial expressions and tones of voice.

[0323] An "emotion engine" refers to a program or algorithm that analyzes voice tone and language patterns based on collected biometric data to determine an overall emotional state.

[0324] A "generative AI model" is an artificial intelligence-based algorithm that generates short comments based on meal content and emotional state, and produces appropriate output based on the training data.

[0325] A "one-line comment" is a short sentence or phrase generated in response to the emotional state of the user or those around them, and is used to facilitate communication in a dining setting.

[0326] An "output device" is a device that presents the generated short comment to the user and has the function of providing it as audio via an ear device or similar.

[0327] This invention is a system that aims to recognize the emotions of the user during a meal and the emotions of those around them, and to offer an appropriate comment based on that. The system consists of a terminal worn by the user and a server that serves as the system's central hub.

[0328] The device is equipped with sensors that detect the user's chewing movements during meals, thereby collecting meal information. The sensors measure the characteristics of ingredients and dishes from chewing sounds and movements, convert them into digital data, and transmit it. In addition, a camera is used to capture the facial expressions of the user and those around them. This facial expression data is sent to a server and analyzed by a facial recognition algorithm. At this time, voice tone and language patterns are also collected and used in an emotion engine that determines the overall emotional state.

[0329] The server integrates collected meal information and emotional data, and uses an advanced generative AI model to generate the most appropriate one-word comment. The generative AI model operates based on a dataset of various meal scenes and emotional patterns, outputting the most contextually accurate comment.

[0330] As a concrete example, let's imagine a scene where a user is enjoying a steak with a close friend. In this case, if the emotion engine determines the emotional state to be "relaxed and smiling," the generating AI model can produce a comment such as, "This steak is cooked perfectly. Everyone looks even brighter!"

[0331] The generated comments are delivered to the user via audio through an ear device. This allows users to naturally incorporate comments into conversations, making communication during meals smoother.

[0332] An example of a prompt message is as follows: "The user is enjoying a steak dinner at home with close friends in a relaxed atmosphere. They are smiling and in a laid-back mood. Generate a short comment that fits this scene."

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The device uses sensors to collect food information and detect the user's chewing movements. The input is the user's chewing sounds and movements, which the sensors detect and convert into digital data. The resulting data includes information such as the texture of the ingredients and dishes. This data is then sent to the server.

[0336] Step 2:

[0337] The device uses its camera to capture the facial expressions of the user and those around them. The input is the acquired image and video data, which is then analyzed to extract facial expression data. Specifically, the camera identifies expressions such as smiles and surprised faces, and quantifies their characteristics. This processed data is then sent to the server.

[0338] Step 3:

[0339] The server integrates the received meal information and facial expression data. The input is the data obtained from steps 1 and 2, and the emotion engine determines the overall emotional state by analyzing this data along with voice tone and language patterns. The output is data representing the emotions of the user and those around them. Specifically, it determines that a user is relaxed based on their relaxed tone of voice and the degree of their smile.

[0340] Step 4:

[0341] The server uses a generative AI model to generate a short comment based on the analyzed emotional state and meal information. The input is the overall emotional state data and meal information obtained in step 3. Based on this information, the generative AI model generates a contextual comment by referring to a large amount of case data. The output is a short comment optimized for the user.

[0342] Step 5:

[0343] The device provides the user with a generated short comment via voice. The input is the data of the short comment created in step 4. Specifically, the device plays the comment near the user's ear using an ear device. The output is the short comment delivered to the user as voice. This process allows the user to naturally incorporate the comment into their conversation.

[0344] (Application Example 2)

[0345] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0346] In today's restaurant environment, there is a need for new services that facilitate smoother communication during meals and improve the customer experience. However, providing appropriate comments and information in real time that are in line with the customer's emotions and the atmosphere at the table is difficult. Therefore, the introduction of sophisticated dialogue technologies to enhance customer satisfaction is desired.

[0347] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0348] In this invention, the server includes means for identifying ingested food using an information processing device equipped with a sensor for collecting meal data, means for recognizing emotional states using an information processing device equipped with a camera for analyzing a person's facial expressions, means for creating and providing short comments appropriate to the user's situation using an audio output device for presenting generated comments to the user, and means for communicating with a store's database to obtain information related to ingested food. This makes it possible to provide appropriate information in accordance with the customer's emotions and the atmosphere at the dining place.

[0349] A "detector" is a device used to collect data about food intake under specific circumstances and to identify the substances consumed.

[0350] An "information processing device" is a device that analyzes data obtained from sensors and imaging devices and generates necessary information.

[0351] A "photography device" is a device used to capture a person's facial expressions and movements, and is used to recognize their emotional state.

[0352] An "audio output device" is a device that provides audio output for presenting generated information or comments to the user.

[0353] A "database" is a collection of data used to store related information and to search for and retrieve that information as needed.

[0354] A "server" is a computer system that plays a central role in aggregating, processing, and distributing information over a network.

[0355] This invention aims to improve the customer experience in a dining environment within a specific restaurant setting. The system is implemented using a server, terminals (smartphones and tablets), and various sensors and acoustic output devices.

[0356] The server identifies ingested food using an information processing device equipped with sensors for collecting meal data. The technology used includes physical sensors for food identification and database communication for managing menu information. A camera captures a person's facial expressions in real time, and the information processing device analyzes this data to recognize their emotional state. This allows for understanding customer reactions and the atmosphere during the meal.

[0357] Using a generative AI model, a short comment is generated based on collected emotional states and dining information. The generated comment is delivered via the user's smartphone or through an audio output device in the restaurant. This allows customers to incorporate the comment into their actual conversation, promoting natural communication. For example, if a positive reaction is detected from the customer's facial expression when the chef's special dish is served, the server can provide a comment such as, "This special dish really showcases the chef's skill!"

[0358] An example of a prompt might be: "There is a group of people looking at a plate of [dish name]. They are all smiling. Please think of a short comment to brighten the mood."

[0359] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0360] Step 1:

[0361] The terminal uses sensors to collect meal data. This provides raw data on each user's chewing movements and food intake. The terminal analyzes this data, identifies the type and quantity of food in digital format, and transmits it to the server.

[0362] Step 2:

[0363] The terminal captures the customer's facial expressions via a camera. The captured video data is input, and the terminal analyzes this data using a facial recognition algorithm to classify the emotional state. This information is also output to the server.

[0364] Step 3:

[0365] The server integrates the received meal data and emotional data. This generates a dataset showing the relationship between meal content and emotional state. Based on this, the server prepares to generate appropriate comments.

[0366] Step 4:

[0367] The server uses a generative AI model to take integrated data as input and attempts to generate comments through prompts. The generative AI outputs a one-word comment based on the context of the meal and emotions. The prompt used is, "Consider what comment would be appropriate based on the customer's expression as they enjoy this dish."

[0368] Step 5:

[0369] The generated comments are sent from the server to the terminal. The terminal then provides the received comments to the customer using an audio output device. This ensures that a comment related to the food and emotions is shared within the customer's environment.

[0370] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0371] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0372] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0373] [Third Embodiment]

[0374] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0375] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0376] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0377] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0378] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0379] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0380] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0381] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0382] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0383] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0384] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0385] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0386] This invention is implemented as a system that provides thoughtful remarks in real time to facilitate smooth communication during meals. The system incorporates a device equipped with sensors for collecting meal data and a camera for detecting emotional states.

[0387] First, when the user eats, the device uses sensors attached to the teeth to detect chewing movements and acquire information about the food consumed. This information is then digitized as data related to the ingredients and dishes used.

[0388] Next, the device uses its camera to capture the facial expressions of the people present in real time. Based on this video data, facial recognition technology is applied to analyze each person's emotional state.

[0389] The analyzed data is sent to a server, where a generative AI generates the most appropriate one-word comment based on the meal content and emotional state. This generative AI has the ability to select appropriate words based on the context by accumulating past data through machine learning.

[0390] The generated short comment is provided to the user via the device. This output is transmitted as audio using the user's usual ear device, allowing the user to use it naturally in conversation.

[0391] For example, if a user is enjoying a pasta dish and those around them appear relaxed, the generated comment might be something like, "This pasta has an exquisite sauce!" In this way, the present invention always provides the user with a conversation starter appropriate to the situation, creating a pleasant atmosphere during the meal.

[0392] The following describes the processing flow.

[0393] Step 1:

[0394] The device uses sensors to detect the user's chewing movements and collects meal data. The data is processed in real time and recorded as characteristic information to identify ingredients and dishes.

[0395] Step 2:

[0396] The device uses its camera to capture images of the faces of people around it. The captured video data is input into a facial expression analysis module, and the emotional state is determined based on the information obtained from it.

[0397] Step 3:

[0398] The device transmits collected meal and emotional data to the server. Communication takes place securely and quickly via the internet.

[0399] Step 4:

[0400] The server matches meal data against a database to identify ingredients and dish names. The ingredient database contains information on various dishes, and the meal content is classified in detail based on the matching results.

[0401] Step 5:

[0402] The server analyzes emotional data and maps the resulting facial expression information to emotional categories. Using facial recognition technology, it instantly classifies emotional states such as "smiling," "surprised," and "serious."

[0403] Step 6:

[0404] The server uses a generative AI to generate a short comment based on the identified meal content and emotional state. The generative AI utilizes a contextual model to select the most appropriate expression.

[0405] Step 7:

[0406] The server sends the generated comment to the terminal. This comment is then converted into a format that the user can receive via their understanding device.

[0407] Step 8:

[0408] The device transmits a generated short comment as audio through the user's ear device. The user can hear this and naturally incorporate it into the conversation.

[0409] This entire process allows users to receive a brief comment at the appropriate time and with the right content during a meal, thus facilitating smooth communication.

[0410] (Example 1)

[0411] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0412] In modern society, smooth communication during meals is crucial for enhancing individual social satisfaction and well-being. However, many people struggle to find appropriate conversation starters. This challenge is particularly pronounced when people from different generations and cultural backgrounds share a meal. Therefore, there is a need for a system that naturally facilitates communication during meals and provides appropriate comments for the situation.

[0413] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0414] In this invention, the server includes means for sensing and identifying the contents of a meal using a device equipped with sensors for collecting information about the meal, means for extracting emotional information using a device equipped with a camera for analyzing a person's emotional state, and system means for generating and presenting a timely comment using an artificial intelligence model and the emotional information and meal information. This facilitates smooth communication during meals and makes it possible to naturally provide conversation appropriate to the atmosphere of the situation.

[0415] A "sensor" is a device that detects physical movements or states in a specific environment and converts them into electrical signals.

[0416] A "device" is a combination of hardware and software designed to achieve a specific function or purpose.

[0417] A "recording device" is a device equipped with technology for recording video or images.

[0418] "Emotional information" refers to data about a person's psychological state obtained by analyzing their facial expressions and behavior.

[0419] An "artificial intelligence model" is an algorithm and its implementation designed to learn from data and perform a task.

[0420] A "timely remark" is the act of making a short comment or statement that is immediately relevant to the situation.

[0421] An "output device" is a device that converts digital information into a format that humans can recognize and present it.

[0422] This invention is a system designed to facilitate smooth communication among users during meals. This system is realized through the collaborative efforts of several devices and algorithms.

[0423] The device first uses sensors attached to the teeth to collect information about the meal. These sensors physically sense the user's chewing movements and detect the type of food and dishes. Specific chewing patterns are digitized as data to identify different meal contents.

[0424] The terminal also uses a camera to capture the facial expressions of each person present in real time. The video data is analyzed using facial recognition technology, and individual emotional states are extracted. This provides people's psychological states as numerical data.

[0425] This information is sent to the server. The server integrates the meal information and emotional information and sends a prompt to the generative AI model. This prompt functions as an instruction to generate a contextually appropriate one-word comment. For example, it might be in the format of, "Generate a one-word comment about having pasta as a meal and being relaxed."

[0426] The generative AI model utilizes accumulated data and learned knowledge to generate the most appropriate one-word response, which is then received by the server. The generated one-word comment is transmitted as audio to the ear device via the terminal and presented to the user.

[0427] For example, if a user is enjoying a pasta dish and the people around them are in a relaxed mood, the generated comment might be, "This pasta has an exquisite sauce!" In this way, the present invention provides users with conversation starters appropriate to the situation and promotes natural communication.

[0428] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0429] Step 1:

[0430] The device recognizes that the user has started eating. Sensors attached to the teeth detect the chewing pattern and acquire the signal as digital data. The input is the physical signal from the sensor, and the output is digitized food content data. This data indicates the type of ingredients and dishes.

[0431] Step 2:

[0432] The device uses a camera to capture the facial expressions of people sitting together during a meal in real time. The input is video data obtained through the camera, and the output is analyzed emotional information. Using facial recognition technology, each person's emotions are extracted as numerical data from the video data.

[0433] Step 3:

[0434] The server receives meal content data and emotional information sent from the terminal and integrates them. The input is meal content data and emotional information, and the output is a prompt statement for analyzing them together. This prompt statement serves as a command to run the generative AI model.

[0435] Step 4:

[0436] The server sends prompt messages to the AI ​​model based on the integrated data. These prompt messages specify a concrete situation. For example, "Generate a one-word comment describing a relaxed atmosphere while eating pasta." The input is the prompt message, and the output is the generated one-word comment.

[0437] Step 5:

[0438] A short comment generated from the server is sent to the terminal and transmitted to the user as audio through an ear device. The input is the generated short comment, and the output is audio information for the user to use in conversation. This allows the user to participate in the conversation in a natural way.

[0439] (Application Example 1)

[0440] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0441] Conversation during meals can sometimes be difficult, and natural communication between staff and customers is especially important in brick-and-mortar restaurants. However, it is difficult for staff to always provide the right comment, which can lead to decreased customer satisfaction. A system is needed to solve this problem and provide a better customer experience.

[0442] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0443] In this invention, the server includes means for identifying meal information using a device equipped with an instrument for collecting meal data, means for detecting emotional states using a device equipped with a camera for analyzing a person's facial expressions, means for generating and providing short comments in a timely manner using an output device for providing generated comments to the user, and means for providing conversation support for the user using a glasses-type device in a physical store. This makes it possible to facilitate natural and smooth conversation during meals.

[0444] A "device for collecting meal data" is a device that senses chewing and eating actions during a meal and obtains information about the content of the meal.

[0445] "Photography equipment for analyzing human facial expressions" refers to a device that uses a camera or sensor to photograph a person's face and analyze their facial expressions.

[0446] An "output device" is a device that transmits generated information or comments to the user, and has the function of providing information in the form of audio or visuals.

[0447] A "glasses-type device" is a confidential device that can provide information within the wearer's field of vision, and generally utilizes technologies such as AR (augmented reality).

[0448] A "physical store" is a commercial facility that provides goods and services in a physical location, and includes restaurants, cafes, and other similar establishments.

[0449] This invention provides a system to support natural and smooth communication among users during meals. This system is achieved by collecting meal data, analyzing emotional states, and generating appropriate short comments.

[0450] The server first processes information sent from a terminal equipped with sensors that detect chewing and eating actions in order to collect meal data. This terminal identifies information containing the contents of the meal as digital data and sends it to the server. Next, a camera is used to capture video data in order to analyze the facial expressions of the people present. Based on this video data, the server analyzes the emotional state using an expression recognition algorithm and classifies it into an emotional category.

[0451] Furthermore, the server uses a generative AI model to generate the most appropriate one-word comment based on meal data and emotional data. This AI model has the ability to store past data through machine learning and select contextually appropriate words. The generated comment is either visually displayed via glasses-type output devices worn by the user, or transmitted audibly via ear devices. For example, if a user is eating a steak and their emotional state is relaxed, the system will generate a comment such as, "The steak looks perfectly cooked!"

[0452] An example of a prompt is, "Steak, generate a short comment suitable for a customer in a relaxed state." Based on this prompt, the AI ​​model constructs the optimal comment.

[0453] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0454] Step 1:

[0455] The device uses sensors to detect chewing and eating actions when the user is eating. The detected action data is collected as digital signals to identify the contents of the meal. In this process, the input is information about the physical chewing action, and the output is sent to the server as "meal content data."

[0456] Step 2:

[0457] The device uses a camera to capture the facial expressions of the people present in real time. Based on the video data acquired by the camera, an expression recognition algorithm is applied to analyze their emotional state. The input here is camera video data, and the output is interpreted as "emotional state data" and sent to the server.

[0458] Step 3:

[0459] The server integrates the received meal content data and emotional state data, and uses a generative AI model to create a prompt. A concrete example of this prompt might be, "Steak, generate a short comment suitable for a customer in a relaxed state." Based on this prompt, the AI ​​model utilizes past data and contextual understanding to generate the optimal comment. The output is "short comment data" to be presented to the user.

[0460] Step 4:

[0461] The server sends back the generated one-word comment data to the terminal. The terminal either displays this data visually via a glasses-type output device or conveys it to the user as audio via an ear device. The user can then naturally incorporate this comment into their conversation. The input is one-word comment data, and the output is "the user's natural integration into conversation."

[0462] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0463] This invention relates to a system that aims to recognize the emotions of the user and those around them during a meal and to offer an appropriate comment based on that recognition. This system is implemented by combining the collection of meal data, a device for analyzing emotions, a device for providing the generated comment, and an emotion engine.

[0464] First, the device uses sensors to detect the user's chewing movements during a meal and identifies the contents of the meal. The sensors are used to acquire information about the texture and taste of the ingredients and dishes, and the acquired data is digitized.

[0465] Next, the device uses its camera to capture the facial expressions of people around it and analyzes that facial expression data. Using facial recognition technology, it determines emotions from subtle facial movements. Furthermore, the emotion engine analyzes the emotional state of the user and those present in detail. The emotion engine analyzes the tone of voice and language patterns that match the facial expression data to derive an overall emotional state.

[0466] This allows the server to integrate the meal content and each person's emotional state to generate the most appropriate one-word comment from the AI. This AI utilizes an advanced model that combines diverse meal scenes and emotional patterns, enabling it to produce a contextually accurate comment.

[0467] For example, in a scene where a user is enjoying a steak, if the emotion engine determines that the user's emotional state at that moment is "relaxed and smiling," the generating AI will produce a phrase like, "This steak is cooked perfectly, and everyone looks even brighter!" This phrase is transmitted audibly from the device to the user through the earpiece, allowing the user to naturally incorporate it into their conversation.

[0468] This allows users to communicate more smoothly during meals and avoid awkward situations.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The device uses sensors to acquire data while the user is chewing their food. The sensors detect chewing patterns and pressure, and use this information to acquire data that identifies the contents of the food.

[0472] Step 2:

[0473] The device uses its camera to capture images of the faces of people around the table. The captured images are analyzed in real time and recorded as facial expression data.

[0474] Step 3:

[0475] The device sends facial expression data to the emotion engine for analysis. The emotion engine analyzes this data and determines the emotional state of each person.

[0476] Step 4:

[0477] The emotion engine analyzes voice tone and language patterns based on the user's and those around them' facial expression data, and comprehensively evaluates their emotional state. This enables more precise emotional assessment.

[0478] Step 5:

[0479] The device transmits meal data acquired by sensors and emotional states analyzed by an emotion engine to a server. Data transmission is fast and secure.

[0480] Step 6:

[0481] The server refers to a database of ingredients and dishes to identify ingredients and dishes from the submitted meal data. This allows for detailed classification of the meal content.

[0482] Step 7:

[0483] The server uses a generative AI to generate the most appropriate one-word comment based on the analyzed meal content and emotional state. The AI ​​derives words that are appropriate to the context and people's current emotions.

[0484] Step 8:

[0485] The server generates a short comment and transmits it to the terminal. The terminal then converts the received comment into a format that is easy for the user to understand.

[0486] Step 9:

[0487] The device transmits a short comment as audio through the user's ear device. The user can hear this audio and naturally incorporate it into the conversation.

[0488] This series of steps allows users to receive support in facilitating smooth communication during meals.

[0489] (Example 2)

[0490] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] In dining settings, there is a need to support smooth communication by accurately understanding the emotional states of users and those around them and providing appropriate comments based on that understanding. However, conventional technologies often fail to adequately analyze emotions or generate appropriate comments, which can actually make communication more awkward.

[0492] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0493] In this invention, the server includes means for identifying the contents of a meal using a device equipped with sensors for collecting meal information, means for detecting an emotional state using a device equipped with a camera for analyzing a person's facial expressions, and means for using an emotion engine that analyzes the tone and language patterns of a voice to derive an overall emotional state. This makes it possible to generate a short comment based on the emotions and meal contents analyzed using a generative AI model and provide it to the user as natural-sounding audio.

[0494] "Meal information" refers to information about the user's meals, including details about ingredients, the texture of the dishes, and their taste.

[0495] A "sensor" is a device that collects food information, and has functions to detect the user's chewing movements and measure the characteristics of the food.

[0496] A "device" refers to hardware designed to perform a specific function, such as sensors or cameras used to collect and process data.

[0497] A "camera" is a device that acquires image or video data in order to analyze a person's facial expressions.

[0498] "Emotional state" refers to the psychological and emotional state of the user and those around them, and is derived from various facial expressions and tones of voice.

[0499] An "emotion engine" refers to a program or algorithm that analyzes voice tone and language patterns based on collected biometric data to determine an overall emotional state.

[0500] A "generative AI model" is an artificial intelligence-based algorithm that generates short comments based on meal content and emotional state, and produces appropriate output based on the training data.

[0501] A "one-line comment" is a short sentence or phrase generated in response to the emotional state of the user or those around them, and is used to facilitate communication in a dining setting.

[0502] An "output device" is a device that presents the generated short comment to the user and has the function of providing it as audio via an ear device or similar.

[0503] This invention is a system that aims to recognize the emotions of the user during a meal and the emotions of those around them, and to offer an appropriate comment based on that. The system consists of a terminal worn by the user and a server that serves as the system's central hub.

[0504] The device is equipped with sensors that detect the user's chewing movements during meals, thereby collecting meal information. The sensors measure the characteristics of ingredients and dishes from chewing sounds and movements, convert them into digital data, and transmit it. In addition, a camera is used to capture the facial expressions of the user and those around them. This facial expression data is sent to a server and analyzed by a facial recognition algorithm. At this time, voice tone and language patterns are also collected and used in an emotion engine that determines the overall emotional state.

[0505] The server integrates collected meal information and emotional data, and uses an advanced generative AI model to generate the most appropriate one-word comment. The generative AI model operates based on a dataset of various meal scenes and emotional patterns, outputting the most contextually accurate comment.

[0506] As a concrete example, let's imagine a scene where a user is enjoying a steak with a close friend. In this case, if the emotion engine determines the emotional state to be "relaxed and smiling," the generating AI model can produce a comment such as, "This steak is cooked perfectly. Everyone looks even brighter!"

[0507] The generated comments are delivered to the user via audio through an ear device. This allows users to naturally incorporate comments into conversations, making communication during meals smoother.

[0508] An example of a prompt message is as follows: "The user is enjoying a steak dinner at home with close friends in a relaxed atmosphere. They are smiling and in a laid-back mood. Generate a short comment that fits this scene."

[0509] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0510] Step 1:

[0511] The device uses sensors to collect food information and detect the user's chewing movements. The input is the user's chewing sounds and movements, which the sensors detect and convert into digital data. The resulting data includes information such as the texture of the ingredients and dishes. This data is then sent to the server.

[0512] Step 2:

[0513] The device uses its camera to capture the facial expressions of the user and those around them. The input is the acquired image and video data, which is then analyzed to extract facial expression data. Specifically, the camera identifies expressions such as smiles and surprised faces, and quantifies their characteristics. This processed data is then sent to the server.

[0514] Step 3:

[0515] The server integrates the received meal information and facial expression data. The input is the data obtained from steps 1 and 2, and the emotion engine determines the overall emotional state by analyzing this data along with voice tone and language patterns. The output is data representing the emotions of the user and those around them. Specifically, it determines that a user is relaxed based on their relaxed tone of voice and the degree of their smile.

[0516] Step 4:

[0517] The server uses a generative AI model to generate a short comment based on the analyzed emotional state and meal information. The input is the overall emotional state data and meal information obtained in step 3. Based on this information, the generative AI model generates a contextual comment by referring to a large amount of case data. The output is a short comment optimized for the user.

[0518] Step 5:

[0519] The device provides the user with a generated short comment via voice. The input is the data of the short comment created in step 4. Specifically, the device plays the comment near the user's ear using an ear device. The output is the short comment delivered to the user as voice. This process allows the user to naturally incorporate the comment into their conversation.

[0520] (Application Example 2)

[0521] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0522] In today's restaurant environment, there is a need for new services that facilitate smoother communication during meals and improve the customer experience. However, providing appropriate comments and information in real time that are in line with the customer's emotions and the atmosphere at the table is difficult. Therefore, the introduction of sophisticated dialogue technologies to enhance customer satisfaction is desired.

[0523] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0524] In this invention, the server includes means for identifying ingested food using an information processing device equipped with a sensor for collecting meal data, means for recognizing emotional states using an information processing device equipped with a camera for analyzing a person's facial expressions, means for creating and providing short comments appropriate to the user's situation using an audio output device for presenting generated comments to the user, and means for communicating with a store's database to obtain information related to ingested food. This makes it possible to provide appropriate information in accordance with the customer's emotions and the atmosphere at the dining place.

[0525] A "detector" is a device used to collect data about food intake under specific circumstances and to identify the substances consumed.

[0526] An "information processing device" is a device that analyzes data obtained from sensors and imaging devices and generates necessary information.

[0527] A "photography device" is a device used to capture a person's facial expressions and movements, and is used to recognize their emotional state.

[0528] An "audio output device" is a device that provides audio output for presenting generated information or comments to the user.

[0529] A "database" is a collection of data used to store related information and to search for and retrieve that information as needed.

[0530] A "server" is a computer system that plays a central role in aggregating, processing, and distributing information over a network.

[0531] This invention aims to improve the customer experience in a dining environment within a specific restaurant setting. The system is implemented using a server, terminals (smartphones and tablets), and various sensors and acoustic output devices.

[0532] The server identifies ingested food using an information processing device equipped with sensors for collecting meal data. The technology used includes physical sensors for food identification and database communication for managing menu information. A camera captures a person's facial expressions in real time, and the information processing device analyzes this data to recognize their emotional state. This allows for understanding customer reactions and the atmosphere during the meal.

[0533] Using a generative AI model, a short comment is generated based on collected emotional states and dining information. The generated comment is delivered via the user's smartphone or through an audio output device in the restaurant. This allows customers to incorporate the comment into their actual conversation, promoting natural communication. For example, if a positive reaction is detected from the customer's facial expression when the chef's special dish is served, the server can provide a comment such as, "This special dish really showcases the chef's skill!"

[0534] An example of a prompt might be: "There is a group of people looking at a plate of [dish name]. They are all smiling. Please think of a short comment to brighten the mood."

[0535] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0536] Step 1:

[0537] The terminal uses sensors to collect meal data. This provides raw data on each user's chewing movements and food intake. The terminal analyzes this data, identifies the type and quantity of food in digital format, and transmits it to the server.

[0538] Step 2:

[0539] The terminal captures the customer's facial expressions via a camera. The captured video data is input, and the terminal analyzes this data using a facial recognition algorithm to classify the emotional state. This information is also output to the server.

[0540] Step 3:

[0541] The server integrates the received meal data and emotional data. This generates a dataset showing the relationship between meal content and emotional state. Based on this, the server prepares to generate appropriate comments.

[0542] Step 4:

[0543] The server uses a generative AI model to take integrated data as input and attempts to generate comments through prompts. The generative AI outputs a one-word comment based on the context of the meal and emotions. The prompt used is, "Consider what comment would be appropriate based on the customer's expression as they enjoy this dish."

[0544] Step 5:

[0545] The generated comments are sent from the server to the terminal. The terminal then provides the received comments to the customer using an audio output device. This ensures that a comment related to the food and emotions is shared within the customer's environment.

[0546] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0547] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0548] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0549] [Fourth Embodiment]

[0550] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0551] As shown in Figure 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.

[0552] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0553] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0554] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0555] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0556] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0557] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0558] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0559] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0560] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0561] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0562] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0563] This invention is implemented as a system that provides thoughtful remarks in real time to facilitate smooth communication during meals. The system incorporates a device equipped with sensors for collecting meal data and a camera for detecting emotional states.

[0564] First, when the user eats, the device uses sensors attached to the teeth to detect chewing movements and acquire information about the food consumed. This information is then digitized as data related to the ingredients and dishes used.

[0565] Next, the device uses its camera to capture the facial expressions of the people present in real time. Based on this video data, facial recognition technology is applied to analyze each person's emotional state.

[0566] The analyzed data is sent to a server, where a generative AI generates the most appropriate one-word comment based on the meal content and emotional state. This generative AI has the ability to select appropriate words based on the context by accumulating past data through machine learning.

[0567] The generated short comment is provided to the user via the device. This output is transmitted as audio using the user's usual ear device, allowing the user to use it naturally in conversation.

[0568] For example, if a user is enjoying a pasta dish and those around them appear relaxed, the generated comment might be something like, "This pasta has an exquisite sauce!" In this way, the present invention always provides the user with a conversation starter appropriate to the situation, creating a pleasant atmosphere during the meal.

[0569] The following describes the processing flow.

[0570] Step 1:

[0571] The device uses sensors to detect the user's chewing movements and collects meal data. The data is processed in real time and recorded as characteristic information to identify ingredients and dishes.

[0572] Step 2:

[0573] The device uses its camera to capture images of the faces of people around it. The captured video data is input into a facial expression analysis module, and the emotional state is determined based on the information obtained from it.

[0574] Step 3:

[0575] The device transmits collected meal and emotional data to the server. Communication takes place securely and quickly via the internet.

[0576] Step 4:

[0577] The server matches meal data against a database to identify ingredients and dish names. The ingredient database contains information on various dishes, and the meal content is classified in detail based on the matching results.

[0578] Step 5:

[0579] The server analyzes emotional data and maps the resulting facial expression information to emotional categories. Using facial recognition technology, it instantly classifies emotional states such as "smiling," "surprised," and "serious."

[0580] Step 6:

[0581] The server uses a generative AI to generate a short comment based on the identified meal content and emotional state. The generative AI utilizes a contextual model to select the most appropriate expression.

[0582] Step 7:

[0583] The server sends the generated comment to the terminal. This comment is then converted into a format that the user can receive via their understanding device.

[0584] Step 8:

[0585] The device transmits a generated short comment as audio through the user's ear device. The user can hear this and naturally incorporate it into the conversation.

[0586] This entire process allows users to receive a brief comment at the appropriate time and with the right content during a meal, thus facilitating smooth communication.

[0587] (Example 1)

[0588] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0589] In modern society, smooth communication during meals is crucial for enhancing individual social satisfaction and well-being. However, many people struggle to find appropriate conversation starters. This challenge is particularly pronounced when people from different generations and cultural backgrounds share a meal. Therefore, there is a need for a system that naturally facilitates communication during meals and provides appropriate comments for the situation.

[0590] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0591] In this invention, the server includes means for sensing and identifying the contents of a meal using a device equipped with sensors for collecting information about the meal, means for extracting emotional information using a device equipped with a camera for analyzing a person's emotional state, and system means for generating and presenting a timely comment using an artificial intelligence model and the emotional information and meal information. This facilitates smooth communication during meals and makes it possible to naturally provide conversation appropriate to the atmosphere of the situation.

[0592] A "sensor" is a device that detects physical movements or states in a specific environment and converts them into electrical signals.

[0593] A "device" is a combination of hardware and software designed to achieve a specific function or purpose.

[0594] A "recording device" is a device equipped with technology for recording video or images.

[0595] "Emotional information" refers to data about a person's psychological state obtained by analyzing their facial expressions and behavior.

[0596] An "artificial intelligence model" is an algorithm and its implementation designed to learn from data and perform a task.

[0597] A "timely remark" is the act of making a short comment or statement that is immediately relevant to the situation.

[0598] An "output device" is a device that converts digital information into a format that humans can recognize and present it.

[0599] This invention is a system designed to facilitate smooth communication among users during meals. This system is realized through the collaborative efforts of several devices and algorithms.

[0600] The device first uses sensors attached to the teeth to collect information about the meal. These sensors physically sense the user's chewing movements and detect the type of food and dishes. Specific chewing patterns are digitized as data to identify different meal contents.

[0601] The terminal also uses a camera to capture the facial expressions of each person present in real time. The video data is analyzed using facial recognition technology, and individual emotional states are extracted. This provides people's psychological states as numerical data.

[0602] This information is sent to the server. The server integrates the meal information and emotional information and sends a prompt to the generative AI model. This prompt functions as an instruction to generate a contextually appropriate one-word comment. For example, it might be in the format of, "Generate a one-word comment about having pasta as a meal and being relaxed."

[0603] The generative AI model utilizes accumulated data and learned knowledge to generate the most appropriate one-word response, which is then received by the server. The generated one-word comment is transmitted as audio to the ear device via the terminal and presented to the user.

[0604] For example, if a user is enjoying a pasta dish and the people around them are in a relaxed mood, the generated comment might be, "This pasta has an exquisite sauce!" In this way, the present invention provides users with conversation starters appropriate to the situation and promotes natural communication.

[0605] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0606] Step 1:

[0607] The device recognizes that the user has started eating. Sensors attached to the teeth detect the chewing pattern and acquire the signal as digital data. The input is the physical signal from the sensor, and the output is digitized food content data. This data indicates the type of ingredients and dishes.

[0608] Step 2:

[0609] The device uses a camera to capture the facial expressions of people sitting together during a meal in real time. The input is video data obtained through the camera, and the output is analyzed emotional information. Using facial recognition technology, each person's emotions are extracted as numerical data from the video data.

[0610] Step 3:

[0611] The server receives meal content data and emotional information sent from the terminal and integrates them. The input is meal content data and emotional information, and the output is a prompt statement for analyzing them together. This prompt statement serves as a command to run the generative AI model.

[0612] Step 4:

[0613] The server sends prompt messages to the AI ​​model based on the integrated data. These prompt messages specify a concrete situation. For example, "Generate a one-word comment describing a relaxed atmosphere while eating pasta." The input is the prompt message, and the output is the generated one-word comment.

[0614] Step 5:

[0615] A short comment generated from the server is sent to the terminal and transmitted to the user as audio through an ear device. The input is the generated short comment, and the output is audio information for the user to use in conversation. This allows the user to participate in the conversation in a natural way.

[0616] (Application Example 1)

[0617] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] Conversation during meals can sometimes be difficult, and natural communication between staff and customers is especially important in brick-and-mortar restaurants. However, it is difficult for staff to always provide the right comment, which can lead to decreased customer satisfaction. A system is needed to solve this problem and provide a better customer experience.

[0619] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0620] In this invention, the server includes means for identifying meal information using a device equipped with an instrument for collecting meal data, means for detecting emotional states using a device equipped with a camera for analyzing a person's facial expressions, means for generating and providing short comments in a timely manner using an output device for providing generated comments to the user, and means for providing conversation support for the user using a glasses-type device in a physical store. This makes it possible to facilitate natural and smooth conversation during meals.

[0621] A "device for collecting meal data" is a device that senses chewing and eating actions during a meal and obtains information about the content of the meal.

[0622] "Photography equipment for analyzing human facial expressions" refers to a device that uses a camera or sensor to photograph a person's face and analyze their facial expressions.

[0623] An "output device" is a device that transmits generated information or comments to the user, and has the function of providing information in the form of audio or visuals.

[0624] A "glasses-type device" is a confidential device that can provide information within the wearer's field of vision, and generally utilizes technologies such as AR (augmented reality).

[0625] A "physical store" is a commercial facility that provides goods and services in a physical location, and includes restaurants, cafes, and other similar establishments.

[0626] This invention provides a system to support natural and smooth communication among users during meals. This system is achieved by collecting meal data, analyzing emotional states, and generating appropriate short comments.

[0627] The server first processes information sent from a terminal equipped with sensors that detect chewing and eating actions in order to collect meal data. This terminal identifies information containing the contents of the meal as digital data and sends it to the server. Next, a camera is used to capture video data in order to analyze the facial expressions of the people present. Based on this video data, the server analyzes the emotional state using an expression recognition algorithm and classifies it into an emotional category.

[0628] Furthermore, the server uses a generative AI model to generate the most appropriate one-word comment based on meal data and emotional data. This AI model has the ability to store past data through machine learning and select contextually appropriate words. The generated comment is either visually displayed via glasses-type output devices worn by the user, or transmitted audibly via ear devices. For example, if a user is eating a steak and their emotional state is relaxed, the system will generate a comment such as, "The steak looks perfectly cooked!"

[0629] An example of a prompt is, "Steak, generate a short comment suitable for a customer in a relaxed state." Based on this prompt, the AI ​​model constructs the optimal comment.

[0630] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0631] Step 1:

[0632] The device uses sensors to detect chewing and eating actions when the user is eating. The detected action data is collected as digital signals to identify the contents of the meal. In this process, the input is information about the physical chewing action, and the output is sent to the server as "meal content data."

[0633] Step 2:

[0634] The device uses a camera to capture the facial expressions of the people present in real time. Based on the video data acquired by the camera, an expression recognition algorithm is applied to analyze their emotional state. The input here is camera video data, and the output is interpreted as "emotional state data" and sent to the server.

[0635] Step 3:

[0636] The server integrates the received meal content data and emotional state data, and uses a generative AI model to create a prompt. A concrete example of this prompt might be, "Steak, generate a short comment suitable for a customer in a relaxed state." Based on this prompt, the AI ​​model utilizes past data and contextual understanding to generate the optimal comment. The output is "short comment data" to be presented to the user.

[0637] Step 4:

[0638] The server sends back the generated one-word comment data to the terminal. The terminal either displays this data visually via a glasses-type output device or conveys it to the user as audio via an ear device. The user can then naturally incorporate this comment into their conversation. The input is one-word comment data, and the output is "the user's natural integration into conversation."

[0639] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0640] This invention relates to a system that aims to recognize the emotions of the user and those around them during a meal and to offer an appropriate comment based on that recognition. This system is implemented by combining the collection of meal data, a device for analyzing emotions, a device for providing the generated comment, and an emotion engine.

[0641] First, the device uses sensors to detect the user's chewing movements during a meal and identifies the contents of the meal. The sensors are used to acquire information about the texture and taste of the ingredients and dishes, and the acquired data is digitized.

[0642] Next, the device uses its camera to capture the facial expressions of people around it and analyzes that facial expression data. Using facial recognition technology, it determines emotions from subtle facial movements. Furthermore, the emotion engine analyzes the emotional state of the user and those present in detail. The emotion engine analyzes the tone of voice and language patterns that match the facial expression data to derive an overall emotional state.

[0643] This allows the server to integrate the meal content and each person's emotional state to generate the most appropriate one-word comment from the AI. This AI utilizes an advanced model that combines diverse meal scenes and emotional patterns, enabling it to produce a contextually accurate comment.

[0644] For example, in a scene where a user is enjoying a steak, if the emotion engine determines that the user's emotional state at that moment is "relaxed and smiling," the generating AI will produce a phrase like, "This steak is cooked perfectly, and everyone looks even brighter!" This phrase is transmitted audibly from the device to the user through the earpiece, allowing the user to naturally incorporate it into their conversation.

[0645] This allows users to communicate more smoothly during meals and avoid awkward situations.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The device uses sensors to acquire data while the user is chewing their food. The sensors detect chewing patterns and pressure, and use this information to acquire data that identifies the contents of the food.

[0649] Step 2:

[0650] The device uses its camera to capture images of the faces of people around the table. The captured images are analyzed in real time and recorded as facial expression data.

[0651] Step 3:

[0652] The device sends facial expression data to the emotion engine for analysis. The emotion engine analyzes this data and determines the emotional state of each person.

[0653] Step 4:

[0654] The emotion engine analyzes voice tone and language patterns based on the user's and those around them' facial expression data, and comprehensively evaluates their emotional state. This enables more precise emotional assessment.

[0655] Step 5:

[0656] The device transmits meal data acquired by sensors and emotional states analyzed by an emotion engine to a server. Data transmission is fast and secure.

[0657] Step 6:

[0658] The server refers to a database of ingredients and dishes to identify ingredients and dishes from the submitted meal data. This allows for detailed classification of the meal content.

[0659] Step 7:

[0660] The server uses a generative AI to generate the most appropriate one-word comment based on the analyzed meal content and emotional state. The AI ​​derives words that are appropriate to the context and people's current emotions.

[0661] Step 8:

[0662] The server generates a short comment and transmits it to the terminal. The terminal then converts the received comment into a format that is easy for the user to understand.

[0663] Step 9:

[0664] The device transmits a short comment as audio through the user's ear device. The user can hear this audio and naturally incorporate it into the conversation.

[0665] This series of steps allows users to receive support in facilitating smooth communication during meals.

[0666] (Example 2)

[0667] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0668] In dining settings, there is a need to support smooth communication by accurately understanding the emotional states of users and those around them and providing appropriate comments based on that understanding. However, conventional technologies often fail to adequately analyze emotions or generate appropriate comments, which can actually make communication more awkward.

[0669] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0670] In this invention, the server includes means for identifying the contents of a meal using a device equipped with sensors for collecting meal information, means for detecting an emotional state using a device equipped with a camera for analyzing a person's facial expressions, and means for using an emotion engine that analyzes the tone and language patterns of a voice to derive an overall emotional state. This makes it possible to generate a short comment based on the emotions and meal contents analyzed using a generative AI model and provide it to the user as natural-sounding audio.

[0671] "Meal information" refers to information about the user's meals, including details about ingredients, the texture of the dishes, and their taste.

[0672] A "sensor" is a device that collects food information, and has functions to detect the user's chewing movements and measure the characteristics of the food.

[0673] A "device" refers to hardware designed to perform a specific function, such as sensors or cameras used to collect and process data.

[0674] A "camera" is a device that acquires image or video data in order to analyze a person's facial expressions.

[0675] "Emotional state" refers to the psychological and emotional state of the user and those around them, and is derived from various facial expressions and tones of voice.

[0676] An "emotion engine" refers to a program or algorithm that analyzes voice tone and language patterns based on collected biometric data to determine an overall emotional state.

[0677] A "generative AI model" is an artificial intelligence-based algorithm that generates short comments based on meal content and emotional state, and produces appropriate output based on the training data.

[0678] A "one-line comment" is a short sentence or phrase generated in response to the emotional state of the user or those around them, and is used to facilitate communication in a dining setting.

[0679] An "output device" is a device that presents the generated short comment to the user and has the function of providing it as audio via an ear device or similar.

[0680] This invention is a system that aims to recognize the emotions of the user during a meal and the emotions of those around them, and to offer an appropriate comment based on that. The system consists of a terminal worn by the user and a server that serves as the system's central hub.

[0681] The device is equipped with sensors that detect the user's chewing movements during meals, thereby collecting meal information. The sensors measure the characteristics of ingredients and dishes from chewing sounds and movements, convert them into digital data, and transmit it. In addition, a camera is used to capture the facial expressions of the user and those around them. This facial expression data is sent to a server and analyzed by a facial recognition algorithm. At this time, voice tone and language patterns are also collected and used in an emotion engine that determines the overall emotional state.

[0682] The server integrates collected meal information and emotional data, and uses an advanced generative AI model to generate the most appropriate one-word comment. The generative AI model operates based on a dataset of various meal scenes and emotional patterns, outputting the most contextually accurate comment.

[0683] As a concrete example, let's imagine a scene where a user is enjoying a steak with a close friend. In this case, if the emotion engine determines the emotional state to be "relaxed and smiling," the generating AI model can produce a comment such as, "This steak is cooked perfectly. Everyone looks even brighter!"

[0684] The generated comments are delivered to the user via audio through an ear device. This allows users to naturally incorporate comments into conversations, making communication during meals smoother.

[0685] An example of a prompt message is as follows: "The user is enjoying a steak dinner at home with close friends in a relaxed atmosphere. They are smiling and in a laid-back mood. Generate a short comment that fits this scene."

[0686] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0687] Step 1:

[0688] The device uses sensors to collect food information and detect the user's chewing movements. The input is the user's chewing sounds and movements, which the sensors detect and convert into digital data. The resulting data includes information such as the texture of the ingredients and dishes. This data is then sent to the server.

[0689] Step 2:

[0690] The device uses its camera to capture the facial expressions of the user and those around them. The input is the acquired image and video data, which is then analyzed to extract facial expression data. Specifically, the camera identifies expressions such as smiles and surprised faces, and quantifies their characteristics. This processed data is then sent to the server.

[0691] Step 3:

[0692] The server integrates the received meal information and facial expression data. The input is the data obtained from steps 1 and 2, and the emotion engine determines the overall emotional state by analyzing this data along with voice tone and language patterns. The output is data representing the emotions of the user and those around them. Specifically, it determines that a user is relaxed based on their relaxed tone of voice and the degree of their smile.

[0693] Step 4:

[0694] The server uses a generative AI model to generate a short comment based on the analyzed emotional state and meal information. The input is the overall emotional state data and meal information obtained in step 3. Based on this information, the generative AI model generates a contextual comment by referring to a large amount of case data. The output is a short comment optimized for the user.

[0695] Step 5:

[0696] The device provides the user with a generated short comment via voice. The input is the data of the short comment created in step 4. Specifically, the device plays the comment near the user's ear using an ear device. The output is the short comment delivered to the user as voice. This process allows the user to naturally incorporate the comment into their conversation.

[0697] (Application Example 2)

[0698] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0699] In today's restaurant environment, there is a need for new services that facilitate smoother communication during meals and improve the customer experience. However, providing appropriate comments and information in real time that are in line with the customer's emotions and the atmosphere at the table is difficult. Therefore, the introduction of sophisticated dialogue technologies to enhance customer satisfaction is desired.

[0700] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0701] In this invention, the server includes means for identifying ingested food using an information processing device equipped with a sensor for collecting meal data, means for recognizing emotional states using an information processing device equipped with a camera for analyzing a person's facial expressions, means for creating and providing short comments appropriate to the user's situation using an audio output device for presenting generated comments to the user, and means for communicating with a store's database to obtain information related to ingested food. This makes it possible to provide appropriate information in accordance with the customer's emotions and the atmosphere at the dining place.

[0702] A "detector" is a device used to collect data about food intake under specific circumstances and to identify the substances consumed.

[0703] An "information processing device" is a device that analyzes data obtained from sensors and imaging devices and generates necessary information.

[0704] A "photography device" is a device used to capture a person's facial expressions and movements, and is used to recognize their emotional state.

[0705] An "audio output device" is a device that provides audio output for presenting generated information or comments to the user.

[0706] A "database" is a collection of data used to store related information and to search for and retrieve that information as needed.

[0707] A "server" is a computer system that plays a central role in aggregating, processing, and distributing information over a network.

[0708] This invention aims to improve the customer experience in a dining environment within a specific restaurant setting. The system is implemented using a server, terminals (smartphones and tablets), and various sensors and acoustic output devices.

[0709] The server identifies ingested food using an information processing device equipped with sensors for collecting meal data. The technology used includes physical sensors for food identification and database communication for managing menu information. A camera captures a person's facial expressions in real time, and the information processing device analyzes this data to recognize their emotional state. This allows for understanding customer reactions and the atmosphere during the meal.

[0710] Using a generative AI model, a short comment is generated based on collected emotional states and dining information. The generated comment is delivered via the user's smartphone or through an audio output device in the restaurant. This allows customers to incorporate the comment into their actual conversation, promoting natural communication. For example, if a positive reaction is detected from the customer's facial expression when the chef's special dish is served, the server can provide a comment such as, "This special dish really showcases the chef's skill!"

[0711] An example of a prompt might be: "There is a group of people looking at a plate of [dish name]. They are all smiling. Please think of a short comment to brighten the mood."

[0712] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0713] Step 1:

[0714] The terminal uses sensors to collect meal data. This provides raw data on each user's chewing movements and food intake. The terminal analyzes this data, identifies the type and quantity of food in digital format, and transmits it to the server.

[0715] Step 2:

[0716] The terminal captures the customer's facial expressions via a camera. The captured video data is input, and the terminal analyzes this data using a facial recognition algorithm to classify the emotional state. This information is also output to the server.

[0717] Step 3:

[0718] The server integrates the received meal data and emotional data. This generates a dataset showing the relationship between meal content and emotional state. Based on this, the server prepares to generate appropriate comments.

[0719] Step 4:

[0720] The server uses a generative AI model to take integrated data as input and attempts to generate comments through prompts. The generative AI outputs a one-word comment based on the context of the meal and emotions. The prompt used is, "Consider what comment would be appropriate based on the customer's expression as they enjoy this dish."

[0721] Step 5:

[0722] The generated comments are sent from the server to the terminal. The terminal then provides the received comments to the customer using an audio output device. This ensures that a comment related to the food and emotions is shared within the customer's environment.

[0723] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0724] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0725] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0726] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0727] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0728] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0729] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0730] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0731] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0732] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0733] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0734] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0735] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0736] 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.

[0737] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0738] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0739] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0740] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0741] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0742] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0743] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0744] The following is further disclosed regarding the embodiments described above.

[0745] (Claim 1)

[0746] A device equipped with sensors for collecting meal data provides a means for identifying the contents of a meal,

[0747] A device equipped with a camera for analyzing human facial expressions provides a means for detecting emotional states,

[0748] It includes an output device for presenting generated comments to the user, and means for generating and providing short comments in a timely manner.

[0749] A system that includes this.

[0750] (Claim 2)

[0751] The system according to claim 1, wherein in identifying the contents of the meal, the system refers to a database to identify ingredients and dishes.

[0752] (Claim 3)

[0753] The system according to claim 1, wherein in detecting the emotional state, an facial expression recognition algorithm is used to classify the recognized facial expression into a pre-set emotional category.

[0754] "Example 1"

[0755] (Claim 1)

[0756] A device equipped with sensors for collecting information about meals, a means for sensing and identifying the contents of a meal,

[0757] A device equipped with a camera for analyzing a person's emotional state, and a means for extracting emotional information,

[0758] A system means comprising an output device for generating and presenting a timely comment using an artificial intelligence model, utilizing emotional information and meal information,

[0759] A system that includes this.

[0760] (Claim 2)

[0761] The system according to claim 1, wherein in identifying the aforementioned meal information, the system refers to an information recording device to identify food and dishes.

[0762] (Claim 3)

[0763] The system according to claim 1, wherein in the extraction of the emotional information, an analysis technique is used to associate recognized facial expressions with pre-set emotional categories.

[0764] "Application Example 1"

[0765] (Claim 1)

[0766] A device equipped with instruments for collecting dietary data, a means for identifying dietary information,

[0767] A device equipped with photographic equipment for analyzing a person's facial expressions provides a means for detecting emotional states,

[0768] It includes an output device for providing generated comments to users, and means for generating and providing short comments in a timely manner,

[0769] A means of providing conversational support for users using glasses-type devices in physical stores,

[0770] A system that includes this.

[0771] (Claim 2)

[0772] The system according to claim 1, wherein in identifying the aforementioned meal information, a recording device is used to identify the ingredients and prepared food.

[0773] (Claim 3)

[0774] The system according to claim 1, wherein in detecting the emotional state, an facial expression recognition algorithm is used to classify the recognized facial expression into a pre-set emotional classification.

[0775] "Example 2 of combining an emotion engine"

[0776] (Claim 1)

[0777] A device equipped with sensors for collecting meal information, with means for identifying the contents of a meal,

[0778] A device equipped with a camera for analyzing a person's facial expressions provides a means for detecting emotional states,

[0779] This method uses an emotion engine that analyzes voice tone and language patterns to derive an overall emotional state,

[0780] A means of generating a short comment based on emotions and meal content analyzed by a generative AI model, and providing it to the user via voice through an output device,

[0781] A system that includes this.

[0782] (Claim 2)

[0783] The system according to claim 1, wherein in identifying the contents of the meal, the system refers to an information base to identify ingredients and dishes.

[0784] (Claim 3)

[0785] The system according to claim 1, wherein in detecting the emotional state, a facial recognition algorithm is used to classify the recognized facial expression into a pre-set emotional category.

[0786] "Application example 2 when combining with an emotional engine"

[0787] (Claim 1)

[0788] An information processing device equipped with a sensor for collecting dietary data provides means for identifying ingested substances,

[0789] An information processing device equipped with a camera for analyzing a person's facial expressions provides a means for recognizing an emotional state,

[0790] A means of generating and providing short comments that correspond to the user's situation, equipped with an audio output device for presenting the generated comments to the user,

[0791] A means of communicating with the store's database to obtain information related to the ingested food,

[0792] ...

[0793] A system that includes this.

[0794] (Claim 2)

[0795] The system according to claim 1, wherein in identifying the contents of the meal, the ingredients and cooking methods are identified by referring to a database.

[0796] (Claim 3)

[0797] The system according to claim 1, which utilizes a facial expression recognition algorithm for classifying analyzed facial expressions into pre-set emotional categories in the recognition of the aforementioned emotional state. [Explanation of symbols]

[0798] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device equipped with sensors for collecting meal data provides a means for identifying the contents of a meal, A device equipped with a camera for analyzing human facial expressions provides a means for detecting emotional states, It includes an output device for presenting generated comments to the user, and means for generating and providing short comments in a timely manner. A system that includes this.

2. The system according to claim 1, wherein in identifying the contents of the meal, the system refers to a database to identify ingredients and dishes.

3. The system according to claim 1, wherein in detecting the emotional state, an facial expression recognition algorithm is used to classify the recognized facial expression into a pre-set emotional category.

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