system

Abstract

The system according to this embodiment aims to effectively support cognitive behavioral therapy for patients with social eating anxiety. [Solution] The system according to the embodiment comprises an acquisition unit, a recording unit, a diagnostic unit, and a report creation unit. The acquisition unit acquires the schedule of the meal. The recording unit records the meal based on the schedule acquired by the acquisition unit. The diagnostic unit diagnoses the meal based on the data recorded by the recording unit. The report creation unit creates a report based on the diagnostic results obtained by the diagnostic unit.

Description

Technical Field

[0006] , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, a system that effectively supports cognitive behavioral therapy for patients with social phobia has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively support cognitive behavioral therapy for patients with social phobia.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, a recording unit, a diagnostic unit, and a report generation unit. The acquisition unit acquires the schedule of meals. The recording unit records the meals based on the schedule acquired by the acquisition unit. The diagnostic unit diagnoses the meals based on the data recorded by the recording unit. The report generation unit creates a report based on the diagnostic results obtained by the diagnostic unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively support cognitive behavioral therapy for patients with social eating anxiety. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dining management, diagnosis, and reporting system according to an embodiment of the present invention is a system that supports cognitive behavioral therapy for patients with social dining phobia. This system works in conjunction with a calendar application to database records of dining out in daily life, create reports, and perform dining out diagnoses, thereby supporting cognitive behavioral therapy. First, the user enters a dining out appointment into the calendar application. Next, the system automatically retrieves the appointment and begins recording the dining out. After the dining out is finished, the user enters details of the dining out into the system. For example, the location of the dining out, participants, meal contents, and the level of stress felt. The system performs a diagnosis of the dining out based on the entered data. The diagnosis results evaluate the user's stress level and the progress of the meal, and provide advice for the next dining out. For example, if a particular location or participant causes stress, this information can be used to plan the next dining out. Furthermore, the system databases the dining out records and creates reports periodically. The reports include the user's progress in dining out, changes in stress levels, and areas for improvement. These reports are used by the user themselves and medical professionals to understand the progress of their treatment. This system allows patients with social eating anxiety to receive cognitive behavioral therapy in their daily lives, which is expected to enhance the effectiveness of their treatment. Thus, the social eating management, diagnosis, and reporting system can support cognitive behavioral therapy for patients with social eating anxiety and improve the effectiveness of their treatment.

[0029] The meal management, diagnosis, and reporting system according to this embodiment comprises an acquisition unit, a recording unit, a diagnosis unit, and a report creation unit. The acquisition unit acquires meal plans entered by the user in a calendar application. For example, the acquisition unit can automatically acquire the user's meal plans using the calendar application's API. The acquisition unit can also acquire meal plans manually entered by the user. The recording unit records the meal based on the meal plans acquired by the acquisition unit. For example, the recording unit records details such as the location of the meal, participants, meal contents, and perceived stress level. The recording unit saves the information entered by the user in a database, which can be used later for diagnosis and report creation. The diagnosis unit diagnoses the meal based on the data recorded by the recording unit. For example, the diagnosis unit evaluates the user's stress level and the progress of the meal, and provides advice for the next meal. The diagnosis unit can analyze the data using AI and provide appropriate advice to the user. The report creation unit creates a report based on the diagnosis results obtained by the diagnosis unit. For example, the reporting unit creates reports that include the user's progress during meals, changes in stress levels, and areas for improvement. The reporting unit can create reports regularly and provide them to users and medical professionals. In this way, the meal management, diagnosis, and reporting system according to this embodiment can support cognitive behavioral therapy for patients with social eating phobia and enhance the effectiveness of treatment.

[0030] The recording unit can record the specific details of a meal entered by the user. These details may include, but are not limited to, the purpose of the meal, the menu, and the job titles of the participants. The recording unit can, for example, save the user-entered meal details to a database. For example, the recording unit can record the purpose of the meal, which may include business meals, private meals, or family meals. The recording unit can record the menu, which may include appetizers, main courses, and desserts. The recording unit can also record the job titles of the participants, which may include superiors, colleagues, and friends. This allows for detailed analysis based on the recorded meal details. Some or all of the above processing in the recording unit may be performed using AI, or not. For example, the recording unit can input the user-entered meal details into an AI, which can then automatically organize the data and save it to a database.

[0031] The diagnostic unit can evaluate the user's stress level and eating progress based on recorded data. Stress level evaluation criteria and specific measurement methods include, but are not limited to, questionnaires and biometric data. For example, the diagnostic unit can conduct a questionnaire to evaluate the stress level entered by the user. The questionnaire may include questions about the degree and cause of the stress the user experienced. The diagnostic unit can evaluate stress levels using biometric data. Biometric data may include heart rate, skin electrical activity, and respiratory rate. The diagnostic unit can evaluate the eating progress. Evaluation criteria and specific measurement methods for eating progress include, for example, meal times and meal amounts. For example, the diagnostic unit can evaluate the meal times entered by the user. Meal times include the start time and end time of the meal. The diagnostic unit can evaluate the meal amount. Meal amount includes the amount eaten and the amount left over. By evaluating the user's stress level and eating progress, an appropriate diagnosis can be made. Some or all of the above-described processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's stress level and dietary progress into the AI, which can then automatically analyze the data and output evaluation results.

[0032] The report generation unit can provide advice to the user based on the diagnostic results. The specific content and format of the advice may include, but are not limited to, suggestions for action and psychological support. For example, the report generation unit can suggest actions for the user regarding their next meal. These suggestions may include selecting a venue, participants, and menu. The report generation unit can also provide psychological support to the user. This support may include suggestions for relaxation techniques and stress management methods. This allows the user to plan for their next meal by providing advice based on the diagnostic results. Some or all of the above processes in the report generation unit may be performed using, for example, AI, or not. For example, the report generation unit can input the diagnostic results into an AI, which can then automatically generate and provide advice to the user.

[0033] The meal management, diagnosis, and reporting system includes a security section to protect data security. The security section provides specific methods and standards for ensuring data security. For example, the security section can use methods such as data encryption, access control, and data backup. Data encryption includes encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). Access control includes user authentication, permission management, and monitoring of login history. Data backup includes regular data backups, encryption of backup data, and secure storage of backup data. This ensures data security and protects user privacy. Some or all of the above processes in the security section may be performed using AI, for example, or not. For example, the security section can entrust data encryption and access control to AI, which can automatically implement security measures.

[0034] The meal management, diagnosis, and reporting system can automatically retrieve meal schedules by linking with a calendar application. Specific examples of calendar applications and integration methods include, but are not limited to, Google® Calendar and Outlook® Calendar. For example, the system can automatically retrieve a user's meal schedule using the Google Calendar API. Similarly, the system can automatically retrieve a user's meal schedule using the Outlook Calendar API. This allows for the automatic retrieval of meal schedules by linking with a calendar application. Some or all of the above-described processes in the system may be performed using AI, or not. For example, the system can input the calendar application API into an AI, which can then automatically retrieve the meal schedule.

[0035] The acquisition unit can analyze the user's past dining history and select the optimal acquisition method. Specific details and analysis methods of past dining history include, but are not limited to, the date, time, location, and participants of past meals. For example, the acquisition unit can select the optimal acquisition method based on patterns of meals the user has preferred to attend in the past. The acquisition unit can adjust the acquisition method based on patterns of meals the user has avoided in the past. The acquisition unit can select the least stressful acquisition method from the user's past dining history. For example, the acquisition unit analyzes the date, time, and location of meals the user has attended in the past to identify patterns of meals the user can participate in comfortably. The acquisition unit analyzes patterns of meals the user has avoided in the past to select patterns of meals the user is less likely to experience stress. The acquisition unit selects the least stressful acquisition method based on the user's past dining history. Thus, by analyzing the user's past dining history, the optimal acquisition method can be selected. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit inputs the user's past dining history data into a generating AI, which then automatically analyzes the data and selects the optimal acquisition method.

[0036] The acquisition unit can filter meal plans based on the user's current lifestyle and areas of interest when acquiring them. Specific details and evaluation methods for lifestyle include, but are not limited to, work schedules and family circumstances. Specific details and evaluation methods for areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is currently busy, the acquisition unit can filter out low-priority meal plans. If the user is interested in a particular area of ​​interest, the acquisition unit can prioritize acquiring meal plans related to that area. The acquisition unit can filter out appropriate meal plans based on the user's lifestyle. For example, the acquisition unit analyzes the user's work schedule and filters out low-priority meal plans. The acquisition unit prioritizes acquiring relevant meal plans based on the user's hobbies and topics of interest. The acquisition unit considers the user's family circumstances and filters out appropriate meal plans. This allows for the acquisition of more appropriate meal plans by filtering them based on the user's lifestyle and areas of interest. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then automatically analyze the data and filter out meal plans.

[0037] The acquisition unit can prioritize the acquisition of highly relevant dining appointments based on the user's geographical location information when acquiring dining appointments. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. For example, the acquisition unit can prioritize the acquisition of dining appointments in locations close to the user's current location. If the user is in a specific region, the acquisition unit can prioritize the acquisition of dining appointments in that region. If the user is on the move, the acquisition unit can prioritize the acquisition of dining appointments at their destination. For example, the acquisition unit acquires the user's current location from GPS data and prioritizes the acquisition of nearby dining appointments. If the user is in a specific region, the acquisition unit prioritizes the acquisition of dining appointments in that region. If the user is on the move, the acquisition unit prioritizes the acquisition of dining appointments at their destination. This allows for the priority acquisition of highly relevant dining appointments by considering the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit inputs the user's geographical location data into the generating AI, which automatically analyzes the data and prioritizes the acquisition of highly relevant meal plans.

[0038] The acquisition unit can analyze the user's social media activity when acquiring dinner plans and acquire related plans. Specific examples of social media activity and analysis methods include, but are not limited to, posts and the number of likes. The acquisition unit can, for example, prioritize acquiring dinner plans mentioned by the user on social media. The acquisition unit can prioritize acquiring dinner plans that the user's social media friends will participate in. The acquisition unit can acquire dinner plans that are likely to be of interest based on the user's social media activity. For example, the acquisition unit automatically acquires dinner plans mentioned by the user on social media. The acquisition unit prioritizes acquiring dinner plans that the user's social media friends will participate in. The acquisition unit acquires dinner plans that are likely to be of interest based on the user's social media activity. This allows the acquisition unit to acquire relevant dinner plans by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not. For example, the acquisition unit can input the user's social media activity data into a generating AI, which can automatically analyze the data and acquire relevant dinner plans.

[0039] The recording unit can provide input assistance when the user enters details of a meal by referring to the user's past meal data. Specific methods and criteria for input assistance include, but are not limited to, autocomplete and templates. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit can prioritize displaying frequently used input items from the user's past meal data. The recording unit can make suggestions to reduce input effort based on the user's past meal data. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit prioritizes displaying frequently used input items from the user's past meal data. The recording unit makes suggestions to reduce input effort based on the user's past meal data. This allows the recording unit to provide input assistance and reduce input effort by referring to the user's past meal data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past dining data into a generating AI, which can then automatically analyze the data and provide input assistance.

[0040] The recording unit can customize input fields based on the user's current lifestyle when entering details about a meal. Specific examples of input fields and customization methods include, but are not limited to, required and optional fields. For example, the recording unit can minimize input fields when the user is busy. It can provide detailed input fields when the user is relaxed. The recording unit can dynamically change input fields according to the user's lifestyle. This allows for more appropriate input by customizing input fields based on the user's lifestyle. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input user lifestyle data into a generating AI, which can automatically analyze the data and customize the input fields.

[0041] The recording unit can optimize input fields when users enter details of a meal, taking into account their geographical location. Specific methods for obtaining and using geographical location information include, but are not limited to, GPS data and location services. For example, if the meal is near the user's current location, the recording unit can prioritize displaying input fields related to that location. If the user is in a specific region, the recording unit can prioritize displaying input fields related to that region. If the user is on the move, the recording unit can prioritize displaying input fields related to their destination. For example, the recording unit obtains the user's current location from GPS data and prioritizes displaying input fields related to nearby meals. If the user is in a specific region, the recording unit prioritizes displaying input fields related to that region. If the user is on the move, the recording unit prioritizes displaying input fields related to their destination. This allows for the optimization of input fields by taking into account the user's geographical location. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's geographical location data into a generating AI, which can then automatically analyze the data and optimize the input fields.

[0042] The recording unit can analyze the user's social media activity and automatically input relevant information when entering details of a meal. Specific examples of social media activity and analysis methods include, but are not limited to, posts and the number of likes. For example, the recording unit can automatically input details of a meal mentioned by the user on social media. The recording unit can automatically input details of a meal attended by the user's social media friends. The recording unit can automatically input relevant information from the user's social media activity. For example, the recording unit automatically inputs details of a meal mentioned by the user on social media. The recording unit automatically inputs details of a meal attended by the user's social media friends. The recording unit automatically inputs relevant information from the user's social media activity. This allows for the automatic input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can input the user's social media activity data into a generating AI, which can automatically analyze the data and automatically input relevant information.

[0043] The diagnostic unit can improve the accuracy of its diagnosis based on the interrelationships of the meals during the diagnosis process. Specific details and evaluation methods of the interrelationships of the meals include, but are not limited to, the relationships between participants and the purpose of the meal. For example, the diagnostic unit can assess stress levels by considering the relationships between participants in the meal. The diagnostic unit can improve the accuracy of its diagnosis by considering the combination of the meal location and participants. The diagnostic unit can adjust the diagnostic results by considering the frequency of the meals and the relationships between participants. For example, the diagnostic unit analyzes the relationships between participants in the meals and assesses stress levels. The diagnostic unit analyzes the combination of the meal location and participants to improve the accuracy of the diagnosis. The diagnostic unit analyzes the frequency of the meals and the relationships between participants to adjust the diagnostic results. This improves the accuracy of the diagnosis by considering the interrelationships of the meals. Some or all of the above processing in the diagnostic unit may be performed using, for example, AI, or not. For example, the diagnostic unit can input the interrelationship data of the meals into a generating AI, which can then automatically analyze the data and improve the accuracy of the diagnosis.

[0044] The diagnostic unit can perform a diagnosis based on the user's attribute information. Specific examples of attribute information include, but are not limited to, age, gender, and occupation. The diagnostic unit can adjust diagnostic criteria, for example, by considering the user's age and gender. The diagnostic unit can adjust diagnostic results, considering the user's occupation and lifestyle. The diagnostic unit can improve the accuracy of the diagnosis by considering the user's health condition. For example, the diagnostic unit analyzes the user's age and gender and adjusts the diagnostic criteria. The diagnostic unit analyzes the user's occupation and lifestyle and adjusts the diagnostic results. The diagnostic unit analyzes the user's health condition and improves the accuracy of the diagnosis. This allows for a more appropriate diagnosis by considering the user's attribute information. Some or all of the above processes in the diagnostic unit may be performed using, for example, AI, or without AI. For example, the diagnostic unit can input user attribute information data into a generating AI, which can then automatically analyze the data and perform a diagnosis.

[0045] The diagnostic unit can perform a diagnosis based on the geographical distribution of meals during the diagnosis process. Specific details and evaluation methods of the geographical distribution include, but are not limited to, the location of the meal and regional characteristics. For example, if the location of the meal is a source of stress for the user, the diagnostic unit can reflect this information in the diagnosis. The diagnostic unit can evaluate stress levels considering the geographical distribution of meals. The diagnostic unit can adjust the diagnosis results considering the distance between the meal location and the user's place of residence. For example, if the location of the meal is a source of stress for the user, the diagnostic unit reflects this information in the diagnosis. The diagnostic unit evaluates stress levels considering the geographical distribution of meals. The diagnostic unit adjusts the diagnosis results considering the distance between the meal location and the user's place of residence. This allows for a more appropriate diagnosis by considering the geographical distribution of meals. Some or all of the above processing in the diagnostic unit may be performed using, for example, AI, or without AI. For example, the diagnostic unit can input geographical distribution data of meals into a generating AI, which can automatically analyze the data and perform a diagnosis.

[0046] The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature during the diagnosis process. Specific examples of relevant literature and methods of reference include, but are not limited to, academic papers and specialized books. The diagnostic unit can update diagnostic criteria by referring to the latest research findings. The diagnostic unit can improve the reliability of diagnostic results based on relevant literature. The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature. For example, the diagnostic unit updates diagnostic criteria by referring to the latest research findings. The diagnostic unit improves the reliability of diagnostic results based on relevant literature. The diagnostic unit improves the accuracy of its diagnosis by referring to relevant literature. Thus, by referring to relevant literature, the accuracy of the diagnosis is improved. Some or all of the above processes in the diagnostic unit may be performed using, for example, AI, or not using AI. For example, the diagnostic unit can input relevant literature data into a generating AI, which can then automatically analyze the data and improve the accuracy of the diagnosis.

[0047] The report generation department can adjust the level of detail in a report based on the importance of the meal. Specific evaluation criteria and adjustment methods for importance include, but are not limited to, the purpose of the meal and the positions of the participants. For example, the report generation department can create a detailed report for a highly important meal. For less important meals, the report generation department can create a concise report. The report generation department can dynamically adjust the level of detail in a report according to the importance of the meal. For example, the report generation department creates a detailed report for a highly important meal. For less important meals, the report generation department creates a concise report. The report generation department dynamically adjusts the level of detail in a report according to the importance of the meal. This allows for the creation of more appropriate reports by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the report generation department may be performed using, for example, AI, or not using AI. For example, the report creation department can input data on the importance of business dinners into a generating AI, which can then automatically analyze the data and adjust the level of detail in the report.

[0048] The report generation unit can apply different report formats based on the category of the meal when creating a report. Specific categories and classification methods include, but are not limited to, business meals and private meals. For example, the report generation unit can apply a business-oriented report format for business meals. For private meals, it can apply a private-oriented report format. The report generation unit can select the most appropriate report format according to the category of the meal. For example, the report generation unit applies a business-oriented report format for business meals. For private meals, it applies a private-oriented report format. The report generation unit selects the most appropriate report format according to the category of the meal. This allows for the creation of more appropriate reports by applying different report formats according to the category of the meal. Some or all of the above processing in the report generation unit may be performed using, for example, AI, or not. For example, the report generation unit can input meal category data into a generating AI, which can automatically analyze the data and apply the most appropriate report format.

[0049] The report creation unit can determine the priority of reports based on the timing of the meals when creating reports. Specific details and evaluation methods for the timing include, but are not limited to, the date and season of the meal. For example, the report creation unit can prioritize reports on recent meals. It can postpone reports on past meals. The report creation unit can dynamically adjust the priority of reports according to the timing of the meals. For example, it prioritizes reports on recent meals. It postpones reports on past meals. By dynamically adjusting the priority of reports according to the timing of the meals, more appropriate reports can be created. Some or all of the above processing in the report creation unit may be performed using, for example, AI, or not. For example, the report creation unit can input meal timing data into a generating AI, which can automatically analyze the data and determine the priority of reports.

[0050] The report generation unit can adjust the order of reports based on the relevance of the meals when creating the report. Specific evaluation criteria and adjustment methods for relevance include, but are not limited to, the purpose of the meal and the relationships between participants. For example, the report generation unit can prioritize creating reports for highly relevant meals. For less relevant meals, the report generation unit can postpone the creation of reports. The report generation unit can dynamically adjust the order of reports according to the relevance of the meals. For example, the report generation unit prioritizes creating reports for highly relevant meals. For less relevant meals, the report generation unit postpones the creation of reports. The report generation unit dynamically adjusts the order of reports according to the relevance of the meals. This allows for the creation of more appropriate reports by adjusting the order of reports based on the relevance of the meals. Some or all of the above processing in the report generation unit may be performed using, for example, AI, or not. For example, the report generation unit can input meal relevance data into a generating AI, which can automatically analyze the data and adjust the order of reports.

[0051] The Security Department can select a data encryption method based on user attributes. Specific types of encryption methods and selection criteria include, but are not limited to, AES and RSA. The Security Department can select the optimal encryption method considering, for example, the user's age and gender. The Security Department can adjust the encryption method considering the user's occupation and lifestyle. The Security Department can select an encryption method considering the user's health status. For example, the Security Department analyzes the user's age and gender and selects the optimal encryption method. The Security Department analyzes the user's occupation and lifestyle and adjusts the encryption method. The Security Department analyzes the user's health status and selects an encryption method. This strengthens data security by selecting the optimal encryption method based on user attributes. Some or all of the above processing in the Security Department may be performed using, for example, AI, or not. For example, the Security Department can input user attribute data into a generating AI, which can automatically analyze the data and select the optimal encryption method.

[0052] The security unit can optimize security measures by taking into account the user's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. For example, if the user is in a specific region, the security unit can apply security measures appropriate to that region. If the user is on the move, the security unit can apply security measures appropriate to their destination. The security unit can dynamically adjust security measures based on the user's geographical location information. For example, if the security unit is in a specific region, it applies security measures appropriate to that region. If the user is on the move, the security unit applies security measures appropriate to their destination. The security unit dynamically adjusts security measures based on the user's geographical location information. This allows for the provision of more appropriate security measures by taking the user's geographical location information into consideration. Some or all of the above-described processes in the security unit may be performed using, for example, AI, or not using AI. For example, the security unit can input the user's geographical location information data into a generating AI, which can then automatically analyze the data and optimize security measures.

[0053] The calendar app integration unit can select the optimal integration method by referring to the user's past schedule history when integrating with a calendar app. Specific examples of the schedule history and methods of reference include, but are not limited to, past appointments and participant history. The calendar app integration unit can, for example, propose the optimal integration method based on the user's past schedule history. The calendar app integration unit can prioritize suggesting integration methods previously used by the user. The calendar app integration unit can select the most efficient integration method from the user's past schedule history. For example, the calendar app integration unit proposes the optimal integration method based on the user's past schedule history. The calendar app integration unit prioritizes suggesting integration methods previously used by the user. The calendar app integration unit selects the most efficient integration method from the user's past schedule history. This allows the optimal integration method to be selected by referring to the user's past schedule history. Some or all of the above processing in the calendar app integration unit may be performed using, for example, AI, or without AI. For example, the calendar app integration unit can input the user's past schedule history data into a generating AI, which can automatically analyze the data and select the optimal integration method.

[0054] The calendar app integration unit can select the optimal integration method when integrating with a calendar app, taking into account the user's device information. Specific details and usage of device information include, but are not limited to, the type of device and the OS version. For example, if the user is using a smartphone, the calendar app integration unit can provide the optimal integration method for smartphones. If the user is using a tablet, the calendar app integration unit can provide the optimal integration method for tablets. If the user is using a smartwatch, the calendar app integration unit can provide the optimal integration method for smartwatches. In this way, the optimal integration method can be selected by considering the user's device information. Some or all of the above processing in the calendar app integration unit may be performed using, for example, AI, or without AI. For example, the calendar app integration unit can input user device information data into a generating AI, which can automatically analyze the data and select the optimal integration method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The acquisition unit can analyze the user's past dining history and select the optimal acquisition method. Specific details and analysis methods of past dining history include, but are not limited to, the date, time, location, and participants of past meals. For example, the acquisition unit can select the optimal acquisition method based on patterns of meals the user has preferred to attend in the past. The acquisition unit can adjust the acquisition method based on patterns of meals the user has avoided in the past. The acquisition unit can select the least stressful acquisition method from the user's past dining history. For example, the acquisition unit analyzes the date, time, and location of meals the user has attended in the past to identify patterns of meals the user can participate in comfortably. The acquisition unit analyzes patterns of meals the user has avoided in the past to select patterns of meals the user is less likely to experience stress. The acquisition unit selects the least stressful acquisition method based on the user's past dining history. Thus, by analyzing the user's past dining history, the optimal acquisition method can be selected. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit inputs the user's past dining history data into a generating AI, which then automatically analyzes the data and selects the optimal acquisition method.

[0057] The recording unit can provide input assistance when the user enters details of a meal by referring to the user's past meal data. Specific methods and criteria for input assistance include, but are not limited to, autocomplete and templates. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit can prioritize displaying frequently used input items from the user's past meal data. The recording unit can make suggestions to reduce input effort based on the user's past meal data. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit prioritizes displaying frequently used input items from the user's past meal data. The recording unit makes suggestions to reduce input effort based on the user's past meal data. This allows the recording unit to provide input assistance and reduce input effort by referring to the user's past meal data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past dining data into a generating AI, which can then automatically analyze the data and provide input assistance.

[0058] The diagnostic unit can improve the accuracy of its diagnosis based on the interrelationships of the meals during the diagnosis process. Specific details and evaluation methods of the interrelationships of the meals include, but are not limited to, the relationships between participants and the purpose of the meal. For example, the diagnostic unit can assess stress levels by considering the relationships between participants in the meal. The diagnostic unit can improve the accuracy of its diagnosis by considering the combination of the meal location and participants. The diagnostic unit can adjust the diagnostic results by considering the frequency of the meals and the relationships between participants. For example, the diagnostic unit analyzes the relationships between participants in the meals and assesses stress levels. The diagnostic unit analyzes the combination of the meal location and participants to improve the accuracy of the diagnosis. The diagnostic unit analyzes the frequency of the meals and the relationships between participants to adjust the diagnostic results. This improves the accuracy of the diagnosis by considering the interrelationships of the meals. Some or all of the above processing in the diagnostic unit may be performed using, for example, AI, or not. For example, the diagnostic unit can input the interrelationship data of the meals into a generating AI, which can then automatically analyze the data and improve the accuracy of the diagnosis.

[0059] The report generation department can adjust the level of detail in a report based on the importance of the meal. Specific evaluation criteria and adjustment methods for importance include, but are not limited to, the purpose of the meal and the positions of the participants. For example, the report generation department can create a detailed report for a highly important meal. For less important meals, the report generation department can create a concise report. The report generation department can dynamically adjust the level of detail in a report according to the importance of the meal. For example, the report generation department creates a detailed report for a highly important meal. For less important meals, the report generation department creates a concise report. The report generation department dynamically adjusts the level of detail in a report according to the importance of the meal. This allows for the creation of more appropriate reports by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the report generation department may be performed using, for example, AI, or not using AI. For example, the report creation department can input data on the importance of business dinners into a generating AI, which can then automatically analyze the data and adjust the level of detail in the report.

[0060] The Security Department can select a data encryption method based on user attributes. Specific types of encryption methods and selection criteria include, but are not limited to, AES and RSA. The Security Department can select the optimal encryption method considering, for example, the user's age and gender. The Security Department can adjust the encryption method considering the user's occupation and lifestyle. The Security Department can select an encryption method considering the user's health status. For example, the Security Department analyzes the user's age and gender and selects the optimal encryption method. The Security Department analyzes the user's occupation and lifestyle and adjusts the encryption method. The Security Department analyzes the user's health status and selects an encryption method. This strengthens data security by selecting the optimal encryption method based on user attributes. Some or all of the above processing in the Security Department may be performed using, for example, AI, or not. For example, the Security Department can input user attribute data into a generating AI, which can automatically analyze the data and select the optimal encryption method.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The retrieval unit retrieves the dinner plans entered by the user in the calendar app. For example, the retrieval unit can automatically retrieve the user's dinner plans using the calendar app's API. The retrieval unit can also retrieve dinner plans that the user has manually entered. Step 2: The recording unit records the meal based on the meal schedule obtained by the acquisition unit. For example, the recording unit records details such as the location of the meal, participants, meal contents, and perceived stress level. The recording unit saves the information entered by the user to a database, which can be used later for diagnosis and report creation. Step 3: The diagnostic unit performs a diagnosis of the meal based on the data recorded by the recording unit. For example, the diagnostic unit evaluates the user's stress level and the progress of the meal, and provides advice for the next meal. The diagnostic unit can use AI to analyze the data and provide appropriate advice to the user. Step 4: The reporting department creates a report based on the diagnostic results obtained by the diagnostic department. For example, the reporting department creates a report that includes the user's progress in meals, changes in stress levels, and areas for improvement. The reporting department can create reports regularly and provide them to users and medical professionals.

[0063] (Example of form 2) The dining management, diagnosis, and reporting system according to an embodiment of the present invention is a system that supports cognitive behavioral therapy for patients with social dining phobia. This system works in conjunction with a calendar application to database records of dining out in daily life, create reports, and perform dining out diagnoses, thereby supporting cognitive behavioral therapy. First, the user enters a dining out appointment into the calendar application. Next, the system automatically retrieves the appointment and begins recording the dining out. After the dining out is finished, the user enters details of the dining out into the system. For example, the location of the dining out, participants, meal contents, and the level of stress felt. The system performs a diagnosis of the dining out based on the entered data. The diagnosis results evaluate the user's stress level and the progress of the meal, and provide advice for the next dining out. For example, if a particular location or participant causes stress, this information can be used to plan the next dining out. Furthermore, the system databases the dining out records and creates reports periodically. The reports include the user's progress in dining out, changes in stress levels, and areas for improvement. These reports are used by the user themselves and medical professionals to understand the progress of their treatment. This system allows patients with social eating anxiety to receive cognitive behavioral therapy in their daily lives, which is expected to enhance the effectiveness of their treatment. Thus, the social eating management, diagnosis, and reporting system can support cognitive behavioral therapy for patients with social eating anxiety and improve the effectiveness of their treatment.

[0064] The meal management, diagnosis, and reporting system according to this embodiment comprises an acquisition unit, a recording unit, a diagnosis unit, and a report creation unit. The acquisition unit acquires meal plans entered by the user in a calendar application. For example, the acquisition unit can automatically acquire the user's meal plans using the calendar application's API. The acquisition unit can also acquire meal plans manually entered by the user. The recording unit records the meal based on the meal plans acquired by the acquisition unit. For example, the recording unit records details such as the location of the meal, participants, meal contents, and perceived stress level. The recording unit saves the information entered by the user in a database, which can be used later for diagnosis and report creation. The diagnosis unit diagnoses the meal based on the data recorded by the recording unit. For example, the diagnosis unit evaluates the user's stress level and the progress of the meal, and provides advice for the next meal. The diagnosis unit can analyze the data using AI and provide appropriate advice to the user. The report creation unit creates a report based on the diagnosis results obtained by the diagnosis unit. For example, the reporting unit creates reports that include the user's progress during meals, changes in stress levels, and areas for improvement. The reporting unit can create reports regularly and provide them to users and medical professionals. In this way, the meal management, diagnosis, and reporting system according to this embodiment can support cognitive behavioral therapy for patients with social eating phobia and enhance the effectiveness of treatment.

[0065] The recording unit can record the specific details of a meal entered by the user. These details may include, but are not limited to, the purpose of the meal, the menu, and the job titles of the participants. The recording unit can, for example, save the user-entered meal details to a database. For example, the recording unit can record the purpose of the meal, which may include business meals, private meals, or family meals. The recording unit can record the menu, which may include appetizers, main courses, and desserts. The recording unit can also record the job titles of the participants, which may include superiors, colleagues, and friends. This allows for detailed analysis based on the recorded meal details. Some or all of the above processing in the recording unit may be performed using AI, or not. For example, the recording unit can input the user-entered meal details into an AI, which can then automatically organize the data and save it to a database.

[0066] The diagnostic unit can evaluate the user's stress level and eating progress based on recorded data. Stress level evaluation criteria and specific measurement methods include, but are not limited to, questionnaires and biometric data. For example, the diagnostic unit can conduct a questionnaire to evaluate the stress level entered by the user. The questionnaire may include questions about the degree and cause of the stress the user experienced. The diagnostic unit can evaluate stress levels using biometric data. Biometric data may include heart rate, skin electrical activity, and respiratory rate. The diagnostic unit can evaluate the eating progress. Evaluation criteria and specific measurement methods for eating progress include, for example, meal times and meal amounts. For example, the diagnostic unit can evaluate the meal times entered by the user. Meal times include the start time and end time of the meal. The diagnostic unit can evaluate the meal amount. Meal amount includes the amount eaten and the amount left over. By evaluating the user's stress level and eating progress, an appropriate diagnosis can be made. Some or all of the above-described processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's stress level and dietary progress into the AI, which can then automatically analyze the data and output evaluation results.

[0067] The report generation unit can provide advice to the user based on the diagnostic results. The specific content and format of the advice may include, but are not limited to, suggestions for action and psychological support. For example, the report generation unit can suggest actions for the user regarding their next meal. These suggestions may include selecting a venue, participants, and menu. The report generation unit can also provide psychological support to the user. This support may include suggestions for relaxation techniques and stress management methods. This allows the user to plan for their next meal by providing advice based on the diagnostic results. Some or all of the above processes in the report generation unit may be performed using, for example, AI, or not. For example, the report generation unit can input the diagnostic results into an AI, which can then automatically generate and provide advice to the user.

[0068] The meal management, diagnosis, and reporting system includes a security section to protect data security. The security section provides specific methods and standards for ensuring data security. For example, the security section can use methods such as data encryption, access control, and data backup. Data encryption includes encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). Access control includes user authentication, permission management, and monitoring of login history. Data backup includes regular data backups, encryption of backup data, and secure storage of backup data. This ensures data security and protects user privacy. Some or all of the above processes in the security section may be performed using AI, for example, or not. For example, the security section can entrust data encryption and access control to AI, which can automatically implement security measures.

[0069] The meal management, diagnosis, and reporting system can automatically retrieve meal schedules by linking with a calendar application. Specific examples of calendar applications and integration methods include, but are not limited to, Google Calendar and Outlook Calendar. For example, the system can automatically retrieve a user's meal schedule using the Google Calendar API. Similarly, the system can automatically retrieve a user's meal schedule using the Outlook Calendar API. This allows for the automatic retrieval of meal schedules by linking with a calendar application. Some or all of the above-described processes in the system may be performed using AI, or not. For example, the system can input the calendar application's API into an AI, which can then automatically retrieve the meal schedule.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring meal plans based on the estimated emotions. Specific methods and criteria for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the acquisition unit can capture the user's facial expression with a camera and estimate their emotions using facial expression recognition technology. Facial expression recognition technology includes algorithms that analyze changes in facial expressions and estimate emotions. The acquisition unit can record the user's voice and estimate their emotions using voice analysis technology. Voice analysis technology includes algorithms that analyze the tone and speed of the voice and estimate emotions. The acquisition unit can delay acquiring meal plans if the user is feeling stressed. For example, if the acquisition unit determines from the user's facial expression or voice that they are feeling stressed, it will delay acquiring meal plans. The acquisition unit can accelerate acquiring meal plans if the user is relaxed. For example, if the acquisition unit determines from the user's facial expression or voice that they are relaxed, it will accelerate acquiring meal plans. The acquisition unit can instantly retrieve meal schedules if the user is in a hurry. For example, if the acquisition unit determines from the user's facial expressions or voice that the user is in a hurry, it will instantly retrieve the meal schedule. This allows for more appropriate timing of meal schedule acquisition by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's facial expressions and voice data into the generative AI, which can automatically estimate emotions and adjust the timing of meal schedule acquisition.

[0071] The acquisition unit can analyze the user's past dining history and select the optimal acquisition method. Specific details and analysis methods of past dining history include, but are not limited to, the date, time, location, and participants of past meals. For example, the acquisition unit can select the optimal acquisition method based on patterns of meals the user has preferred to attend in the past. The acquisition unit can adjust the acquisition method based on patterns of meals the user has avoided in the past. The acquisition unit can select the least stressful acquisition method from the user's past dining history. For example, the acquisition unit analyzes the date, time, and location of meals the user has attended in the past to identify patterns of meals the user can participate in comfortably. The acquisition unit analyzes patterns of meals the user has avoided in the past to select patterns of meals the user is less likely to experience stress. The acquisition unit selects the least stressful acquisition method based on the user's past dining history. Thus, by analyzing the user's past dining history, the optimal acquisition method can be selected. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit inputs the user's past dining history data into a generating AI, which then automatically analyzes the data and selects the optimal acquisition method.

[0072] The acquisition unit can filter meal plans based on the user's current lifestyle and areas of interest when acquiring them. Specific details and evaluation methods for lifestyle include, but are not limited to, work schedules and family circumstances. Specific details and evaluation methods for areas of interest include, but are not limited to, hobbies and topics of interest. For example, if the user is currently busy, the acquisition unit can filter out low-priority meal plans. If the user is interested in a particular area of ​​interest, the acquisition unit can prioritize acquiring meal plans related to that area. The acquisition unit can filter out appropriate meal plans based on the user's lifestyle. For example, the acquisition unit analyzes the user's work schedule and filters out low-priority meal plans. The acquisition unit prioritizes acquiring relevant meal plans based on the user's hobbies and topics of interest. The acquisition unit considers the user's family circumstances and filters out appropriate meal plans. This allows for the acquisition of more appropriate meal plans by filtering them based on the user's lifestyle and areas of interest. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's lifestyle and areas of interest into a generating AI, which can then automatically analyze the data and filter out meal plans.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of meal appointments to acquire based on the estimated emotions. Specific criteria and methods for determining priority include, but are not limited to, importance and urgency. For example, if the user is feeling stressed, the acquisition unit can postpone less important meal appointments. If the user is relaxed, the acquisition unit can prioritize more important meal appointments. If the user is in a hurry, the acquisition unit can prioritize meal appointments requiring immediate attention. For example, if the acquisition unit determines from the user's facial expressions and voice that they are feeling stressed, it will postpone less important meal appointments. If the acquisition unit determines from the user's facial expressions and voice that they are relaxed, it will prioritize more important meal appointments. If the acquisition unit determines from the user's facial expressions and voice that they are in a hurry, it will prioritize meal appointments requiring immediate attention. This allows for the acquisition of more appropriate meal appointments by determining the priority of meal appointments based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and determine the priority of meal plans.

[0074] The acquisition unit can prioritize the acquisition of highly relevant dining appointments based on the user's geographical location information when acquiring dining appointments. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. For example, the acquisition unit can prioritize the acquisition of dining appointments in locations close to the user's current location. If the user is in a specific region, the acquisition unit can prioritize the acquisition of dining appointments in that region. If the user is on the move, the acquisition unit can prioritize the acquisition of dining appointments at their destination. For example, the acquisition unit acquires the user's current location from GPS data and prioritizes the acquisition of nearby dining appointments. If the user is in a specific region, the acquisition unit prioritizes the acquisition of dining appointments in that region. If the user is on the move, the acquisition unit prioritizes the acquisition of dining appointments at their destination. This allows for the priority acquisition of highly relevant dining appointments by considering the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit inputs the user's geographical location data into the generating AI, which automatically analyzes the data and prioritizes the acquisition of highly relevant meal plans.

[0075] The acquisition unit can analyze the user's social media activity when acquiring dinner plans and acquire related plans. Specific examples of social media activity and analysis methods include, but are not limited to, posts and the number of likes. The acquisition unit can, for example, prioritize acquiring dinner plans mentioned by the user on social media. The acquisition unit can prioritize acquiring dinner plans that the user's social media friends will participate in. The acquisition unit can acquire dinner plans that are likely to be of interest based on the user's social media activity. For example, the acquisition unit automatically acquires dinner plans mentioned by the user on social media. The acquisition unit prioritizes acquiring dinner plans that the user's social media friends will participate in. The acquisition unit acquires dinner plans that are likely to be of interest based on the user's social media activity. This allows the acquisition unit to acquire relevant dinner plans by analyzing the user's social media activity. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not. For example, the acquisition unit can input the user's social media activity data into a generating AI, which can automatically analyze the data and acquire relevant dinner plans.

[0076] The recording unit can estimate the user's emotions and adjust the timing of prompting for detailed input about the meal based on the estimated emotions. Specific details and methods of input include, but are not limited to, input fields and input formats. For example, if the user is feeling stressed, the recording unit can postpone the input of detailed information about the meal. If the user is relaxed, the recording unit can expedite the input of detailed information. If the user is in a hurry, the recording unit can provide a simplified input form. For example, if the recording unit determines from the user's facial expressions and voice that they are feeling stressed, it will postpone the input of detailed information about the meal. If the recording unit determines from the user's facial expressions and voice that they are relaxed, it will expedite the input of detailed information. If the recording unit determines from the user's facial expressions and voice that they are in a hurry, it will provide a simplified input form. This allows for more appropriate timing of detailed input by adjusting the timing of prompting for detailed information about the meal based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the recording unit may be performed using AI, or not using AI. For example, the recording unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the timing for prompting for detailed input of the meal.

[0077] The recording unit can provide input assistance when the user enters details of a meal by referring to the user's past meal data. Specific methods and criteria for input assistance include, but are not limited to, autocomplete and templates. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit can prioritize displaying frequently used input items from the user's past meal data. The recording unit can make suggestions to reduce input effort based on the user's past meal data. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit prioritizes displaying frequently used input items from the user's past meal data. The recording unit makes suggestions to reduce input effort based on the user's past meal data. This allows the recording unit to provide input assistance and reduce input effort by referring to the user's past meal data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past dining data into a generating AI, which can then automatically analyze the data and provide input assistance.

[0078] The recording unit can customize input fields based on the user's current lifestyle when entering details about a meal. Specific examples of input fields and customization methods include, but are not limited to, required and optional fields. For example, the recording unit can minimize input fields when the user is busy. It can provide detailed input fields when the user is relaxed. The recording unit can dynamically change input fields according to the user's lifestyle. This allows for more appropriate input by customizing input fields based on the user's lifestyle. Some or all of the above processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input user lifestyle data into a generating AI, which can automatically analyze the data and customize the input fields.

[0079] The recording unit can estimate the user's emotions and determine the priority of input items based on those emotions. Specific criteria and methods for determining priority include, but are not limited to, importance and urgency. For example, if the user is stressed, the recording unit can postpone low-importance input items. If the user is relaxed, the recording unit can prioritize high-importance input items. If the user is in a hurry, the recording unit can prioritize items that require immediate input. For example, if the recording unit determines from the user's facial expressions and voice that they are stressed, it will postpone low-importance input items. If the recording unit determines from the user's facial expressions and voice that they are relaxed, it will prioritize high-importance input items. If the recording unit determines from the user's facial expressions and voice that they are in a hurry, it will prioritize items that require immediate input. This allows for more appropriate input by determining the priority of input items based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the recording unit may be performed using AI, or not using AI. For example, the recording unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and determine the priority of the input items.

[0080] The recording unit can optimize input fields when users enter details of a meal, taking into account their geographical location. Specific methods for obtaining and using geographical location information include, but are not limited to, GPS data and location services. For example, if the meal is near the user's current location, the recording unit can prioritize displaying input fields related to that location. If the user is in a specific region, the recording unit can prioritize displaying input fields related to that region. If the user is on the move, the recording unit can prioritize displaying input fields related to their destination. For example, the recording unit obtains the user's current location from GPS data and prioritizes displaying input fields related to nearby meals. If the user is in a specific region, the recording unit prioritizes displaying input fields related to that region. If the user is on the move, the recording unit prioritizes displaying input fields related to their destination. This allows for the optimization of input fields by taking into account the user's geographical location. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's geographical location data into a generating AI, which can then automatically analyze the data and optimize the input fields.

[0081] The recording unit can analyze the user's social media activity and automatically input relevant information when entering details of a meal. Specific examples of social media activity and analysis methods include, but are not limited to, posts and the number of likes. For example, the recording unit can automatically input details of a meal mentioned by the user on social media. The recording unit can automatically input details of a meal attended by the user's social media friends. The recording unit can automatically input relevant information from the user's social media activity. For example, the recording unit automatically inputs details of a meal mentioned by the user on social media. The recording unit automatically inputs details of a meal attended by the user's social media friends. The recording unit automatically inputs relevant information from the user's social media activity. This allows for the automatic input of relevant information by analyzing the user's social media activity. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not. For example, the recording unit can input the user's social media activity data into a generating AI, which can automatically analyze the data and automatically input relevant information.

[0082] The diagnostic unit can estimate the user's emotions and adjust the diagnostic criteria based on the estimated emotions. Specific details and adjustment methods for the diagnostic criteria include, but are not limited to, evaluation items and evaluation criteria. For example, the diagnostic unit can relax the diagnostic criteria if the user is stressed. It can tighten the diagnostic criteria if the user is relaxed. It can apply simplified diagnostic criteria if the user is in a hurry. For example, if the diagnostic unit determines from the user's facial expressions and voice that the user is stressed, it will relax the diagnostic criteria. If the diagnostic unit determines from the user's facial expressions and voice that the user is relaxed, it will tighten the diagnostic criteria. If the diagnostic unit determines from the user's facial expressions and voice that the user is in a hurry, it will apply simplified diagnostic criteria. This allows for a more accurate diagnosis by adjusting the diagnostic criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the diagnostic unit may be performed using AI, for example, or without AI. For example, the diagnostic unit can input the user's facial expressions and voice data into a generating AI, which can then automatically estimate emotions and adjust the diagnostic criteria.

[0083] The diagnostic unit can improve the accuracy of its diagnosis based on the interrelationships of the meals during the diagnosis process. Specific details and evaluation methods of the interrelationships of the meals include, but are not limited to, the relationships between participants and the purpose of the meal. For example, the diagnostic unit can assess stress levels by considering the relationships between participants in the meal. The diagnostic unit can improve the accuracy of its diagnosis by considering the combination of the meal location and participants. The diagnostic unit can adjust the diagnostic results by considering the frequency of the meals and the relationships between participants. For example, the diagnostic unit analyzes the relationships between participants in the meals and assesses stress levels. The diagnostic unit analyzes the combination of the meal location and participants to improve the accuracy of the diagnosis. The diagnostic unit analyzes the frequency of the meals and the relationships between participants to adjust the diagnostic results. This improves the accuracy of the diagnosis by considering the interrelationships of the meals. Some or all of the above processing in the diagnostic unit may be performed using, for example, AI, or not. For example, the diagnostic unit can input the interrelationship data of the meals into a generating AI, which can then automatically analyze the data and improve the accuracy of the diagnosis.

[0084] The diagnostic unit can perform a diagnosis based on the user's attribute information. Specific examples of attribute information include, but are not limited to, age, gender, and occupation. The diagnostic unit can adjust diagnostic criteria, for example, by considering the user's age and gender. The diagnostic unit can adjust diagnostic results, considering the user's occupation and lifestyle. The diagnostic unit can improve the accuracy of the diagnosis by considering the user's health condition. For example, the diagnostic unit analyzes the user's age and gender and adjusts the diagnostic criteria. The diagnostic unit analyzes the user's occupation and lifestyle and adjusts the diagnostic results. The diagnostic unit analyzes the user's health condition and improves the accuracy of the diagnosis. This allows for a more appropriate diagnosis by considering the user's attribute information. Some or all of the above processes in the diagnostic unit may be performed using, for example, AI, or without AI. For example, the diagnostic unit can input user attribute information data into a generating AI, which can then automatically analyze the data and perform a diagnosis.

[0085] The diagnostic unit can estimate the user's emotions and adjust the display order of diagnostic results based on the estimated emotions. Specific criteria and methods for determining the display order include, but are not limited to, importance and urgency. For example, if the user is stressed, the diagnostic unit can postpone less important diagnostic results. If the user is relaxed, the diagnostic unit can prioritize more important diagnostic results. If the user is in a hurry, the diagnostic unit can prioritize diagnostic results requiring immediate attention. For example, if the diagnostic unit determines from the user's facial expressions and voice that they are stressed, it will postpone less important diagnostic results. If the diagnostic unit determines from the user's facial expressions and voice that they are relaxed, it will prioritize more important diagnostic results. If the diagnostic unit determines from the user's facial expressions and voice that they are in a hurry, it will prioritize diagnostic results requiring immediate attention. This allows for the display of diagnostic results in a more appropriate order by adjusting the display order based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the diagnostic unit may be performed using AI, or not using AI. For example, the diagnostic unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the display order of the diagnostic results.

[0086] The diagnostic unit can perform a diagnosis based on the geographical distribution of meals during the diagnosis process. Specific details and evaluation methods of the geographical distribution include, but are not limited to, the location of the meal and regional characteristics. For example, if the location of the meal is a source of stress for the user, the diagnostic unit can reflect this information in the diagnosis. The diagnostic unit can evaluate stress levels considering the geographical distribution of meals. The diagnostic unit can adjust the diagnosis results considering the distance between the meal location and the user's place of residence. For example, if the location of the meal is a source of stress for the user, the diagnostic unit reflects this information in the diagnosis. The diagnostic unit evaluates stress levels considering the geographical distribution of meals. The diagnostic unit adjusts the diagnosis results considering the distance between the meal location and the user's place of residence. This allows for a more appropriate diagnosis by considering the geographical distribution of meals. Some or all of the above processing in the diagnostic unit may be performed using, for example, AI, or without AI. For example, the diagnostic unit can input geographical distribution data of meals into a generating AI, which can automatically analyze the data and perform a diagnosis.

[0087] The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature during the diagnosis process. Specific examples of relevant literature and methods of reference include, but are not limited to, academic papers and specialized books. The diagnostic unit can update diagnostic criteria by referring to the latest research findings. The diagnostic unit can improve the reliability of diagnostic results based on relevant literature. The diagnostic unit can improve the accuracy of its diagnosis by referring to relevant literature. For example, the diagnostic unit updates diagnostic criteria by referring to the latest research findings. The diagnostic unit improves the reliability of diagnostic results based on relevant literature. The diagnostic unit improves the accuracy of its diagnosis by referring to relevant literature. Thus, by referring to relevant literature, the accuracy of the diagnosis is improved. Some or all of the above processes in the diagnostic unit may be performed using, for example, AI, or not using AI. For example, the diagnostic unit can input relevant literature data into a generating AI, which can then automatically analyze the data and improve the accuracy of the diagnosis.

[0088] The reporting unit can estimate the user's emotions and adjust the report's presentation based on those emotions. Specific examples of adjustments include, but are not limited to, the tone of the writing and the vocabulary used. For example, if the user is stressed, the reporting unit can create a concise and easy-to-read report. If the user is relaxed, the reporting unit can create a detailed report. If the user is in a hurry, the reporting unit can create a to-the-point report. For instance, if the reporting unit determines from the user's facial expressions and voice that they are stressed, it will create a concise and easy-to-read report. If the reporting unit determines from the user's facial expressions and voice that they are relaxed, it will create a detailed report. If the reporting unit determines from the user's facial expressions and voice that they are in a hurry, it will create a to-the-point report. This allows for the creation of more appropriate reports by adjusting the report's presentation based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the report creation unit may be performed using AI, or not using AI. For example, the report creation unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the way the report is presented.

[0089] The report generation department can adjust the level of detail in a report based on the importance of the meal. Specific evaluation criteria and adjustment methods for importance include, but are not limited to, the purpose of the meal and the positions of the participants. For example, the report generation department can create a detailed report for a highly important meal. For less important meals, the report generation department can create a concise report. The report generation department can dynamically adjust the level of detail in a report according to the importance of the meal. For example, the report generation department creates a detailed report for a highly important meal. For less important meals, the report generation department creates a concise report. The report generation department dynamically adjusts the level of detail in a report according to the importance of the meal. This allows for the creation of more appropriate reports by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the report generation department may be performed using, for example, AI, or not using AI. For example, the report creation department can input data on the importance of business dinners into a generating AI, which can then automatically analyze the data and adjust the level of detail in the report.

[0090] The report generation unit can apply different report formats based on the category of the meal when creating a report. Specific categories and classification methods include, but are not limited to, business meals and private meals. For example, the report generation unit can apply a business-oriented report format for business meals. For private meals, it can apply a private-oriented report format. The report generation unit can select the most appropriate report format according to the category of the meal. For example, the report generation unit applies a business-oriented report format for business meals. For private meals, it applies a private-oriented report format. The report generation unit selects the most appropriate report format according to the category of the meal. This allows for the creation of more appropriate reports by applying different report formats according to the category of the meal. Some or all of the above processing in the report generation unit may be performed using, for example, AI, or not. For example, the report generation unit can input meal category data into a generating AI, which can automatically analyze the data and apply the most appropriate report format.

[0091] The reporting system can estimate the user's emotions and adjust the report length based on those emotions. Specific criteria and adjustment methods for report length include, but are not limited to, the number of pages and characters. For example, if the user is stressed, the system can create a short, concise report. If the user is relaxed, it can create a longer report with more detailed explanations. If the user is in a hurry, it can create a concise and easy-to-read report. For example, if the system determines from the user's facial expressions and voice that they are stressed, it will create a short, concise report. If the system determines from the user's facial expressions and voice that they are relaxed, it will create a longer report with more detailed explanations. If the system determines from the user's facial expressions and voice that they are in a hurry, it will create a concise and easy-to-read report. This allows for the creation of more appropriate reports by adjusting the report length based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using AI, or not using AI. For example, the report generation unit can input user facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the length of the report.

[0092] The report creation unit can determine the priority of reports based on the timing of the meals when creating reports. Specific details and evaluation methods for the timing include, but are not limited to, the date and season of the meal. For example, the report creation unit can prioritize reports on recent meals. It can postpone reports on past meals. The report creation unit can dynamically adjust the priority of reports according to the timing of the meals. For example, it prioritizes reports on recent meals. It postpones reports on past meals. By dynamically adjusting the priority of reports according to the timing of the meals, more appropriate reports can be created. Some or all of the above processing in the report creation unit may be performed using, for example, AI, or not. For example, the report creation unit can input meal timing data into a generating AI, which can automatically analyze the data and determine the priority of reports.

[0093] The report generation unit can adjust the order of reports based on the relevance of the meals when creating the report. Specific evaluation criteria and adjustment methods for relevance include, but are not limited to, the purpose of the meal and the relationships between participants. For example, the report generation unit can prioritize creating reports for highly relevant meals. For less relevant meals, the report generation unit can postpone the creation of reports. The report generation unit can dynamically adjust the order of reports according to the relevance of the meals. For example, the report generation unit prioritizes creating reports for highly relevant meals. For less relevant meals, the report generation unit postpones the creation of reports. The report generation unit dynamically adjusts the order of reports according to the relevance of the meals. This allows for the creation of more appropriate reports by adjusting the order of reports based on the relevance of the meals. Some or all of the above processing in the report generation unit may be performed using, for example, AI, or not. For example, the report generation unit can input meal relevance data into a generating AI, which can automatically analyze the data and adjust the order of reports.

[0094] The security unit can estimate the user's emotions and adjust the security level based on those emotions. Specific criteria for determining and adjusting the security level include, but are not limited to, encryption strength and access control strictness. For example, the security unit can relax the security level if the user is stressed. It can strengthen the security level if the user is relaxed. It can apply simplified security settings if the user is in a hurry. For example, if the security unit determines from the user's facial expressions and voice that they are stressed, it will relax the security level. If the security unit determines from the user's facial expressions and voice that they are relaxed, it will strengthen the security level. If the security unit determines from the user's facial expressions and voice that they are in a hurry, it will apply simplified security settings. This allows for more appropriate security measures to be provided by adjusting the security level based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the security unit may be performed using AI, for example, or without AI. For example, the security unit can input user facial expressions and voice data into a generating AI, which can then automatically estimate emotions and adjust the security level.

[0095] The Security Department can select a data encryption method based on user attributes. Specific types of encryption methods and selection criteria include, but are not limited to, AES and RSA. The Security Department can select the optimal encryption method considering, for example, the user's age and gender. The Security Department can adjust the encryption method considering the user's occupation and lifestyle. The Security Department can select an encryption method considering the user's health status. For example, the Security Department analyzes the user's age and gender and selects the optimal encryption method. The Security Department analyzes the user's occupation and lifestyle and adjusts the encryption method. The Security Department analyzes the user's health status and selects an encryption method. This strengthens data security by selecting the optimal encryption method based on user attributes. Some or all of the above processing in the Security Department may be performed using, for example, AI, or not. For example, the Security Department can input user attribute data into a generating AI, which can automatically analyze the data and select the optimal encryption method.

[0096] The security department can estimate the user's emotions and adjust the frequency of security notifications based on those emotions. Specific criteria and adjustment methods for notification frequency include, but are not limited to, notification intervals and frequency based on importance. For example, the security department can reduce the frequency of security notifications if the user is stressed. It can increase the frequency of security notifications if the user is relaxed. It can prioritize only important security notifications if the user is in a hurry. For example, if the security department determines from the user's facial expressions and voice that they are stressed, it will reduce the frequency of security notifications. If the security department determines from the user's facial expressions and voice that they are relaxed, it will increase the frequency of security notifications. If the security department determines from the user's facial expressions and voice that they are in a hurry, it will prioritize only important security notifications. This allows for the provision of more appropriate security notifications by adjusting the frequency based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the security unit may be performed using AI, or not using AI. For example, the security unit can input user facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the frequency of security notifications.

[0097] The security unit can optimize security measures by taking into account the user's geographical location information. Specific methods for acquiring and using geographical location information include, but are not limited to, GPS data and location services. For example, if the user is in a specific region, the security unit can apply security measures appropriate to that region. If the user is on the move, the security unit can apply security measures appropriate to their destination. The security unit can dynamically adjust security measures based on the user's geographical location information. For example, if the security unit is in a specific region, it applies security measures appropriate to that region. If the user is on the move, the security unit applies security measures appropriate to their destination. The security unit dynamically adjusts security measures based on the user's geographical location information. This allows for the provision of more appropriate security measures by taking the user's geographical location information into consideration. Some or all of the above-described processes in the security unit may be performed using, for example, AI, or not using AI. For example, the security unit can input the user's geographical location information data into a generating AI, which can then automatically analyze the data and optimize security measures.

[0098] The calendar app integration unit can estimate the user's emotions and adjust the integration method with the calendar app based on the estimated emotions. Specific integration methods and adjustments include, but are not limited to, API integration and data synchronization. For example, if the user is stressed, the calendar app integration unit can provide a simple integration method. If the user is relaxed, it can provide detailed integration options. If the user is in a hurry, it can provide a method for quick integration completion. For example, if the calendar app integration unit determines from the user's facial expressions and voice that they are stressed, it provides a simple integration method. If the calendar app integration unit determines from the user's facial expressions and voice that they are relaxed, it provides detailed integration options. If the calendar app integration unit determines from the user's facial expressions and voice that they are in a hurry, it provides a method for quick integration completion. This allows for more appropriate integration by adjusting the integration method with the calendar app based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the calendar app integration unit may be performed using AI, or not using AI. For example, the calendar app integration unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the method of integration with the calendar app.

[0099] The calendar app integration unit can select the optimal integration method by referring to the user's past schedule history when integrating with a calendar app. Specific examples of the schedule history and methods of reference include, but are not limited to, past appointments and participant history. The calendar app integration unit can, for example, propose the optimal integration method based on the user's past schedule history. The calendar app integration unit can prioritize suggesting integration methods previously used by the user. The calendar app integration unit can select the most efficient integration method from the user's past schedule history. For example, the calendar app integration unit proposes the optimal integration method based on the user's past schedule history. The calendar app integration unit prioritizes suggesting integration methods previously used by the user. The calendar app integration unit selects the most efficient integration method from the user's past schedule history. This allows the optimal integration method to be selected by referring to the user's past schedule history. Some or all of the above processing in the calendar app integration unit may be performed using, for example, AI, or without AI. For example, the calendar app integration unit can input the user's past schedule history data into a generating AI, which can automatically analyze the data and select the optimal integration method.

[0100] The calendar app integration unit can estimate the user's emotions and determine the priority of calendar app integrations based on those emotions. Specific criteria and methods for determining priority include, but are not limited to, importance and urgency. For example, if the user is stressed, the calendar app integration unit can postpone less important integrations. If the user is relaxed, the calendar app integration unit can prioritize more important integrations. If the user is in a hurry, the calendar app integration unit can prioritize integrations requiring immediate attention. For example, if the calendar app integration unit determines from the user's facial expressions and voice that they are stressed, it will postpone less important integrations. If the calendar app integration unit determines from the user's facial expressions and voice that they are relaxed, it will prioritize more important integrations. If the calendar app integration unit determines from the user's facial expressions and voice that they are in a hurry, it will prioritize integrations requiring immediate attention. This allows for more appropriate integrations by determining the priority of calendar app integrations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the calendar app integration unit may be performed using AI, or not using AI. For example, the calendar app integration unit can input the user's facial expressions and voice data into the generation AI, which can then automatically estimate emotions and determine the priority of calendar app integration.

[0101] The calendar app integration unit can select the optimal integration method when integrating with a calendar app, taking into account the user's device information. Specific details and usage of device information include, but are not limited to, the type of device and the OS version. For example, if the user is using a smartphone, the calendar app integration unit can provide the optimal integration method for smartphones. If the user is using a tablet, the calendar app integration unit can provide the optimal integration method for tablets. If the user is using a smartwatch, the calendar app integration unit can provide the optimal integration method for smartwatches. In this way, the optimal integration method can be selected by considering the user's device information. Some or all of the above processing in the calendar app integration unit may be performed using, for example, AI, or without AI. For example, the calendar app integration unit can input user device information data into a generating AI, which can automatically analyze the data and select the optimal integration method. === Hard Collateral 1-1 === Each of the multiple elements described above, including the acquisition unit, recording unit, diagnostic unit, report creation unit, and security unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires meal schedules using the API of a calendar application. The recording unit is implemented by the control unit 46A of the smart device 14 and records the details of the meal and stores them in a database. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI and provides advice to the user. The report creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a report based on the diagnostic results. The security unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs data encryption and access control. === Hard Collateral 1-2 === Each of the multiple elements described above, including the acquisition unit, recording unit, diagnostic unit, report creation unit, and security unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires dinner schedules using the API of a calendar application. The recording unit is implemented by the control unit 46A of the smart glasses 214 and records the details of dinners and stores them in a database. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes data using AI to provide advice to the user. The report creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a report based on the diagnostic results. The security unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs data encryption and access control. === Hard Collateral 1-3 === Each of the multiple elements described above, including the acquisition unit, recording unit, diagnostic unit, report creation unit, and security unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires meal schedules using the API of a calendar application. The recording unit is implemented by the control unit 46A of the headset terminal 314 and records the details of the meal and stores them in a database. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI and provides advice to the user. The report creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a report based on the diagnostic results. The security unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs data encryption and access control. === Hard Collateral 1-4 === Each of the multiple elements described above, including the acquisition unit, recording unit, diagnostic unit, report creation unit, and security unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires meal schedules using the API of a calendar application. The recording unit is implemented by the control unit 46A of the robot 414 and records the details of the meal and stores them in a database. The diagnostic unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using AI and provides advice to the user. The report creation unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates a report based on the diagnostic results. The security unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs data encryption and access control.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The acquisition unit can analyze the user's past dining history and select the optimal acquisition method. Specific details and analysis methods of past dining history include, but are not limited to, the date, time, location, and participants of past meals. For example, the acquisition unit can select the optimal acquisition method based on patterns of meals the user has preferred to attend in the past. The acquisition unit can adjust the acquisition method based on patterns of meals the user has avoided in the past. The acquisition unit can select the least stressful acquisition method from the user's past dining history. For example, the acquisition unit analyzes the date, time, and location of meals the user has attended in the past to identify patterns of meals the user can participate in comfortably. The acquisition unit analyzes patterns of meals the user has avoided in the past to select patterns of meals the user is less likely to experience stress. The acquisition unit selects the least stressful acquisition method based on the user's past dining history. Thus, by analyzing the user's past dining history, the optimal acquisition method can be selected. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit inputs the user's past dining history data into a generating AI, which then automatically analyzes the data and selects the optimal acquisition method.

[0104] The recording unit can provide input assistance when the user enters details of a meal by referring to the user's past meal data. Specific methods and criteria for input assistance include, but are not limited to, autocomplete and templates. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit can prioritize displaying frequently used input items from the user's past meal data. The recording unit can make suggestions to reduce input effort based on the user's past meal data. For example, the recording unit can automatically display details of meals previously entered by the user to assist with input. The recording unit prioritizes displaying frequently used input items from the user's past meal data. The recording unit makes suggestions to reduce input effort based on the user's past meal data. This allows the recording unit to provide input assistance and reduce input effort by referring to the user's past meal data. Some or all of the above processing in the recording unit may be performed using, for example, AI, or not using AI. For example, the recording unit can input the user's past dining data into a generating AI, which can then automatically analyze the data and provide input assistance.

[0105] The diagnostic unit can improve the accuracy of its diagnosis based on the interrelationships of the meals during the diagnosis process. Specific details and evaluation methods of the interrelationships of the meals include, but are not limited to, the relationships between participants and the purpose of the meal. For example, the diagnostic unit can assess stress levels by considering the relationships between participants in the meal. The diagnostic unit can improve the accuracy of its diagnosis by considering the combination of the meal location and participants. The diagnostic unit can adjust the diagnostic results by considering the frequency of the meals and the relationships between participants. For example, the diagnostic unit analyzes the relationships between participants in the meals and assesses stress levels. The diagnostic unit analyzes the combination of the meal location and participants to improve the accuracy of the diagnosis. The diagnostic unit analyzes the frequency of the meals and the relationships between participants to adjust the diagnostic results. This improves the accuracy of the diagnosis by considering the interrelationships of the meals. Some or all of the above processing in the diagnostic unit may be performed using, for example, AI, or not. For example, the diagnostic unit can input the interrelationship data of the meals into a generating AI, which can then automatically analyze the data and improve the accuracy of the diagnosis.

[0106] The report generation department can adjust the level of detail in a report based on the importance of the meal. Specific evaluation criteria and adjustment methods for importance include, but are not limited to, the purpose of the meal and the positions of the participants. For example, the report generation department can create a detailed report for a highly important meal. For less important meals, the report generation department can create a concise report. The report generation department can dynamically adjust the level of detail in a report according to the importance of the meal. For example, the report generation department creates a detailed report for a highly important meal. For less important meals, the report generation department creates a concise report. The report generation department dynamically adjusts the level of detail in a report according to the importance of the meal. This allows for the creation of more appropriate reports by adjusting the level of detail based on the importance of the meal. Some or all of the above processing in the report generation department may be performed using, for example, AI, or not using AI. For example, the report creation department can input data on the importance of business dinners into a generating AI, which can then automatically analyze the data and adjust the level of detail in the report.

[0107] The Security Department can select a data encryption method based on user attributes. Specific types of encryption methods and selection criteria include, but are not limited to, AES and RSA. The Security Department can select the optimal encryption method considering, for example, the user's age and gender. The Security Department can adjust the encryption method considering the user's occupation and lifestyle. The Security Department can select an encryption method considering the user's health status. For example, the Security Department analyzes the user's age and gender and selects the optimal encryption method. The Security Department analyzes the user's occupation and lifestyle and adjusts the encryption method. The Security Department analyzes the user's health status and selects an encryption method. This strengthens data security by selecting the optimal encryption method based on user attributes. Some or all of the above processing in the Security Department may be performed using, for example, AI, or not. For example, the Security Department can input user attribute data into a generating AI, which can automatically analyze the data and select the optimal encryption method.

[0108] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring meal plans based on the estimated emotions. Specific methods and criteria for estimating user emotions include, but are not limited to, facial expression recognition and voice analysis. For example, the acquisition unit can capture the user's facial expression with a camera and estimate their emotions using facial expression recognition technology. Facial expression recognition technology includes algorithms that analyze changes in facial expressions and estimate emotions. The acquisition unit can record the user's voice and estimate their emotions using voice analysis technology. Voice analysis technology includes algorithms that analyze the tone and speed of the voice and estimate emotions. The acquisition unit can delay acquiring meal plans if the user is feeling stressed. For example, if the acquisition unit determines from the user's facial expression or voice that they are feeling stressed, it will delay acquiring meal plans. The acquisition unit can accelerate acquiring meal plans if the user is relaxed. For example, if the acquisition unit determines from the user's facial expression or voice that they are relaxed, it will accelerate acquiring meal plans. The acquisition unit can instantly retrieve meal schedules if the user is in a hurry. For example, if the acquisition unit determines from the user's facial expressions or voice that the user is in a hurry, it will instantly retrieve the meal schedule. This allows for more appropriate timing of meal schedule acquisition by adjusting the timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's facial expressions and voice data into the generative AI, which can automatically estimate emotions and adjust the timing of meal schedule acquisition.

[0109] The recording unit can estimate the user's emotions and adjust the timing of prompting for detailed input about the meal based on the estimated emotions. Specific details and methods of input include, but are not limited to, input fields and input formats. For example, if the user is feeling stressed, the recording unit can postpone the input of detailed information about the meal. If the user is relaxed, the recording unit can expedite the input of detailed information. If the user is in a hurry, the recording unit can provide a simplified input form. For example, if the recording unit determines from the user's facial expressions and voice that they are feeling stressed, it will postpone the input of detailed information about the meal. If the recording unit determines from the user's facial expressions and voice that they are relaxed, it will expedite the input of detailed information. If the recording unit determines from the user's facial expressions and voice that they are in a hurry, it will provide a simplified input form. This allows for more appropriate timing of detailed input by adjusting the timing of prompting for detailed information about the meal based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the recording unit may be performed using AI, or not using AI. For example, the recording unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the timing for prompting for detailed input of the meal.

[0110] The diagnostic unit can estimate the user's emotions and adjust the diagnostic criteria based on the estimated emotions. Specific details of the diagnostic criteria and adjustment methods include, but are not limited to, evaluation items and evaluation criteria. For example, if the user is experiencing stress, the diagnostic unit can relax the diagnostic criteria. The diagnostic unit can tighten the diagnostic criteria when the user is relaxed. It can apply simplified diagnostic criteria when the user is in a hurry. For example, if the diagnostic unit determines from the user's facial expressions and voice that they are stressed, it will relax the diagnostic criteria. If the diagnostic unit determines from the user's facial expressions and voice that they are relaxed, it will tighten the diagnostic criteria. If the diagnostic unit determines from the user's facial expressions and voice that they are in a hurry, it will apply simplified diagnostic criteria. This allows for a more accurate diagnosis by adjusting the diagnostic criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the diagnostic unit may be performed using AI, for example, or not using AI. For example, the diagnostic unit can input the user's facial expressions and voice data into a generative AI, which can automatically estimate emotions and adjust the diagnostic criteria.

[0111] The reporting unit can estimate the user's emotions and adjust the report's presentation based on those emotions. Specific examples of adjustments include, but are not limited to, the tone of the writing and the vocabulary used. For example, if the user is stressed, the reporting unit can create a concise and easy-to-read report. If the user is relaxed, the reporting unit can create a detailed report. If the user is in a hurry, the reporting unit can create a to-the-point report. For instance, if the reporting unit determines from the user's facial expressions and voice that they are stressed, it will create a concise and easy-to-read report. If the reporting unit determines from the user's facial expressions and voice that they are relaxed, it will create a detailed report. If the reporting unit determines from the user's facial expressions and voice that they are in a hurry, it will create a to-the-point report. This allows for the creation of more appropriate reports by adjusting the report's presentation based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the report creation unit may be performed using AI, or not using AI. For example, the report creation unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the way the report is presented.

[0112] The calendar app integration unit can estimate the user's emotions and adjust the integration method with the calendar app based on the estimated emotions. Specific integration methods and adjustments include, but are not limited to, API integration and data synchronization. For example, if the user is stressed, the calendar app integration unit can provide a simple integration method. If the user is relaxed, it can provide detailed integration options. If the user is in a hurry, it can provide a method for quick integration completion. For example, if the calendar app integration unit determines from the user's facial expressions and voice that they are stressed, it provides a simple integration method. If the calendar app integration unit determines from the user's facial expressions and voice that they are relaxed, it provides detailed integration options. If the calendar app integration unit determines from the user's facial expressions and voice that they are in a hurry, it provides a method for quick integration completion. This allows for more appropriate integration by adjusting the integration method with the calendar app based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the calendar app integration unit may be performed using AI, or not using AI. For example, the calendar app integration unit can input the user's facial expressions and voice data into the generating AI, which can then automatically estimate emotions and adjust the method of integration with the calendar app.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The retrieval unit retrieves the dinner plans entered by the user in the calendar app. For example, the retrieval unit can automatically retrieve the user's dinner plans using the calendar app's API. The retrieval unit can also retrieve dinner plans that the user has manually entered. Step 2: The recording unit records the meal based on the meal schedule obtained by the acquisition unit. For example, the recording unit records details such as the location of the meal, participants, meal contents, and perceived stress level. The recording unit saves the information entered by the user to a database, which can be used later for diagnosis and report creation. Step 3: The diagnostic unit performs a diagnosis of the meal based on the data recorded by the recording unit. For example, the diagnostic unit evaluates the user's stress level and the progress of the meal, and provides advice for the next meal. The diagnostic unit can use AI to analyze the data and provide appropriate advice to the user. Step 4: The reporting department creates a report based on the diagnostic results obtained by the diagnostic department. For example, the reporting department creates a report that includes the user's progress in meals, changes in stress levels, and areas for improvement. The reporting department can create reports regularly and provide them to users and medical professionals.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0168] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A system characterized by comprising: an acquisition unit for acquiring dinner schedules; a recording unit for recording dinners based on the dinner schedules acquired by the acquisition unit; a diagnostic unit for diagnosing dinners based on the data recorded by the recording unit; and a report generation unit for creating a report based on the diagnostic results obtained by the diagnostic unit.

2. The system according to claim 1, characterized in that the recording unit records the specific details of the meal entered by the user.

3. The system according to claim 1, characterized in that the diagnostic unit evaluates the user's stress level and the progress of their meal based on the recorded data.

4. The system according to claim 1, characterized in that the report generation unit provides advice to the user based on the diagnostic results.

5. The system according to claim 1, characterized by comprising a security unit for protecting data security.

6. The system according to claim 1, characterized in that it automatically retrieves dinner schedules in conjunction with a calendar app.

7. The system according to claim 1, characterized in that the acquisition unit estimates the user's emotions and adjusts the timing of acquiring the meal schedule based on the estimated user's emotions.

8. The system according to claim 1, characterized in that the acquisition unit analyzes the user's past dining history and selects an appropriate acquisition method.

9. The system according to claim 1, characterized in that the acquisition unit performs filtering based on the user's current lifestyle and areas of interest when acquiring meal plans.

10. The system according to claim 1, characterized in that the acquisition unit estimates the user's emotions and determines the priority of meal plans to be acquired based on the estimated user's emotions.

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