System

Abstract

An object of a system according to an embodiment is to visually display the effect of a medicine and prompt a user to take the medicine at an appropriate time.SOLUTION: A system according to an embodiment includes a personal parameter collection unit, an analysis unit, a visualization unit, and a reminder unit. The personal parameter collecting unit collects personal parameters of a user. The analysis unit analyzes the data collected by the personal parameter collection unit. The visualization unit digitizes and visually displays the effect of the drug based on the result analyzed by the analysis unit. The reminder unit notifies the user to take medicine at an appropriate time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide a means for visually understanding the effects of medicine and encouraging people to take medicine at the appropriate time, and there is room for improvement.

[0005] The system according to the embodiment aims to visually display the effects of medicine and encourage people to take medicine at the appropriate time. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal parameter collection unit, an analysis unit, a visualization unit, and a reminder unit. The personal parameter collection unit collects personal parameters of the user. The analysis unit analyzes the data collected by the personal parameter collection unit. The visualization unit quantifies and visually displays the effectiveness of the medicine based on the results of the analysis by the analysis unit. The reminder unit notifies the user to take the medicine at the appropriate time. [Effects of the Invention]

[0007] The system according to the embodiment can visually display the effects of medicine and encourage people to take medicine at the appropriate time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A medicine taking promotion system according to an embodiment of the present invention is a system that visually encourages a user to take medicine at the appropriate time. This system utilizes personal parameters to visually display the appropriate time to take medicine and quantify its effectiveness, thereby encouraging the user to take the medicine and maximizing its effectiveness. In this way, the medicine taking promotion system can visually encourage the user to take the medicine at the appropriate time and maximize its effectiveness.

[0029] A medicine-taking promotion system according to an embodiment includes a personal parameter collection unit, an analysis unit, a visualization unit, and a reminder unit. The personal parameter collection unit collects personal parameters of a user. For example, the personal parameter collection unit collects the user's age, gender, weight, medical history, current health condition, and the type and amount of medication being taken. The personal parameter collection unit can also collect the user's vital signs (heart rate, blood pressure, body temperature, etc.). The analysis unit analyzes the data collected by the personal parameter collection unit. For example, the analysis unit analyzes the data using statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using generative AI (e.g., text generation AI or multimodal generation AI). The visualization unit quantifies and visually displays the effectiveness of the medicine based on the results of the analysis by the analysis unit. For example, the visualization unit displays the effects obtained by taking the medicine in a graph or chart. The visualization unit can also similarly display the effects lost by missing the appropriate time to take the medicine. Furthermore, the visualization unit can visualize the effects of medicine using 3D models or AR technology. The reminder unit notifies users to take their medicine at the appropriate time. For example, the reminder unit notifies users that it is almost time to take their medicine via a smartphone app or wearable device. The reminder unit can also provide voice notifications in cooperation with a voice assistant or smart speaker. Furthermore, the reminder unit can provide notifications at the optimal time by linking with the user's schedule or calendar. This allows the medicine-taking promotion system according to the embodiment to encourage users to take their medicine at the appropriate time and maximize its effectiveness. For example, by taking pain-relieving medicine at the appropriate time, pain can be alleviated early, improving the quality of daily life. Furthermore, by taking fever- and cough-suppressing medicine at the appropriate time, symptoms can be addressed before they worsen. Furthermore, by taking allergic reaction-relieving medicine at the appropriate time, allergic symptoms can be alleviated, allowing users to live a more comfortable life.

[0030] The personal parameter collecting unit collects lifestyle habit data of the user and incorporates the lifestyle habit data into the analysis, thereby enabling creation of a more accurate medication schedule. The personal parameter collecting unit, for example, analyzes the user's sleep patterns and suggests optimal medication timing. For example, it adjusts the schedule so that the user takes a sleeping pill before entering deep sleep. The personal parameter collecting unit also analyzes the user's dietary content and creates a medication schedule that matches meal timings. For example, it suggests medication to be taken after meals to match meal timings. The personal parameter collecting unit also analyzes the user's exercise amount and creates a medication schedule that takes post-exercise recovery into consideration. For example, it suggests taking medication to relieve muscle pain after exercise. This allows creation of a more accurate medication schedule based on the user's lifestyle habit data.

[0031] The personal parameter collecting unit can analyze the user's genetic information and suggest the optimal type of medication and timing of administration based on genetic factors. The personal parameter collecting unit, for example, analyzes the user's genetic information and selects the type of medication based on specific genetic factors. For example, if a specific medication is genetically effective, the unit preferentially suggests that medication. The personal parameter collecting unit also optimizes the timing of medication administration based on the genetic information. For example, if a user is genetically slow to metabolize medication, the unit adjusts the schedule to lengthen the interval between medications. The personal parameter collecting unit also analyzes the user's genetic information and creates a medication schedule to reduce the risk of medication side effects. For example, the unit suggests avoiding medications that are genetically more likely to cause side effects. This makes it possible to suggest the optimal type of medication and timing of administration based on the user's genetic information.

[0032] The personal parameter collection unit collects the pet's individual parameters and can create an optimal medication schedule based on the individual parameters. The personal parameter collection unit collects the pet's individual parameters, such as its age, weight, and health condition, and creates an optimal medication schedule. For example, it suggests the amount of medication based on the pet's weight. The personal parameter collection unit also collects the pet's lifestyle data (e.g., meal timing, amount of exercise) and adjusts the medication schedule based on this data. For example, it suggests medication to be taken after meals to coincide with meal timing. The personal parameter collection unit also monitors the pet's health condition in real time and dynamically adjusts the medication schedule as needed. For example, it suggests appropriate medication when the pet becomes ill. This makes it possible to create an optimal medication schedule based on the pet's individual parameters.

[0033] The personal parameter collection unit can provide an optimal supplement intake schedule taking into account the athlete's training data and game schedule. The personal parameter collection unit, for example, analyzes the athlete's training data and suggests the optimal timing for taking supplements. For example, it suggests taking supplements that take into account post-training recovery. The personal parameter collection unit also creates a supplement intake schedule before and after a game taking into account the game schedule. For example, it suggests supplements for replenishing energy before a game. The personal parameter collection unit also suggests the optimal type and amount of supplements based on the athlete's personal parameters (e.g., weight, muscle mass). For example, it suggests the amount of protein according to muscle mass. This makes it possible to provide an optimal supplement intake schedule taking into account the athlete's training data and game schedule.

[0034] The visualization unit can display the effectiveness of the medicine in real time in conjunction with the vital signs. The visualization unit, for example, monitors the user's heart rate and displays the effectiveness of the medicine in real time. For example, it displays changes in heart rate in a graph to visually show the effectiveness of the medicine. The visualization unit also monitors the user's blood pressure and displays the effectiveness of the medicine in real time. For example, it displays changes in blood pressure in a chart to visually show the effectiveness of the medicine. The visualization unit also monitors the user's body temperature and displays the effectiveness of the medicine in real time. For example, it displays changes in body temperature in a graph to visually show the effectiveness of the medicine. This allows the effectiveness of the medicine to be displayed in real time in conjunction with the user's vital signs.

[0035] The visualization department applies the visualization of drug effectiveness to the visualization of learning effects in educational settings, making it possible to quantify and display students' learning progress. For example, the visualization department will build a system that quantifies and visually displays students' learning progress. For example, it will display test scores and assignment submission status in graphs and charts. The visualization department will also use 3D graphics and AR technology to visualize learning effects. For example, it will display the level of comprehension of learning content using 3D models and AR, allowing students to understand intuitively. The visualization department will also develop a system that monitors students' learning progress in real time and visually displays it. For example, it will display study time and progress in learning content in graphs, allowing students to understand at a glance. This makes it possible to apply the visualization of drug effectiveness to the visualization of learning effects in educational settings, making it possible to quantify and display students' learning progress.

[0036] The visualization unit can apply the visualization of drug effects to a fitness app and quantify and display the training effect. The visualization unit, for example, develops a fitness app that quantifies and visually displays the training effect. For example, it displays changes in calories burned and muscle mass in graphs and charts. The visualization unit also uses 3D graphics and AR technology to visualize the training effect. For example, it displays changes in the body after training using 3D models or AR, allowing the user to intuitively understand. The visualization unit also develops a fitness app that monitors and visually displays the training effect in real time. For example, it displays changes in heart rate and exercise volume in graphs, allowing the user to understand at a glance. This allows the visualization of drug effects to be applied to a fitness app and quantify and display the training effect.

[0037] The reminder unit can be linked to the user's schedule and calendar to send notifications at the optimal timing. For example, the reminder unit will build a system that analyzes the user's schedule and calendar and sends reminders at the optimal timing. For example, it can set a medication reminder before or after a meeting or appointment. The reminder unit can also provide reminders that match the user's lifestyle by linking with the schedule and calendar. For example, it can send reminders to coincide with the user's morning commute or evening relaxation time. The reminder unit will also develop a system that dynamically adjusts the timing of reminders in response to changes in the user's schedule. For example, it can automatically update the reminder time if the schedule changes. This allows it to be linked to the user's schedule and calendar and send notifications at the optimal timing.

[0038] The reminder unit can be linked to a voice assistant or a smart speaker to provide voice notifications. The reminder unit, for example, can be linked to a voice assistant to build a system that provides voice notifications of medication reminders. For example, the reminder unit can notify the user of the time to take medication through a smart speaker. The reminder unit can also be linked to a smart speaker to provide voice reminder notifications. For example, the reminder unit can notify the user of medication reminders by voice when the user is at home. The reminder unit can also use a voice assistant to customize the content of the reminder by voice. For example, the reminder unit can call the user's name and provide a voice message encouraging the user to take their medication. This allows the system to be linked to a voice assistant or a smart speaker to provide voice notifications.

[0039] The reminder unit can be linked to other devices in the home and used as a household chore reminder. For example, the reminder unit can be linked to devices in the home to build a system that provides household chore reminders. For example, it can be linked to a refrigerator to remind users when food expiration dates are approaching. The reminder unit can also be developed to work with a washing machine to remind users when laundry is finished. For example, it can send a notification to a smartphone when laundry is finished. The reminder unit can also be linked to multiple devices in the home to build a system that manages household chore reminders in an integrated manner. For example, it can be linked to a vacuum cleaner or air conditioner to notify users of the household chore schedule. This allows the system to be linked to other devices in the home and used as a household chore reminder.

[0040] The reminder section can be linked with a company's project management tool and used as a task reminder. For example, the reminder section can be linked with a project management tool to build a system that provides task reminders. For example, it can send task reminders before a project deadline. The reminder section can also be linked with a company's schedule management tool to provide conference and meeting reminders. For example, it can send reminders before a meeting starts. The reminder section can also be linked with a project management tool to develop a system that dynamically adjusts reminders according to the progress of a task. For example, it can send early reminders if task progress is behind schedule. This allows the system to be linked with a company's project management tool and used as a task reminder.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The medication reminder system can also analyze the user's diet and adjust medication schedules based on the nutritional balance of the diet. For example, if the user's intake of vitamins or minerals is insufficient, it can suggest taking supplements. It can also take into account interactions between specific ingredients and medications and adjust medication timing to match mealtimes. It can also suggest meals to maximize the effectiveness of medication based on the dietary content. This allows for a more accurate medication schedule that takes into account the user's dietary content.

[0043] The medication reminder system can also collect the user's exercise data and create a medication schedule that takes post-exercise recovery into account. For example, it can suggest taking medication to reduce muscle pain after exercise. It can also adjust the optimal timing for taking medication depending on the type and intensity of exercise. Furthermore, it can suggest nutritional supplements and the intake of supplements before and after exercise based on the exercise data. This allows for a more accurate medication schedule that takes into account the user's exercise data.

[0044] The medication recommendation system can further analyze the user's genetic information and suggest the optimal type of medication and the optimal timing for taking it based on genetic factors. For example, if a specific medication is genetically effective, it will preferentially suggest that medication. It can also optimize the timing of medication based on genetic information. For example, if a user has a genetically slow metabolism of medication, it can adjust the schedule to extend the interval between doses. Furthermore, it can create a medication schedule based on genetic information to reduce the risk of side effects from medication. This makes it possible to suggest the optimal type of medication and the optimal timing for taking it based on the user's genetic information.

[0045] The medication reminder system can also collect individual parameters of the pet and create an optimal medication schedule based on the individual parameters. For example, it can collect individual parameters such as the pet's age, weight, and health condition to create an optimal medication schedule. It can also collect lifestyle data of the pet (e.g., meal timing, amount of exercise) and adjust the medication schedule based on this data. It can also monitor the pet's health condition in real time and dynamically adjust the medication schedule as needed. This allows it to create an optimal medication schedule based on the pet's individual parameters.

[0046] The medication recommendation system can also provide an optimal supplement intake schedule taking into account an athlete's training data and game schedule. For example, it can analyze an athlete's training data and suggest the optimal timing for taking supplements. It can also create a supplement intake schedule before and after a game taking into account the game schedule. It can also suggest the optimal type and amount of supplements based on the athlete's personal parameters (e.g., weight, muscle mass). This makes it possible to provide an optimal supplement intake schedule taking into account an athlete's training data and game schedule.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The personal parameter collection unit collects the user's personal parameters, such as the user's age, gender, weight, medical history, current health condition, type and amount of medication being taken, and vital signs (heart rate, blood pressure, body temperature, etc.). Step 2: The analysis unit analyzes the data collected by the personal parameter collection unit, for example, using statistical analysis, machine learning algorithms, or generative AI (text generation AI or multimodal generation AI). Step 3: The visualization unit quantifies and visually displays the effectiveness of the drug based on the results of the analysis by the analysis unit. For example, it can display the effects gained by taking the drug in graphs and charts, as well as the effects lost by missing the appropriate time to take the drug. Furthermore, it can also visualize the effectiveness of the drug using 3D models and AR technology. Step 4: The reminder section notifies the user to take their medicine at the appropriate time. For example, the system can notify the user through a smartphone app or wearable device, or through voice notification in conjunction with a voice assistant or smart speaker. Furthermore, the system can also be linked to the user's schedule or calendar to notify the user at the optimal time.

[0049] (Example 2) A medicine taking promotion system according to an embodiment of the present invention is a system that visually encourages a user to take medicine at the appropriate time. This system utilizes personal parameters to visually display the appropriate time to take medicine and quantify its effectiveness, thereby encouraging the user to take the medicine and maximizing its effectiveness. In this way, the medicine taking promotion system can visually encourage the user to take the medicine at the appropriate time and maximize its effectiveness.

[0050] A medicine-taking promotion system according to an embodiment includes a personal parameter collection unit, an analysis unit, a visualization unit, and a reminder unit. The personal parameter collection unit collects personal parameters of a user. For example, the personal parameter collection unit collects the user's age, gender, weight, medical history, current health condition, and the type and amount of medication being taken. The personal parameter collection unit can also collect the user's vital signs (heart rate, blood pressure, body temperature, etc.). The analysis unit analyzes the data collected by the personal parameter collection unit. For example, the analysis unit analyzes the data using statistical analysis. The analysis unit can also analyze the data using a machine learning algorithm. The analysis unit can also analyze the data using generative AI (e.g., text generation AI or multimodal generation AI). The visualization unit quantifies and visually displays the effectiveness of the medicine based on the results of the analysis by the analysis unit. For example, the visualization unit displays the effects obtained by taking the medicine in a graph or chart. The visualization unit can also similarly display the effects lost by missing the appropriate time to take the medicine. Furthermore, the visualization unit can visualize the effects of medicine using 3D models or AR technology. The reminder unit notifies users to take their medicine at the appropriate time. For example, the reminder unit notifies users that it is almost time to take their medicine via a smartphone app or wearable device. The reminder unit can also provide voice notifications in cooperation with a voice assistant or smart speaker. Furthermore, the reminder unit can provide notifications at the optimal time by linking with the user's schedule or calendar. This allows the medicine-taking promotion system according to the embodiment to encourage users to take their medicine at the appropriate time and maximize its effectiveness. For example, by taking pain-relieving medicine at the appropriate time, pain can be alleviated early, improving the quality of daily life. Furthermore, by taking fever- and cough-suppressing medicine at the appropriate time, symptoms can be addressed before they worsen. Furthermore, by taking allergic reaction-relieving medicine at the appropriate time, allergic symptoms can be alleviated, allowing users to live a more comfortable life.

[0051] The personal parameter collecting unit can estimate the user's emotional state in real time and dynamically adjust the medication schedule based on the emotional state. The personal parameter collecting unit, for example, analyzes the user's facial expressions and voice to estimate the user's emotional state in real time. For example, the personal parameter collecting unit analyzes the user's emotions using a camera or microphone and suggests taking medication with a relaxing effect when stress or anxiety increases. The personal parameter collecting unit also dynamically adjusts the medication schedule based on the user's emotional state. For example, if the user is feeling stressed, the schedule is changed to prioritize taking medication with a stress-reducing effect. The personal parameter collecting unit also suggests the timing of medication taking based on the user's emotions based on the estimation result of the emotional state. For example, the personal parameter collecting unit suggests taking medication with a relaxing effect during a time period when the user is relaxed. This makes it possible to adjust the medication schedule based on the user's emotional state.

[0052] The personal parameter collecting unit collects lifestyle habit data of the user and incorporates the lifestyle habit data into the analysis, thereby enabling creation of a more accurate medication schedule. The personal parameter collecting unit, for example, analyzes the user's sleep patterns and suggests optimal medication timing. For example, it adjusts the schedule so that the user takes a sleeping pill before entering deep sleep. The personal parameter collecting unit also analyzes the user's dietary content and creates a medication schedule that matches meal timings. For example, it suggests medication to be taken after meals to match meal timings. The personal parameter collecting unit also analyzes the user's exercise amount and creates a medication schedule that takes post-exercise recovery into consideration. For example, it suggests taking medication to relieve muscle pain after exercise. This allows creation of a more accurate medication schedule based on the user's lifestyle habit data.

[0053] The personal parameter collecting unit can analyze the user's genetic information and suggest the optimal type of medication and timing of administration based on genetic factors. The personal parameter collecting unit, for example, analyzes the user's genetic information and selects the type of medication based on specific genetic factors. For example, if a specific medication is genetically effective, the unit preferentially suggests that medication. The personal parameter collecting unit also optimizes the timing of medication administration based on the genetic information. For example, if a user is genetically slow to metabolize medication, the unit adjusts the schedule to lengthen the interval between medications. The personal parameter collecting unit also analyzes the user's genetic information and creates a medication schedule to reduce the risk of medication side effects. For example, the unit suggests avoiding medications that are genetically more likely to cause side effects. This makes it possible to suggest the optimal type of medication and timing of administration based on the user's genetic information.

[0054] The personal parameter collection unit collects the pet's individual parameters and can create an optimal medication schedule based on the individual parameters. The personal parameter collection unit collects the pet's individual parameters, such as its age, weight, and health condition, and creates an optimal medication schedule. For example, it suggests the amount of medication based on the pet's weight. The personal parameter collection unit also collects the pet's lifestyle data (e.g., meal timing, amount of exercise) and adjusts the medication schedule based on this data. For example, it suggests medication to be taken after meals to coincide with meal timing. The personal parameter collection unit also monitors the pet's health condition in real time and dynamically adjusts the medication schedule as needed. For example, it suggests appropriate medication when the pet becomes ill. This makes it possible to create an optimal medication schedule based on the pet's individual parameters.

[0055] The personal parameter collection unit can provide an optimal supplement intake schedule taking into account the athlete's training data and game schedule. The personal parameter collection unit, for example, analyzes the athlete's training data and suggests the optimal timing for taking supplements. For example, it suggests taking supplements that take into account post-training recovery. The personal parameter collection unit also creates a supplement intake schedule before and after a game taking into account the game schedule. For example, it suggests supplements for replenishing energy before a game. The personal parameter collection unit also suggests the optimal type and amount of supplements based on the athlete's personal parameters (e.g., weight, muscle mass). For example, it suggests the amount of protein according to muscle mass. This makes it possible to provide an optimal supplement intake schedule taking into account the athlete's training data and game schedule.

[0056] The personal parameter collection unit can use the emotion estimation function to suggest relaxation methods and stress management methods according to the user's emotional state. The personal parameter collection unit, for example, analyzes the user's emotional state in real time and suggests relaxation methods. For example, it may suggest deep breathing or meditation when stress levels rise. The personal parameter collection unit also customizes the user's stress management methods based on the emotion estimation data. For example, it may suggest music or aromas that help the user relax. The personal parameter collection unit also monitors the user's emotional state and notifies the user of relaxation methods when stress levels rise. For example, it may suggest relaxation methods through a smartphone app. In this way, the user's stress can be reduced by suggesting relaxation methods and stress management methods according to the user's emotional state.

[0057] The visualization unit can estimate the emotional state, quantify the effectiveness of the medicine based on the emotional state, and visually display it. The visualization unit, for example, analyzes the user's emotional state and quantifies the effectiveness of the medicine. For example, it displays the change in stress level when taking a medicine with a stress-reducing effect as a numerical value. The visualization unit also visually displays the effectiveness of the medicine based on the emotion estimation data. For example, it displays the change in the emotion score in a graph or chart so that the user can understand it at a glance. The visualization unit also monitors the user's emotional state in real time and dynamically quantifies the effectiveness of the medicine. For example, it displays the change in the emotion score after taking the medicine in real time. In this way, the effectiveness of the medicine can be quantified based on the user's emotional state and visually displayed.

[0058] The visualization unit can display the effectiveness of the medicine in real time in conjunction with the vital signs. The visualization unit, for example, monitors the user's heart rate and displays the effectiveness of the medicine in real time. For example, it displays changes in heart rate in a graph to visually show the effectiveness of the medicine. The visualization unit also monitors the user's blood pressure and displays the effectiveness of the medicine in real time. For example, it displays changes in blood pressure in a chart to visually show the effectiveness of the medicine. The visualization unit also monitors the user's body temperature and displays the effectiveness of the medicine in real time. For example, it displays changes in body temperature in a graph to visually show the effectiveness of the medicine. This allows the effectiveness of the medicine to be displayed in real time in conjunction with the user's vital signs.

[0059] The visualization department applies the visualization of drug effectiveness to the visualization of learning effects in educational settings, making it possible to quantify and display students' learning progress. For example, the visualization department will build a system that quantifies and visually displays students' learning progress. For example, it will display test scores and assignment submission status in graphs and charts. The visualization department will also use 3D graphics and AR technology to visualize learning effects. For example, it will display the level of comprehension of learning content using 3D models and AR, allowing students to understand intuitively. The visualization department will also develop a system that monitors students' learning progress in real time and visually displays it. For example, it will display study time and progress in learning content in graphs, allowing students to understand at a glance. This makes it possible to apply the visualization of drug effectiveness to the visualization of learning effects in educational settings, making it possible to quantify and display students' learning progress.

[0060] The visualization unit can apply the visualization of drug effects to a fitness app and quantify and display the training effect. The visualization unit, for example, develops a fitness app that quantifies and visually displays the training effect. For example, it displays changes in calories burned and muscle mass in graphs and charts. The visualization unit also uses 3D graphics and AR technology to visualize the training effect. For example, it displays changes in the body after training using 3D models or AR, allowing the user to intuitively understand. The visualization unit also develops a fitness app that monitors and visually displays the training effect in real time. For example, it displays changes in heart rate and exercise volume in graphs, allowing the user to understand at a glance. This allows the visualization of drug effects to be applied to a fitness app and quantify and display the training effect.

[0061] The visualization unit can use the emotion estimation function to quantify and visually display the effectiveness of entertainment content according to the user's emotional state. The visualization unit, for example, analyzes the user's emotional state and quantifies the effectiveness of the entertainment content. For example, it displays a graph showing changes in emotion scores after watching a movie or listening to music. The visualization unit also visually displays the effectiveness of the entertainment content based on the emotion estimation data. For example, it displays changes in emotion scores in a chart so that the user can understand at a glance. The visualization unit also monitors the user's emotional state in real time and dynamically quantifies the effectiveness of the entertainment content. For example, it displays changes in emotion scores during viewing in real time. In this way, the emotion estimation function can be used to quantify and visually display the effectiveness of entertainment content according to the user's emotional state.

[0062] The reminder unit can estimate the user's emotional state and dynamically adjust the timing and content of reminders based on the emotional state. For example, the reminder unit analyzes the user's emotional state in real time and adjusts the timing of reminders. For example, if the user is feeling stressed, an early reminder for a relaxing medication is sent. The reminder unit also customizes the content of reminders based on the emotion estimation data. For example, if the user is feeling anxious, a message that provides reassurance is included in the reminder. The reminder unit also monitors the user's emotional state and develops a system that dynamically adjusts the timing and content of reminders. For example, the reminder is updated every time the user's emotions change. This makes it possible to dynamically adjust the timing and content of reminders based on the user's emotional state.

[0063] The reminder unit can be linked to the user's schedule and calendar to send notifications at the optimal timing. For example, the reminder unit will build a system that analyzes the user's schedule and calendar and sends reminders at the optimal timing. For example, it can set a medication reminder before or after a meeting or appointment. The reminder unit can also provide reminders that match the user's lifestyle by linking with the schedule and calendar. For example, it can send reminders to coincide with the user's morning commute or evening relaxation time. The reminder unit will also develop a system that dynamically adjusts the timing of reminders in response to changes in the user's schedule. For example, it can automatically update the reminder time if the schedule changes. This allows it to be linked to the user's schedule and calendar and send notifications at the optimal timing.

[0064] The reminder unit can be linked to a voice assistant or a smart speaker to provide voice notifications. The reminder unit, for example, can be linked to a voice assistant to build a system that provides voice notifications of medication reminders. For example, the reminder unit can notify the user of the time to take medication through a smart speaker. The reminder unit can also be linked to a smart speaker to provide voice reminder notifications. For example, the reminder unit can notify the user of medication reminders by voice when the user is at home. The reminder unit can also use a voice assistant to customize the content of the reminder by voice. For example, the reminder unit can call the user's name and provide a voice message encouraging the user to take their medication. This allows the system to be linked to a voice assistant or a smart speaker to provide voice notifications.

[0065] The reminder unit can be linked to other devices in the home and used as a household chore reminder. For example, the reminder unit can be linked to devices in the home to build a system that provides household chore reminders. For example, it can be linked to a refrigerator to remind users when food expiration dates are approaching. The reminder unit can also be developed to work with a washing machine to remind users when laundry is finished. For example, it can send a notification to a smartphone when laundry is finished. The reminder unit can also be linked to multiple devices in the home to build a system that manages household chore reminders in an integrated manner. For example, it can be linked to a vacuum cleaner or air conditioner to notify users of the household chore schedule. This allows the system to be linked to other devices in the home and used as a household chore reminder.

[0066] The reminder section can be linked with a company's project management tool and used as a task reminder. For example, the reminder section can be linked with a project management tool to build a system that provides task reminders. For example, it can send task reminders before a project deadline. The reminder section can also be linked with a company's schedule management tool to provide conference and meeting reminders. For example, it can send reminders before a meeting starts. The reminder section can also be linked with a project management tool to develop a system that dynamically adjusts reminders according to the progress of a task. For example, it can send early reminders if task progress is behind schedule. This allows the system to be linked with a company's project management tool and used as a task reminder.

[0067] The reminder unit uses the emotion estimation function to customize the content of the reminder according to the user's emotional state and provide notifications that elicit positive emotions. The reminder unit, for example, analyzes the user's emotional state and customizes the content of the reminder. For example, if the user is feeling anxious, the reminder unit includes a message that gives a sense of security in the reminder. The reminder unit also provides reminders that elicit positive emotions based on the emotion estimation data. For example, the reminder unit notifies the user of an encouraging message that helps the user relax. The reminder unit also develops a system that monitors the user's emotional state in real time and dynamically adjusts the content of the reminder. For example, the reminder is updated every time the user's emotions change. This makes it possible to use the emotion estimation function to customize the content of the reminder according to the user's emotional state and provide notifications that elicit positive emotions.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The medication reminder system can also analyze the user's diet and adjust medication schedules based on the nutritional balance of the diet. For example, if the user's intake of vitamins or minerals is insufficient, it can suggest taking supplements. It can also take into account interactions between specific ingredients and medications and adjust medication timing to match mealtimes. It can also suggest meals to maximize the effectiveness of medication based on the dietary content. This allows for a more accurate medication schedule that takes into account the user's dietary content.

[0070] The medicine-taking prompting system can further estimate the user's emotional state and adjust the timing of taking medicine based on the estimated emotion. For example, if the user is feeling stressed, the system can change the schedule to prioritize taking medicine with stress-reducing effects. It can also suggest taking medicine with relaxing effects during times when the user is relaxed. Furthermore, it can suggest the timing of taking medicine according to the user's emotion based on the estimated emotional state. This makes it possible to provide an optimal medicine-taking schedule according to the user's emotional state.

[0071] The medication reminder system can also collect the user's exercise data and create a medication schedule that takes post-exercise recovery into account. For example, it can suggest taking medication to reduce muscle pain after exercise. It can also adjust the optimal timing for taking medication depending on the type and intensity of exercise. Furthermore, it can suggest nutritional supplements and the intake of supplements before and after exercise based on the exercise data. This allows for a more accurate medication schedule that takes into account the user's exercise data.

[0072] The medication recommendation system can further analyze the user's genetic information and suggest the optimal type of medication and the optimal timing for taking it based on genetic factors. For example, if a specific medication is genetically effective, it will preferentially suggest that medication. It can also optimize the timing of medication based on genetic information. For example, if a user has a genetically slow metabolism of medication, it can adjust the schedule to extend the interval between doses. Furthermore, it can create a medication schedule based on genetic information to reduce the risk of side effects from medication. This makes it possible to suggest the optimal type of medication and the optimal timing for taking it based on the user's genetic information.

[0073] The medication reminder system can also collect individual parameters of the pet and create an optimal medication schedule based on the individual parameters. For example, it can collect individual parameters such as the pet's age, weight, and health condition to create an optimal medication schedule. It can also collect lifestyle data of the pet (e.g., meal timing, amount of exercise) and adjust the medication schedule based on this data. It can also monitor the pet's health condition in real time and dynamically adjust the medication schedule as needed. This allows it to create an optimal medication schedule based on the pet's individual parameters.

[0074] The medication recommendation system can also provide an optimal supplement intake schedule taking into account an athlete's training data and game schedule. For example, it can analyze an athlete's training data and suggest the optimal timing for taking supplements. It can also create a supplement intake schedule before and after a game taking into account the game schedule. It can also suggest the optimal type and amount of supplements based on the athlete's personal parameters (e.g., weight, muscle mass). This makes it possible to provide an optimal supplement intake schedule taking into account an athlete's training data and game schedule.

[0075] The medicine-taking promotion system can also use its emotion estimation function to suggest relaxation methods and stress management based on the user's emotional state. For example, it can analyze the user's emotional state in real time and suggest relaxation methods. It can also customize the user's stress management methods based on the emotion estimation data. It can also monitor the user's emotional state and notify them of relaxation methods when stress levels rise. This allows the system to reduce the user's stress by suggesting relaxation methods and stress management based on the user's emotional state.

[0076] The medicine taking promotion system can further use an emotion estimation function to quantify and visually display the effectiveness of medicine based on the user's emotional state. For example, the system can analyze the user's emotional state and quantify the effectiveness of medicine. It can also visually display the effectiveness of medicine based on emotion estimation data. Furthermore, it can monitor the user's emotional state in real time and dynamically quantify the effectiveness of medicine. This allows the effectiveness of medicine to be quantified and visually displayed based on the user's emotional state.

[0077] The medicine taking promotion system can further use an emotion estimation function to quantify and visually display the effectiveness of entertainment content according to the user's emotional state. For example, the system can analyze the user's emotional state and quantify the effectiveness of the entertainment content. The system can also visually display the effectiveness of the entertainment content based on the emotion estimation data. Furthermore, the system can monitor the user's emotional state in real time and dynamically quantify the effectiveness of the entertainment content. This allows the emotion estimation function to quantify and visually display the effectiveness of entertainment content according to the user's emotional state.

[0078] The medication reminder system can further use emotion estimation to dynamically adjust the timing and content of reminders based on the user's emotional state. For example, the system can analyze the user's emotional state in real time and adjust the timing of reminders. It can also customize the content of reminders based on emotion estimation data. Furthermore, it is possible to develop a system that monitors the user's emotional state and dynamically adjusts the timing and content of reminders. This allows the timing and content of reminders to be dynamically adjusted based on the user's emotional state.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The personal parameter collection unit collects the user's personal parameters, such as the user's age, gender, weight, medical history, current health condition, type and amount of medication being taken, and vital signs (heart rate, blood pressure, body temperature, etc.). Step 2: The analysis unit analyzes the data collected by the personal parameter collection unit, for example, using statistical analysis, machine learning algorithms, or generative AI (text generation AI or multimodal generation AI). Step 3: The visualization unit quantifies and visually displays the effectiveness of the drug based on the results of the analysis by the analysis unit. For example, it can display the effects gained by taking the drug in graphs and charts, as well as the effects lost by missing the appropriate time to take the drug. Furthermore, it can also visualize the effectiveness of the drug using 3D models and AR technology. Step 4: The reminder section notifies the user to take their medicine at the appropriate time. For example, the system can notify the user through a smartphone app or wearable device, or through voice notification in conjunction with a voice assistant or smart speaker. Furthermore, the system can also be linked to the user's schedule or calendar to notify the user at the optimal time.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0094] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 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 personal parameter collection unit that collects personal parameters of a user; an analysis unit that analyzes the data collected by the personal parameter collection unit; a visualization unit that quantifies and visually displays the efficacy of a drug based on the results of the analysis by the analysis unit; A reminder unit that notifies the user to take medicine at the appropriate time. A system characterized by:

2. The personal parameter collection unit Estimating the user's emotional state in real time and dynamically adjusting the medication schedule based on the emotional state.

2. The system of claim 1.

3. The personal parameter collection unit By collecting lifestyle data from users and incorporating it into analysis, a more accurate medication schedule can be created.

2. The system of claim 1.

4. The personal parameter collection unit Analyzes the user's genetic information and suggests the optimal type and timing of the drug based on genetic factors 2. The system of claim 1.

5. The personal parameter collection unit Collecting individual parameters of the pet and creating an optimal medication schedule based on the individual parameters 2. The system of claim 1.

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