System

Abstract

An object of a system according to an embodiment is to provide appropriate advice for preventing excessive drinking based on a health condition and a physical constitution of a drinker.SOLUTION: A system includes a constitution acquisition part, a health condition collection part, a drinking data collection part, an advice provision part, and a physical condition check part. The physical constitution acquisition unit acquires a physical constitution of a drinker to alcohol. The health condition collection unit collects a health condition of today. The drinking data collection unit collects types and amounts of alcohol to be drunk. The advice providing unit gives advice when taking the next cup. The physical condition check unit checks a physical condition in cooperation with a watch device.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to provide appropriate advice in real time to prevent excessive drinking.

[0005] The system according to the embodiment aims to provide appropriate advice to prevent excessive drinking based on the drinker's health condition and constitution. [Means for solving the problem]

[0006] The system according to the embodiment includes a constitution acquisition unit, a health condition collection unit, a drinking data collection unit, an advice provision unit, and a physical condition check unit. The constitution acquisition unit acquires the drinker's constitution to alcohol. The health condition collection unit collects today's health condition. The drinking data collection unit collects the type and amount of alcohol consumed. The advice provision unit provides advice when it is time to drink the next drink. The physical condition check unit checks the health condition in cooperation with a watch device. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate advice to prevent overdrinking based on the drinker's health condition and constitution. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The excessive drinking prevention system according to an embodiment of the present invention is a system in which AI determines whether it is okay for the drinker to have the next drink and gives advice based on the drinker's health condition, alcohol tolerance, and current alcohol intake. This allows the excessive drinking prevention system to maintain the drinker's health and prevent them from drinking too much.

[0029] The overdrinking prevention system according to the embodiment includes a constitutional predisposition acquisition unit, a health condition collection unit, a drinking data collection unit, an advice provision unit, and a health condition check unit. The constitutional predisposition acquisition unit acquires the drinker's alcohol predisposition. For example, the generation AI may ask the drinker questions such as, "How much alcohol do you need to drink to get drunk?" and determine the drinker's constitution based on the drinker's response. The constitutional predisposition acquisition unit also scientifically evaluates the drinker's alcohol tolerance using patch test and medical examination data. The health condition collection unit collects the drinker's current health condition. For example, the generation AI may ask the drinker questions such as, "How are you feeling today?" and evaluate the drinker's health condition based on the drinker's response. The health condition collection unit also connects with the drinker's smartphone calendar app to check important appointments for the next day. The drinking data collection unit collects the type and amount of alcohol consumed. For example, the generation AI may ask the drinker questions such as, "What type and amount of alcohol are you drinking now?" and record data based on the drinker's response. The drinking data collection unit also includes a function that enables the watch device to automatically measure alcohol intake. The advice providing unit gives advice when it's time to drink the next drink. For example, when the drinker asks, "Can I drink next?", the generation AI gives advice such as, "Beer and sours are OK, but sake is not." Also, when the advice providing unit asks, "What should I drink next?", the generation AI reads the menu and suggests an appropriate drink. The health check unit checks the drinker's health in cooperation with a watch device. For example, the watch device monitors the drinker's heart rate, and if an abnormality is detected, the generation AI issues a warning such as, "Your heart rate is high. Take a short break." In this way, the overdrinking prevention system according to the embodiment can maintain the drinker's health and prevent them from drinking too much.

[0030] The constitution acquisition unit can analyze the drinker's past drinking history, identify drinking patterns, and evaluate their constitution. For example, the constitution acquisition unit uses a generation AI to collect the drinker's past drinking history and analyze the amount and frequency of drinking, as well as changes in physical condition after drinking. For example, it records in a database what types and amounts of alcohol the drinker has consumed in the past, and how their physical condition was the next day, and identifies their drinking pattern. This allows for an accurate evaluation of the drinker's constitution.

[0031] The constitution acquisition unit can analyze the drinker's genetic information and evaluate genetic factors related to alcohol metabolism. The constitution acquisition unit, for example, collects the drinker's genetic information and analyzes genes related to alcohol metabolism. For example, genes such as ALDH2 and ADH1B are examined to evaluate alcohol tolerance. This makes it possible to evaluate constitution based on genetic factors.

[0032] The health status collection unit can analyze the drinker's sleep data and evaluate the quality of sleep and health status. For example, the generation AI collects the drinker's sleep data and analyzes the quality of sleep. For example, it evaluates the sleep time and the percentage of deep sleep to determine the health status. This makes it possible to evaluate the health status based on the sleep data.

[0033] The health status collection unit can measure the drinker's stress level and evaluate the impact of stress on their health status. For example, the generation AI measures the drinker's stress level in real time and evaluates their health status. For example, it analyzes heart rate variability and electrodermal activity to evaluate the stress level. This makes it possible to evaluate the health status based on the stress level.

[0034] The drinking data collection unit can analyze the drinker's drinking speed and evaluate the drinking pace. For example, the drinking data collection unit uses a generation AI to analyze the drinker's drinking speed in real time and evaluate the drinking pace. For example, it measures the time it takes the drinker to finish one drink and determines the drinking pace. This makes it possible to evaluate the drinking pace based on the drinking speed.

[0035] The drinking data collection unit can collect information about the drinking environment of the drinker and evaluate the effects of drinking. For example, the drinking data collection unit collects data about the drinking environment of the drinker and analyzes the effects of temperature and humidity on drinking. For example, it evaluates whether high room temperature speeds up alcohol absorption. This makes it possible to evaluate the effects of drinking based on the drinking environment.

[0036] The advice providing unit can predict the impact of the next drink based on the drinker's past drinking history. For example, the advice providing unit uses a generation AI to analyze the drinker's past drinking history and predict the impact of the next drink. For example, it evaluates the impact the next drink will have on physical condition based on past data. This makes it possible to predict the impact of the next drink based on the drinker's past drinking history.

[0037] The advice providing unit can evaluate the impact of the next drink in real time based on the drinker's current physical condition data. For example, the advice providing unit uses a generation AI to analyze the drinker's current physical condition data in real time and evaluate the impact of the next drink. For example, it predicts the impact of the next drink on physical condition based on heart rate and blood pressure data. This makes it possible to evaluate the impact of the next drink in real time based on the drinker's current physical condition data.

[0038] The physical condition check section can analyze the drinker's heart rate fluctuations and evaluate changes in physical condition in real time. For example, the generation AI can analyze the drinker's heart rate fluctuations in real time and evaluate changes in physical condition. For example, it can detect a sudden increase in heart rate or arrhythmia and determine abnormalities in physical condition. This makes it possible to evaluate changes in physical condition in real time based on heart rate fluctuations.

[0039] The physical condition check unit can measure the skin temperature of the drinker and evaluate the physical condition based on changes in body temperature. For example, the physical condition check unit measures the skin temperature of the drinker in real time using a watch device and evaluates the physical condition based on changes in body temperature. For example, it can detect a sudden rise or fall in body temperature and determine whether the physical condition is abnormal. This makes it possible to evaluate the physical condition based on the skin temperature.

[0040] The physical condition check unit can collect breathing data from the drinker and evaluate the drinker's physical condition based on changes in breathing patterns. For example, the physical condition check unit uses a watch device to collect the drinker's breathing data in real time and evaluate the drinker's physical condition based on changes in breathing patterns. For example, the physical condition check unit can detect a sudden increase or decrease in breathing rate and determine whether the drinker's physical condition is abnormal. This allows the drinker's physical condition to be evaluated based on the breathing data.

[0041] The physical condition check unit can collect the drinker's blood pressure data and evaluate the drinker's physical condition based on changes in blood pressure. For example, the physical condition check unit uses a watch device to collect the drinker's blood pressure data in real time and evaluate the drinker's physical condition based on changes in blood pressure. For example, the unit can detect a sudden increase or decrease in blood pressure and determine whether the drinker's physical condition is abnormal. This allows the drinker's physical condition to be evaluated based on the blood pressure data.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The excessive drinking prevention system can also include a meal data collection unit that collects the drinker's meal data and evaluates the effects of drinking. For example, the system can record the type and amount of food the drinker ate before drinking and evaluate the rate and effects of alcohol absorption. This allows for a more accurate evaluation of the effects of drinking based on the meal data.

[0044] The excessive drinking prevention system can also be equipped with an exercise data collection unit that collects the drinker's exercise data and evaluates the effects of drinking. For example, the system can record the type and intensity of exercise the drinker performed before drinking and evaluate the alcohol metabolism rate and effects. This allows for a more accurate evaluation of the effects of drinking based on the exercise data.

[0045] The excessive drinking prevention system may further include a social situation collection unit that collects information about the drinker's social situation and evaluates the impact of drinking. For example, the system may record information about the place where the drinker is drinking and the people they are with, and evaluate the impact of the social situation on drinking. This allows for a more accurate assessment of the impact of drinking based on the social situation.

[0046] The excessive drinking prevention system may further include a sleep data collection unit that collects the drinker's sleep data and evaluates the effects of drinking. For example, the system may record how much sleep the drinker gets before and after drinking, and evaluate the effect of sleep quality on drinking. This allows for a more accurate evaluation of the effects of drinking based on the sleep data.

[0047] The excessive drinking prevention system can also include a body temperature data collection unit that collects the drinker's body temperature data and evaluates the effects of drinking. For example, it can record the change in the drinker's body temperature before and after drinking and evaluate the effect of the change in body temperature on drinking. This allows for a more accurate evaluation of the effects of drinking based on the body temperature data.

[0048] The excessive drinking prevention system can also be equipped with a blood pressure data collection unit that collects the drinker's blood pressure data and evaluates the effects of drinking. For example, it can record the amount of change in blood pressure before and after drinking and evaluate the effect of blood pressure changes on drinking. This allows for a more accurate evaluation of the effects of drinking based on blood pressure data.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The constitution acquisition unit acquires the drinker's alcohol tolerance. For example, the generation AI asks the drinker questions such as, "How much alcohol does it take for you to get drunk?" and determines the drinker's constitution based on the drinker's response. The constitution acquisition unit also uses patch test and medical examination data to scientifically evaluate the drinker's alcohol tolerance. Step 2: The health status collection unit collects information about the day's health status. For example, the generation AI asks the drinker questions such as, "How are you feeling today?" and evaluates their health status based on their response. The health status collection unit also connects with the drinker's smartphone calendar app to check important appointments for the next day. Step 3: The drinking data collection unit collects the type and amount of alcohol consumed. For example, the generation AI asks the drinker questions such as, "Please tell us the type and amount of alcohol you are drinking right now," and records data based on the drinker's response. The drinking data collection unit also includes a function that allows the watch device to automatically measure alcohol intake. Step 4: The advice-providing unit gives advice when it's time to drink the next drink. For example, if the drinker asks, "Can I drink this next?", the generation AI will give advice such as, "Beer and sours are OK, but sake is not." Also, if the drinker asks, "Which drink should I drink next?", the advice-providing unit will read the menu and suggest an appropriate drink. Step 5: The health check unit checks the user's health in conjunction with the watch device. For example, the watch device monitors the user's heart rate, and if an abnormality is detected, the AI ​​generator warns the user, saying, "Your heart rate is high. Take a short break."

[0051] (Example 2) The excessive drinking prevention system according to an embodiment of the present invention is a system in which AI determines whether it is okay for the drinker to have the next drink and gives advice based on the drinker's health condition, alcohol tolerance, and current alcohol intake. This allows the excessive drinking prevention system to maintain the drinker's health and prevent them from drinking too much.

[0052] The overdrinking prevention system according to the embodiment includes a constitutional predisposition acquisition unit, a health condition collection unit, a drinking data collection unit, an advice provision unit, and a health condition check unit. The constitutional predisposition acquisition unit acquires the drinker's alcohol predisposition. For example, the generation AI may ask the drinker questions such as, "How much alcohol do you need to drink to get drunk?" and determine the drinker's constitution based on the drinker's response. The constitutional predisposition acquisition unit also scientifically evaluates the drinker's alcohol tolerance using patch test and medical examination data. The health condition collection unit collects the drinker's current health condition. For example, the generation AI may ask the drinker questions such as, "How are you feeling today?" and evaluate the drinker's health condition based on the drinker's response. The health condition collection unit also connects with the drinker's smartphone calendar app to check important appointments for the next day. The drinking data collection unit collects the type and amount of alcohol consumed. For example, the generation AI may ask the drinker questions such as, "What type and amount of alcohol are you drinking now?" and record data based on the drinker's response. The drinking data collection unit also includes a function that enables the watch device to automatically measure alcohol intake. The advice providing unit gives advice when it's time to drink the next drink. For example, when the drinker asks, "Can I drink next?", the generation AI gives advice such as, "Beer and sours are OK, but sake is not." Also, when the advice providing unit asks, "What should I drink next?", the generation AI reads the menu and suggests an appropriate drink. The health check unit checks the drinker's health in cooperation with a watch device. For example, the watch device monitors the drinker's heart rate, and if an abnormality is detected, the generation AI issues a warning such as, "Your heart rate is high. Take a short break." In this way, the overdrinking prevention system according to the embodiment can maintain the drinker's health and prevent them from drinking too much.

[0053] The constitution acquisition unit can analyze the drinker's past drinking history, identify drinking patterns, and evaluate their constitution. For example, the constitution acquisition unit uses a generation AI to collect the drinker's past drinking history and analyze the amount and frequency of drinking, as well as changes in physical condition after drinking. For example, it records in a database what types and amounts of alcohol the drinker has consumed in the past, and how their physical condition was the next day, and identifies their drinking pattern. This allows for an accurate evaluation of the drinker's constitution.

[0054] The constitution acquisition unit can analyze the drinker's genetic information and evaluate genetic factors related to alcohol metabolism. The constitution acquisition unit, for example, collects the drinker's genetic information and analyzes genes related to alcohol metabolism. For example, genes such as ALDH2 and ADH1B are examined to evaluate alcohol tolerance. This makes it possible to evaluate constitution based on genetic factors.

[0055] The constitution acquisition unit can use the emotion estimation function to analyze the emotions that the drinker has toward drinking and reflect them in the constitution evaluation. The constitution acquisition unit, for example, uses the emotion estimation function to analyze the emotions that the drinker has toward drinking in real time. For example, it monitors emotional changes before and after drinking and evaluates positive or negative emotions toward drinking. This makes it possible to evaluate the constitution based on emotions.

[0056] The health status collection unit can analyze the drinker's sleep data and evaluate the quality of sleep and health status. For example, the generation AI collects the drinker's sleep data and analyzes the quality of sleep. For example, it evaluates the sleep time and the percentage of deep sleep to determine the health status. This makes it possible to evaluate the health status based on the sleep data.

[0057] The health status collection unit can measure the drinker's stress level and evaluate the impact of stress on their health status. For example, the generation AI measures the drinker's stress level in real time and evaluates their health status. For example, it analyzes heart rate variability and electrodermal activity to evaluate the stress level. This makes it possible to evaluate the health status based on the stress level.

[0058] The health condition collection unit can use the emotion estimation function to analyze the emotional state of the drinker and reflect it in the evaluation of the health condition. For example, the health condition collection unit uses the emotion estimation function to analyze the emotional state of the drinker in real time and evaluate the health condition. For example, it evaluates the impact of positive emotions on the health condition. This makes it possible to evaluate the health condition based on the emotional state.

[0059] The drinking data collection unit can analyze the drinker's drinking speed and evaluate the drinking pace. For example, the drinking data collection unit uses a generation AI to analyze the drinker's drinking speed in real time and evaluate the drinking pace. For example, it measures the time it takes the drinker to finish one drink and determines the drinking pace. This makes it possible to evaluate the drinking pace based on the drinking speed.

[0060] The drinking data collection unit can collect information about the drinking environment of the drinker and evaluate the effects of drinking. For example, the drinking data collection unit collects data about the drinking environment of the drinker and analyzes the effects of temperature and humidity on drinking. For example, it evaluates whether high room temperature speeds up alcohol absorption. This makes it possible to evaluate the effects of drinking based on the drinking environment.

[0061] The drinking data collection unit can use the emotion estimation function to analyze the emotional state of the drinker and reflect it in the record of the amount of alcohol consumed. For example, the drinking data collection unit can use the emotion estimation function to analyze the emotional state of the drinker in real time and reflect it in the record of the amount of alcohol consumed. For example, the drinking data collection unit can monitor emotional changes before and after drinking and adjust the amount of alcohol consumed. This allows the amount of alcohol consumed to be recorded based on the emotional state.

[0062] The advice providing unit can predict the impact of the next drink based on the drinker's past drinking history. For example, the advice providing unit uses a generation AI to analyze the drinker's past drinking history and predict the impact of the next drink. For example, it evaluates the impact the next drink will have on physical condition based on past data. This makes it possible to predict the impact of the next drink based on the drinker's past drinking history.

[0063] The advice providing unit can evaluate the impact of the next drink in real time based on the drinker's current physical condition data. For example, the advice providing unit uses a generation AI to analyze the drinker's current physical condition data in real time and evaluate the impact of the next drink. For example, it predicts the impact of the next drink on physical condition based on heart rate and blood pressure data. This makes it possible to evaluate the impact of the next drink in real time based on the drinker's current physical condition data.

[0064] The advice providing unit can use the emotion estimation function to analyze the emotional state of the drinker and reflect it in advice about the next drink. The advice providing unit, for example, uses the emotion estimation function to analyze the emotional state of the drinker in real time and reflect it in advice about the next drink. For example, it determines whether or not to drink the next drink based on changes in emotion before and after drinking. This allows advice about the next drink to be given based on the emotional state.

[0065] The physical condition check section can analyze the drinker's heart rate fluctuations and evaluate changes in physical condition in real time. For example, the generation AI can analyze the drinker's heart rate fluctuations in real time and evaluate changes in physical condition. For example, it can detect a sudden increase in heart rate or arrhythmia and determine abnormalities in physical condition. This makes it possible to evaluate changes in physical condition in real time based on heart rate fluctuations.

[0066] The physical condition check unit can measure the skin temperature of the drinker and evaluate the physical condition based on changes in body temperature. For example, the physical condition check unit measures the skin temperature of the drinker in real time using a watch device and evaluates the physical condition based on changes in body temperature. For example, it can detect a sudden rise or fall in body temperature and determine whether the physical condition is abnormal. This makes it possible to evaluate the physical condition based on the skin temperature.

[0067] The physical condition check unit can use the emotion estimation function to analyze the emotional state of the drinker and reflect it in the physical condition check. For example, the physical condition check unit can use the emotion estimation function to analyze the emotional state of the drinker in real time and reflect it in the physical condition check. For example, it can evaluate the impact of stress and anxiety on the physical condition. This allows the physical condition check to be performed based on the emotional state.

[0068] The physical condition check unit can collect breathing data from the drinker and evaluate the drinker's physical condition based on changes in breathing patterns. For example, the physical condition check unit uses a watch device to collect the drinker's breathing data in real time and evaluate the drinker's physical condition based on changes in breathing patterns. For example, the physical condition check unit can detect a sudden increase or decrease in breathing rate and determine whether the drinker's physical condition is abnormal. This allows the drinker's physical condition to be evaluated based on the breathing data.

[0069] The physical condition check unit can collect the drinker's blood pressure data and evaluate the drinker's physical condition based on changes in blood pressure. For example, the physical condition check unit uses a watch device to collect the drinker's blood pressure data in real time and evaluate the drinker's physical condition based on changes in blood pressure. For example, the unit can detect a sudden increase or decrease in blood pressure and determine whether the drinker's physical condition is abnormal. This allows the drinker's physical condition to be evaluated based on the blood pressure data.

[0070] The physical condition check unit can use the emotion estimation function to monitor the emotional state of the drinker in real time and issue a warning according to the emotion. The physical condition check unit can, for example, use the emotion estimation function to monitor the emotional state of the drinker in real time and issue a warning according to the emotion. For example, if the drinker is feeling a strong negative emotion, the physical condition check unit can advise the drinker to refrain from having the next drink. In this way, a warning can be issued based on the emotional state.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The excessive drinking prevention system can also include a meal data collection unit that collects the drinker's meal data and evaluates the effects of drinking. For example, the system can record the type and amount of food the drinker ate before drinking and evaluate the rate and effects of alcohol absorption. This allows for a more accurate evaluation of the effects of drinking based on the meal data.

[0073] The excessive drinking prevention system can also be equipped with an exercise data collection unit that collects the drinker's exercise data and evaluates the effects of drinking. For example, the system can record the type and intensity of exercise the drinker performed before drinking and evaluate the alcohol metabolism rate and effects. This allows for a more accurate evaluation of the effects of drinking based on the exercise data.

[0074] The excessive drinking prevention system may further include a social situation collection unit that collects information about the drinker's social situation and evaluates the impact of drinking. For example, the system may record information about the place where the drinker is drinking and the people they are with, and evaluate the impact of the social situation on drinking. This allows for a more accurate assessment of the impact of drinking based on the social situation.

[0075] The excessive drinking prevention system may further include an emotional data collection unit that analyzes the drinker's emotional state and evaluates the effects of drinking. For example, the system may record the drinker's emotions before and after drinking and evaluate the effects of emotional changes on drinking. This allows for a more accurate evaluation of the effects of drinking based on the emotional data.

[0076] The excessive drinking prevention system can also include a stress data collection unit that measures the drinker's stress level and evaluates the effects of drinking. For example, the system can record the stress level the drinker feels before and after drinking and evaluate the effects of changes in stress on drinking. This allows for a more accurate evaluation of the effects of drinking based on the stress data.

[0077] The excessive drinking prevention system may further include a sleep data collection unit that collects the drinker's sleep data and evaluates the effects of drinking. For example, the system may record how much sleep the drinker gets before and after drinking, and evaluate the effect of sleep quality on drinking. This allows for a more accurate evaluation of the effects of drinking based on the sleep data.

[0078] The excessive drinking prevention system can also include an emotion monitoring unit that monitors the drinker's emotional state in real time and evaluates the effects of drinking. For example, the system can analyze the emotions felt by the drinker while drinking in real time and evaluate the effects of emotional changes on drinking. This allows for a more accurate evaluation of the effects of drinking based on emotion monitoring.

[0079] The excessive drinking prevention system can also include a body temperature data collection unit that collects the drinker's body temperature data and evaluates the effects of drinking. For example, it can record the change in the drinker's body temperature before and after drinking and evaluate the effect of the change in body temperature on drinking. This allows for a more accurate evaluation of the effects of drinking based on the body temperature data.

[0080] The excessive drinking prevention system can also include an emotion analysis unit that analyzes the drinker's emotional state and evaluates the effects of drinking. For example, it analyzes the emotions the drinker feels before and after drinking and evaluates the effects of emotional changes on drinking. This allows for a more accurate evaluation of the effects of drinking based on emotion analysis.

[0081] The excessive drinking prevention system can also be equipped with a blood pressure data collection unit that collects the drinker's blood pressure data and evaluates the effects of drinking. For example, it can record the amount of change in blood pressure before and after drinking and evaluate the effect of blood pressure changes on drinking. This allows for a more accurate evaluation of the effects of drinking based on blood pressure data.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The constitution acquisition unit acquires the drinker's alcohol tolerance. For example, the generation AI asks the drinker questions such as, "How much alcohol does it take for you to get drunk?" and determines the drinker's constitution based on the drinker's response. The constitution acquisition unit also uses patch test and medical examination data to scientifically evaluate the drinker's alcohol tolerance. Step 2: The health status collection unit collects information about the day's health status. For example, the generation AI asks the drinker questions such as, "How are you feeling today?" and evaluates their health status based on their response. The health status collection unit also connects with the drinker's smartphone calendar app to check important appointments for the next day. Step 3: The drinking data collection unit collects the type and amount of alcohol consumed. For example, the generation AI asks the drinker questions such as, "Please tell us the type and amount of alcohol you are drinking right now," and records data based on the drinker's response. The drinking data collection unit also includes a function that allows the watch device to automatically measure alcohol intake. Step 4: The advice-providing unit gives advice when it's time to drink the next drink. For example, if the drinker asks, "Can I drink this next?", the generation AI will give advice such as, "Beer and sours are OK, but sake is not." Also, if the drinker asks, "Which drink should I drink next?", the advice-providing unit will read the menu and suggest an appropriate drink. Step 5: The health check unit checks the user's health in conjunction with the watch device. For example, the watch device monitors the user's heart rate, and if an abnormality is detected, the AI ​​generator warns the user, saying, "Your heart rate is high. Take a short break."

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A constitution acquisition unit that acquires the drinker's constitution to alcohol; a health status collection unit that collects today's health status; a drinking data collection unit that collects the types and amounts of alcohol consumed; An advice section that gives advice on when to drink your next drink; A health check unit that checks the health of the wearer in cooperation with the watch device. A system characterized by:

2. The constitution acquisition unit Analyze the drinker's drinking history, identify drinking patterns, and evaluate their constitution 2. The system of claim 1.

3. The health status collection unit: Analyzes the drinker's sleep data to evaluate sleep quality and health status 2. The system of claim 1.

4. The drinking data collection unit Analyze the drinker's drinking speed and evaluate their drinking pace 2. The system of claim 1.

5. The advice providing unit Predicting the impact of your next drink based on your drinking history 2. The system of claim 1.

6. The physical condition check unit Analyzing the drinker's heart rate fluctuations and evaluating changes in physical condition in real time 2. The system of claim 1.

7. The constitution acquisition unit Analyzing the drinker's feelings about drinking and reflecting them in the evaluation of their constitution 2. The system of claim 1.

8. The health status collection unit: Analyze the drinker's emotional state and reflect it in the assessment of their health.

2. The system of claim 1.

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