system

Abstract

The system according to the embodiment aims to provide efficient and appropriate medical services to elderly people even when they have difficulty going to the hospital on their own. [Solution] The system according to the embodiment comprises an acquisition unit, a collection unit, a calculation unit, an operation unit, an analysis unit, a selection unit, and a provision unit. The acquisition unit acquires bus reservation status. The collection unit collects congestion prediction data. The calculation unit calculates a route based on the data acquired by the acquisition unit and the collection unit. The operation unit operates the bus based on the route calculated by the calculation unit. The analysis unit analyzes the medical record after the consultation. The selection unit selects a medicine based on the results of the analysis by the analysis unit. The provision unit provides the medicine selected by the selection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide efficient and appropriate medical services to elderly people who find it difficult to visit hospitals on their own.

[0005] The system according to the embodiment aims to provide efficient and appropriate medical services to elderly people even when they have difficulty going to the hospital on their own. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a collection unit, a calculation unit, an operation unit, an analysis unit, a selection unit, and a provision unit. The acquisition unit acquires bus reservation status. The collection unit collects congestion prediction data. The calculation unit calculates a route based on the data acquired by the acquisition unit and the collection unit. The operation unit operates a bus based on the route calculated by the calculation unit. The analysis unit analyzes the medical record after the consultation. The selection unit selects a medicine based on the results of the analysis by the analysis unit. The provision unit provides the medicine selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide efficient and appropriate medical services to elderly people even when they have difficulty going to the hospital on their own. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hospital visit support system according to an embodiment of the present invention is designed to address cases where elderly people have difficulty going to the hospital on their own. This hospital visit support system combines autonomous driving technology and generation AI to operate a shuttle bus from home to the hospital. First, the user makes a bus reservation, and the generation AI combines reservation status and traffic congestion forecasts to calculate the optimal route and create a bus schedule. Next, the bus operates using autonomous driving technology, safely transporting the user from home to the hospital. After the consultation, the generation AI analyzes the patient's medical records and selects the appropriate medication. Finally, the bus resumes operation upon the user's return home, allowing the user to collect their medication upon returning home. This system facilitates hospital visits for elderly people and improves the quality of medical services. For example, when a user makes a bus reservation, the generation AI combines reservation status and traffic congestion forecasts to calculate the optimal route and create a bus schedule. Next, the bus operates using autonomous driving technology to safely transport the user from home to the hospital. After the consultation, the generation AI analyzes the patient's medical records and selects the appropriate medication. Finally, the bus resumes operation upon the user's return home, allowing the user to collect their medication upon returning home. This system will make it easier for elderly people to visit hospitals and improve the quality of medical services.This system will make it easier for elderly people to visit hospitals and improve the quality of medical services.

[0029] The hospital visit support system according to the embodiment includes an acquisition unit, a collection unit, a calculation unit, an operation unit, an analysis unit, a selection unit, and a provision unit. The acquisition unit acquires the reservation status when a user makes a bus reservation. For example, the acquisition unit acquires information entered into a reservation form when a user makes an online reservation. The acquisition unit can also acquire information entered by an operator in the case of a telephone reservation. The acquisition unit can also acquire reservation cancellation information. The collection unit collects traffic information and acquires congestion prediction data. For example, the collection unit collects real-time data from traffic sensors. The collection unit can also predict congestion based on past traffic data. The collection unit can also predict congestion taking weather information into account. The calculation unit uses a generation AI to calculate an optimal route based on the reservation status and congestion prediction data. For example, the calculation unit inputs the reservation status and congestion prediction data and calculates a route to reach the destination in the shortest time. The calculation unit can also calculate the most efficient route taking traffic conditions into account. The calculation unit can also calculate a scenic route according to the user's preferences. The operation department uses autonomous driving technology to operate buses based on calculated routes. For example, the operation department controls the autonomous driving system based on route information provided by the calculation department. The operation department can also monitor traffic conditions in real time and recalculate routes as necessary. Furthermore, the operation department is equipped with an obstacle detection system to ensure user safety. The analysis department uses a generation AI to analyze medical records after consultations. For example, the analysis department performs text analysis of the contents of the medical records and extracts diagnosis results and prescription information. The analysis department can also analyze trends in diagnosis results based on past medical record data. Furthermore, the analysis department can integrate and analyze multiple medical record data to comprehensively evaluate the user's health condition. The selection department uses a generation AI to select medications based on the analysis results. For example, the selection department selects the most appropriate medication based on the user's medical history and allergy information. The selection department can also select the optimal combination of medications from multiple medications based on side effects and drug interactions. Furthermore, the selection department can select generic medications according to the user's preferences.The providing unit provides the selected medicine to the user. For example, the providing unit prepares the medicine so that it can be picked up on a bus. The providing unit can also adjust the method of providing the medicine according to the user's wishes. Furthermore, the providing unit has a function of notifying the user of information regarding the provision of the medicine. As a result, the hospital visit support system according to the embodiment can make it easier for elderly people to visit hospitals and improve the quality of medical services.

[0030] The acquisition unit can analyze the user's past reservation history and select the optimal acquisition method. For example, the acquisition unit can automatically display time slots that the user has frequently made reservations for in the past as candidates. The acquisition unit can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. Furthermore, the acquisition unit can predict and suggest a tendency to make reservations on specific days of the week or time slots based on the user's past reservation history. In this way, by analyzing the past reservation history, the optimal reservation method can be provided to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past reservation data into the generation AI and have the generation AI select the optimal acquisition method.

[0031] When acquiring a reservation, the acquisition unit can perform filtering based on the user's current health condition and lifestyle. For example, if the user is in poor health, the acquisition unit can prioritize suggesting the earliest reservation slot. In addition, if the user has a specific lifestyle rhythm, the acquisition unit can also suggest a reservation slot that matches that rhythm. Furthermore, if the user has a specific health condition, the acquisition unit can also suggest a reservation slot that is suitable for that condition. This allows for more appropriate reservations by suggesting a reservation slot that matches the user's health condition and lifestyle. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's health condition data into the generation AI and have the generation AI perform filtering.

[0032] When acquiring a reservation, the acquisition unit can prioritize acquiring highly relevant reservations by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring reservation slots at bus stops closest to the user's current location. The acquisition unit can also propose optimal reservation slots by taking into account the distance from the user's home to the hospital. Furthermore, the acquisition unit can also propose the most efficient route based on the user's geographical location information. This enables more appropriate reservations by taking into account 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 may be performed without using AI. For example, the acquisition unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant reservations.

[0033] When acquiring a reservation, the acquisition unit can analyze the user's social media activity and acquire related reservations. For example, the acquisition unit prioritizes acquiring reservation slots for hospitals or medical departments mentioned by the user on social media. The acquisition unit can also predict and suggest a tendency to make reservations at specific time periods based on the user's social media activity. Furthermore, the acquisition unit can also suggest optimal reservation slots based on the user's social media activity. This enables more appropriate reservations by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to select related reservations.

[0034] The collection unit can analyze past traffic data and select the optimal collection method. For example, the collection unit can identify routes that are prone to congestion during specific time periods from the past traffic data and prioritize collecting information. The collection unit can also select the most efficient information collection method based on the past traffic data. Furthermore, the collection unit can analyze the past traffic data and select an information collection method that suits specific events or weather conditions. This enables more efficient information collection by analyzing past traffic data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past traffic data into the generation AI and have the generation AI select the optimal collection method.

[0035] When collecting traffic information, the collection unit can filter the data taking into account specific events and weather information. For example, when a specific event is being held, the collection unit prioritizes collecting traffic information in the surrounding area. The collection unit can also prioritize collecting traffic information during bad weather based on weather information. Furthermore, the collection unit can filter traffic information according to specific events and weather conditions and provide optimal information. This makes it possible to provide more appropriate traffic information by taking into account specific events and weather information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input event information and weather data into a generation AI and have the generation AI perform filtering.

[0036] When collecting traffic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting traffic information closest to the user's current location. The collection unit can also prioritize collecting traffic information related to the route from the user's home to the hospital. Furthermore, the collection unit can collect the most efficient traffic information based on the user's geographical location information. This makes it possible to provide more appropriate traffic information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant information.

[0037] The collection unit can analyze the user's social media activities and collect related information when collecting traffic information. For example, the collection unit prioritizes collecting traffic information mentioned by the user on social media. The collection unit can also collect traffic information related to a specific time period from the user's social media activities. Furthermore, the collection unit can collect optimal traffic information based on the user's social media activities. This makes it possible to provide more appropriate traffic information by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0038] The calculation unit can analyze past route data and select the optimal calculation method. For example, the calculation unit identifies and calculates the most efficient route for a specific time period from the past route data. The calculation unit can also select the most efficient calculation method based on the past route data. Furthermore, the calculation unit can analyze the past route data and select a calculation method that suits a specific event or weather conditions. This enables more efficient route calculation by analyzing the past route data. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input past route data into a generation AI and have the generation AI select the optimal calculation method.

[0039] The calculation unit can take specific events and weather information into consideration when calculating a route. For example, if a specific event is being held, the calculation unit can prioritize calculating a route around the event. The calculation unit can also prioritize calculating a route for bad weather based on weather information. Furthermore, the calculation unit can calculate a route according to specific events and weather conditions and provide an optimal route. This makes it possible to provide a more appropriate route by taking specific events and weather information into consideration. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input event information and weather data into the generation AI and have the generation AI perform route calculation.

[0040] When calculating a route, the calculation unit can calculate an optimal route by taking into account the user's geographical location information. The calculation unit, for example, calculates the most efficient route from the user's current location. The calculation unit can also calculate the optimal route by taking into account the distance from the user's home to the hospital. Furthermore, the calculation unit can calculate the most efficient route based on the user's geographical location information. This makes it possible to provide a more appropriate route by taking into account the user's geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's geographical location data into the generation AI and cause the generation AI to calculate the optimal route.

[0041] The calculation unit can analyze the user's social media activity and take related information into consideration when calculating a route. For example, the calculation unit can calculate a route taking into account places mentioned by the user on social media. The calculation unit can also calculate a route relevant to a specific time period from the user's social media activity. Furthermore, the calculation unit can calculate an optimal route based on the user's social media activity. This makes it possible to provide a more appropriate route by taking the user's social media activity into consideration. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input the user's social media data into the generation AI and cause the generation AI to calculate a route taking into account related information.

[0042] The operation unit can analyze past operation data and select the optimal operation method. For example, the operation unit identifies the most efficient operation method for a specific time period from the past operation data and operates the vehicle. The operation unit can also select the most efficient operation method based on the past operation data. Furthermore, the operation unit can analyze the past operation data and select an operation method that suits a specific event or weather conditions. In this way, analyzing the past operation data enables more efficient operation. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input past operation data into a generation AI and have the generation AI select the optimal operation method.

[0043] The operation unit can operate the vehicle while taking into consideration specific events and weather information. For example, when a specific event is being held, the operation unit prioritizes operation of routes in the vicinity of the event. The operation unit can also prioritize operation of routes in bad weather based on weather information. Furthermore, the operation unit can adjust the operation route and perform optimal operation in accordance with the specific event and weather conditions. This enables more appropriate operation by taking into consideration the specific event and weather information. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input event information and weather data into the generation AI and have the generation AI adjust the operation route.

[0044] During operation, the operation unit can select the optimal route by taking into account the user's geographical location information. For example, the operation unit selects the most efficient route from the user's current location. The operation unit can also select the optimal route by taking into account the distance from the user's home to the hospital. Furthermore, the operation unit can select the most efficient route based on the user's geographical location information. This enables more appropriate operation by taking into account the user's geographical location information. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal route.

[0045] The operation department can analyze the user's social media activity and take related information into consideration when operating the vehicle. For example, the operation department selects a route taking into consideration locations mentioned by the user on social media. The operation department can also select a route relevant to a specific time period based on the user's social media activity. Furthermore, the operation department can select an optimal route based on the user's social media activity. This enables more appropriate operation by taking the user's social media activity into consideration. Some or all of the above-described processing in the operation department can be performed using, for example, AI, or can be performed without using AI. For example, the operation department can input the user's social media data into a generation AI and have the generation AI select a route taking related information into consideration.

[0046] The analysis unit can analyze past medical record data and select the optimal analysis method. For example, the analysis unit selects an analysis method based on a specific medical history from the past medical record data. The analysis unit can also select the most efficient analysis method based on the past medical record data. Furthermore, the analysis unit can analyze the past medical record data and select an analysis method according to a specific health condition. This enables more efficient analysis by analyzing past medical record data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past medical record data into a generation AI and have the generation AI select the optimal analysis method.

[0047] The analysis unit can take into account specific medical history and health conditions when analyzing medical records. For example, the analysis unit may prioritize analysis of medical records of users with specific medical histories. The analysis unit can also select the optimal analysis method based on the user's health condition. Furthermore, the analysis unit can analyze medical records according to specific medical history and health conditions and provide optimal results. This enables more appropriate analysis by taking specific medical history and health conditions into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's medical history data into the generation AI and have the generation AI perform the analysis.

[0048] When analyzing medical records, the analysis unit can perform optimal analysis by taking into account the user's geographical location information. The analysis unit, for example, selects the most efficient analysis method based on the user's current location. The analysis unit can also select the optimal analysis method by taking into account the distance from the user's home to the hospital. Furthermore, the analysis unit can select the most efficient analysis method based on the user's geographical location information. This enables more appropriate analysis by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal analysis method.

[0049] When analyzing the medical record, the analysis unit can analyze the user's social media activity and take related information into consideration when performing the analysis. For example, the analysis unit can analyze the medical record taking into consideration health information mentioned by the user on social media. The analysis unit can also analyze the medical record by taking into consideration information related to a specific health condition from the user's social media activity. Furthermore, the analysis unit can select the optimal analysis method based on the user's social media activity. This enables more appropriate analysis by taking the user's social media activity into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into the generation AI and have the generation AI perform an analysis taking into consideration related information.

[0050] The selection unit can analyze past drug selection data and select the optimal selection method. For example, the selection unit selects a selection method based on a specific medical history from the past drug selection data. The selection unit can also select the most efficient selection method based on the past drug selection data. Furthermore, the selection unit can analyze past drug selection data and select a selection method according to a specific health condition. This enables more efficient drug selection by analyzing past drug selection data. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past drug selection data into the generation AI and have the generation AI select the optimal selection method.

[0051] The selection unit can select a medicine taking into consideration a specific medical history or health condition. For example, the selection unit preferentially selects medicines for users with a specific medical history. The selection unit can also select the most appropriate medicine based on the user's health condition. Furthermore, the selection unit can select medicines according to a specific medical history or health condition and provide optimal results. This makes it possible to provide more appropriate medicines by taking into consideration a specific medical history or health condition. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's medical history data into the generation AI and have the generation AI select a medicine.

[0052] When selecting a medicine, the selection unit can select the most appropriate medicine by taking into account the user's geographical location information. For example, the selection unit selects the most efficient medicine based on the user's current location. The selection unit can also select the most appropriate medicine by taking into account the distance from the user's home to the hospital. Furthermore, the selection unit can select the most efficient medicine based on the user's geographical location information. This makes it possible to provide a more appropriate medicine by taking into account the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal medicine.

[0053] When selecting a medicine, the selection unit can analyze the user's social media activity and take related information into consideration. For example, the selection unit selects a medicine by taking into consideration health information mentioned by the user on social media. The selection unit can also select a medicine by taking into consideration information related to a specific health condition from the user's social media activity. Furthermore, the selection unit can select an optimal medicine based on the user's social media activity. This makes it possible to provide a more appropriate medicine by taking into consideration the user's social media activity. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media data into the generation AI and cause the generation AI to select a medicine by taking into consideration related information.

[0054] The provision unit can analyze past drug delivery data and select the optimal delivery method. For example, the provision unit selects a delivery method based on a specific medical history from the past drug delivery data. The provision unit can also select the most efficient delivery method based on the past drug delivery data. Furthermore, the provision unit can analyze the past drug delivery data and select a delivery method according to a specific health condition. This enables more efficient drug delivery by analyzing the past drug delivery data. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input past drug delivery data into a generation AI and cause the generation AI to select the optimal delivery method.

[0055] The provision unit can provide medicine taking into consideration a specific health condition or lifestyle situation. For example, the provision unit provides the most appropriate medicine to a user with a specific health condition. The provision unit can also adjust the timing of medicine provision to match the user's lifestyle. Furthermore, the provision unit can customize the method of providing medicine depending on the user's health condition or lifestyle situation. This makes it possible to provide more appropriate medicine by taking into consideration the specific health condition or lifestyle situation. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's health condition data into the generation AI and have the generation AI adjust the delivery method.

[0056] When providing medicine, the provision unit can select the optimal delivery method by taking into account the user's geographical location information. For example, the provision unit selects the most efficient drug delivery method based on the user's current location. The provision unit can also select the optimal drug delivery method by taking into account the distance from the user's home to the hospital. Furthermore, the provision unit can select the most efficient drug delivery method based on the user's geographical location information. This makes it possible to provide more appropriate medicine by taking into account the user's geographical location information. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal delivery method.

[0057] The provision unit can analyze the user's social media activity and provide the medicine while taking into consideration related information. For example, the provision unit can provide the medicine while taking into consideration health information mentioned by the user on social media. The provision unit can also provide the medicine while taking into consideration information related to a specific health condition from the user's social media activity. Furthermore, the provision unit can provide the optimal medicine based on the user's social media activity. This makes it possible to provide a more appropriate medicine by taking into consideration the user's social media activity. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's social media data into the generation AI and cause the generation AI to provide the medicine while taking into consideration related information.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The acquisition unit can analyze the user's voice data and enable reservations to be made via voice commands. For example, when a user makes a reservation via voice, the acquisition unit uses voice recognition technology to analyze the reservation details and complete the reservation procedure. The acquisition unit can also change or cancel the reservation based on the voice command. Furthermore, the acquisition unit can analyze the voice data, infer the user's intention, and suggest optimal reservation options. In this way, utilizing voice data improves user convenience and enables a smoother reservation procedure.

[0060] The acquisition unit can automatically generate an optimal reservation plan based on the user's past reservation history. For example, the acquisition unit can analyze the time periods and days of the week that the user has frequently used in the past and propose an optimal reservation plan. The acquisition unit can also preferentially propose specific doctors or medical departments based on the user's past reservation history. Furthermore, the acquisition unit can automatically generate an optimal hospital visit schedule based on the user's past reservation history. This makes it possible to provide more efficient and personalized reservation plans by utilizing the user's past reservation history.

[0061] The acquisition unit can dynamically change reservation priorities based on the user's current health condition and lifestyle. For example, if the user suddenly becomes unwell, the acquisition unit can prioritize the earliest reservation slot. The acquisition unit can also flexibly adjust reservation slots to suit the user's lifestyle. Furthermore, the acquisition unit can also make it easy to cancel or change reservations depending on the user's health condition and lifestyle. This enables flexible reservation management according to the user's health condition and lifestyle.

[0062] The acquisition unit can suggest the optimal bus stop location based on the user's geographical location information. For example, the acquisition unit can automatically search for and suggest the bus stop closest to the user's current location. The acquisition unit can also suggest the optimal bus stop location taking into account the route from the user's home to the hospital. Furthermore, the acquisition unit can also suggest the optimal bus stop based on the user's geographical location information and taking into account the bus stop's congestion status. This makes it possible to select a more efficient and convenient bus stop by utilizing the user's geographical location information.

[0063] The acquisition unit can analyze the user's social media activity and make reservation suggestions based on the user's interests. For example, the acquisition unit can prioritize suggestions of medical facilities or departments mentioned by the user on social media. The acquisition unit can also predict and suggest a tendency to make reservations at specific time periods or days of the week based on the user's social media activity. Furthermore, the acquisition unit can suggest reservation options tailored to the user's interests based on the user's social media activity. This makes it possible to make more personalized reservation suggestions by utilizing the user's social media activity.

[0064] The collection unit can optimize the method of collecting traffic information according to specific events or weather conditions based on past traffic data. For example, the collection unit can analyze traffic patterns when specific events are held from past data and collect information on a priority basis. The collection unit can also prioritize collecting traffic information during bad weather based on past weather data. Furthermore, the collection unit can analyze past traffic data and select the most efficient information collection method for specific time periods or days of the week. This makes it possible to collect traffic information more efficiently and effectively by utilizing past data.

[0065] When collecting traffic information, the collection unit can select the optimal information collection point based on the user's geographical location information. For example, the collection unit automatically selects the traffic information collection point closest to the user's current location. The collection unit can also select the optimal information collection point taking into account the user's route from their home to the hospital. Furthermore, the collection unit can dynamically adjust the traffic information collection range based on the user's geographical location information. This makes it possible to collect traffic information more efficiently and effectively by utilizing the user's geographical location information.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The acquisition unit acquires the reservation status when a user makes a bus reservation. For example, the acquisition unit acquires the information entered into the reservation form when a user makes an online reservation. The acquisition unit can also acquire information entered by an operator in the case of a telephone reservation. Furthermore, the acquisition unit can also acquire information on reservation cancellations. Step 2: The collection unit collects traffic information and obtains congestion prediction data. For example, the collection unit collects real-time data from traffic sensors. The collection unit can also make congestion predictions based on past traffic data. Furthermore, the collection unit can also make congestion predictions taking weather information into account. Step 3: The calculation unit uses the generation AI to calculate the optimal route based on the reservation status and traffic congestion forecast data. For example, the calculation unit inputs the reservation status and traffic congestion forecast data and calculates the route to reach the destination in the shortest time. The calculation unit can also calculate the most efficient route taking into account traffic conditions. Furthermore, the calculation unit can also calculate a scenic route according to the user's preference. Step 4: The operation department uses autonomous driving technology to operate the bus based on the calculated route. For example, the operation department controls the autonomous driving system based on the route information provided by the calculation department. The operation department can also monitor traffic conditions in real time and recalculate the route as necessary. Furthermore, the operation department is equipped with an obstacle detection system to ensure user safety. Step 5: The analysis unit uses the generation AI to analyze the medical record after the consultation. For example, the analysis unit performs text analysis of the contents of the medical record and extracts diagnosis results and prescription information. The analysis unit can also analyze trends in diagnosis results based on past medical record data. Furthermore, the analysis unit can integrate and analyze multiple medical record data to comprehensively evaluate the user's health condition. Step 6: The selection unit uses the generative AI to select a drug based on the analysis results. For example, the selection unit selects the most appropriate drug taking into account the user's medical history and allergy information. The selection unit can also select the optimal combination from multiple drugs taking into account drug side effects and drug interactions. Furthermore, the selection unit can also select generic drugs according to the user's preference. Step 7: The providing unit provides the selected medicine to the user. For example, the providing unit prepares the medicine so that it can be picked up on the bus. The providing unit can also adjust the method of providing the medicine according to the user's preference. Furthermore, the providing unit has a function of notifying the user of information regarding the provision of the medicine.

[0068] (Example 2) A hospital visit support system according to an embodiment of the present invention is designed to address cases where elderly people have difficulty going to the hospital on their own. This hospital visit support system combines autonomous driving technology and generation AI to operate a shuttle bus from home to the hospital. First, the user makes a bus reservation, and the generation AI combines reservation status and traffic congestion forecasts to calculate the optimal route and create a bus schedule. Next, the bus operates using autonomous driving technology, safely transporting the user from home to the hospital. After the consultation, the generation AI analyzes the patient's medical records and selects the appropriate medication. Finally, the bus resumes operation upon the user's return home, allowing the user to collect their medication upon returning home. This system facilitates hospital visits for elderly people and improves the quality of medical services. For example, when a user makes a bus reservation, the generation AI combines reservation status and traffic congestion forecasts to calculate the optimal route and create a bus schedule. Next, the bus operates using autonomous driving technology to safely transport the user from home to the hospital. After the consultation, the generation AI analyzes the patient's medical records and selects the appropriate medication. Finally, the bus resumes operation upon the user's return home, allowing the user to collect their medication upon returning home. This system will make it easier for elderly people to visit hospitals and improve the quality of medical services.This system will make it easier for elderly people to visit hospitals and improve the quality of medical services.

[0069] The hospital visit support system according to the embodiment includes an acquisition unit, a collection unit, a calculation unit, an operation unit, an analysis unit, a selection unit, and a provision unit. The acquisition unit acquires the reservation status when a user makes a bus reservation. For example, the acquisition unit acquires information entered into a reservation form when a user makes an online reservation. The acquisition unit can also acquire information entered by an operator in the case of a telephone reservation. The acquisition unit can also acquire reservation cancellation information. The collection unit collects traffic information and acquires congestion prediction data. For example, the collection unit collects real-time data from traffic sensors. The collection unit can also predict congestion based on past traffic data. The collection unit can also predict congestion taking weather information into account. The calculation unit uses a generation AI to calculate an optimal route based on the reservation status and congestion prediction data. For example, the calculation unit inputs the reservation status and congestion prediction data and calculates a route to reach the destination in the shortest time. The calculation unit can also calculate the most efficient route taking traffic conditions into account. The calculation unit can also calculate a scenic route according to the user's preferences. The operation department uses autonomous driving technology to operate buses based on calculated routes. For example, the operation department controls the autonomous driving system based on route information provided by the calculation department. The operation department can also monitor traffic conditions in real time and recalculate routes as necessary. Furthermore, the operation department is equipped with an obstacle detection system to ensure user safety. The analysis department uses a generation AI to analyze medical records after consultations. For example, the analysis department performs text analysis of the contents of the medical records and extracts diagnosis results and prescription information. The analysis department can also analyze trends in diagnosis results based on past medical record data. Furthermore, the analysis department can integrate and analyze multiple medical record data to comprehensively evaluate the user's health condition. The selection department uses a generation AI to select medications based on the analysis results. For example, the selection department selects the most appropriate medication based on the user's medical history and allergy information. The selection department can also select the optimal combination of medications from multiple medications based on side effects and drug interactions. Furthermore, the selection department can select generic medications according to the user's preferences.The providing unit provides the selected medicine to the user. For example, the providing unit prepares the medicine so that it can be picked up on a bus. The providing unit can also adjust the method of providing the medicine according to the user's wishes. Furthermore, the providing unit has a function of notifying the user of information regarding the provision of the medicine. As a result, the hospital visit support system according to the embodiment can make it easier for elderly people to visit hospitals and improve the quality of medical services.

[0070] The acquisition unit can estimate the user's emotions and adjust the timing of a bus reservation based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can simplify the reservation procedure and allow the reservation to be completed quickly. Furthermore, if the user is relaxed, the acquisition unit can provide detailed reservation options and suggest customizable reservation methods. Furthermore, if the user is in a hurry, the acquisition unit can prioritize voice input and allow the reservation to be completed quickly. This allows for more appropriate reservations by adjusting the reservation timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0071] The acquisition unit can analyze the user's past reservation history and select the optimal acquisition method. For example, the acquisition unit can automatically display time slots that the user has frequently made reservations for in the past as candidates. The acquisition unit can also preferentially suggest reservation methods (online, telephone, etc.) that the user has used in the past. Furthermore, the acquisition unit can predict and suggest a tendency to make reservations on specific days of the week or time slots based on the user's past reservation history. In this way, by analyzing the past reservation history, the optimal reservation method can be provided to the user. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past reservation data into the generation AI and have the generation AI select the optimal acquisition method.

[0072] When acquiring a reservation, the acquisition unit can perform filtering based on the user's current health condition and lifestyle. For example, if the user is in poor health, the acquisition unit can prioritize suggesting the earliest reservation slot. In addition, if the user has a specific lifestyle rhythm, the acquisition unit can also suggest a reservation slot that matches that rhythm. Furthermore, if the user has a specific health condition, the acquisition unit can also suggest a reservation slot that is suitable for that condition. This allows for more appropriate reservations by suggesting a reservation slot that matches the user's health condition and lifestyle. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's health condition data into the generation AI and have the generation AI perform filtering.

[0073] The acquisition unit can estimate the user's emotions and determine the priority of reservations to be acquired based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can prioritize acquiring the earliest reservation slot. Furthermore, if the user is relaxed, the acquisition unit can also suggest a reservation slot that meets the user's preferences. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring the reservation slot that can be accommodated most quickly. This enables more appropriate reservations by determining the priority of reservations according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0074] When acquiring a reservation, the acquisition unit can prioritize acquiring highly relevant reservations by taking into account the user's geographical location information. For example, the acquisition unit prioritizes acquiring reservation slots at bus stops closest to the user's current location. The acquisition unit can also propose optimal reservation slots by taking into account the distance from the user's home to the hospital. Furthermore, the acquisition unit can also propose the most efficient route based on the user's geographical location information. This enables more appropriate reservations by taking into account 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 may be performed without using AI. For example, the acquisition unit can input the user's geographical location data into the generation AI and cause the generation AI to select highly relevant reservations.

[0075] When acquiring a reservation, the acquisition unit can analyze the user's social media activity and acquire related reservations. For example, the acquisition unit prioritizes acquiring reservation slots for hospitals or medical departments mentioned by the user on social media. The acquisition unit can also predict and suggest a tendency to make reservations at specific time periods based on the user's social media activity. Furthermore, the acquisition unit can also suggest optimal reservation slots based on the user's social media activity. This enables more appropriate reservations by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's social media data into the generation AI and cause the generation AI to select related reservations.

[0076] The collection unit can estimate the user's emotions and adjust the timing of traffic information collection based on the estimated user emotions. For example, when the user is stressed, the collection unit can quickly collect traffic information and provide the latest information. Furthermore, when the user is relaxed, the collection unit can periodically collect traffic information and provide stable information. Furthermore, when the user is in a hurry, the collection unit can collect traffic information in real time and provide it immediately. This allows for adjusting the timing of traffic information collection according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] The collection unit can analyze past traffic data and select the optimal collection method. For example, the collection unit can identify routes that are prone to congestion during specific time periods from the past traffic data and prioritize collecting information. The collection unit can also select the most efficient information collection method based on the past traffic data. Furthermore, the collection unit can analyze the past traffic data and select an information collection method that suits specific events or weather conditions. This enables more efficient information collection by analyzing past traffic data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input past traffic data into the generation AI and have the generation AI select the optimal collection method.

[0078] When collecting traffic information, the collection unit can filter the data taking into account specific events and weather information. For example, when a specific event is being held, the collection unit prioritizes collecting traffic information in the surrounding area. The collection unit can also prioritize collecting traffic information during bad weather based on weather information. Furthermore, the collection unit can filter traffic information according to specific events and weather conditions and provide optimal information. This makes it possible to provide more appropriate traffic information by taking into account specific events and weather information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input event information and weather data into a generation AI and have the generation AI perform filtering.

[0079] The collection unit can estimate the user's emotions and determine the priority of traffic information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting the most important traffic information. The collection unit can also collect and provide detailed traffic information when the user is relaxed. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting traffic information that can be handled most quickly. This allows for more appropriate information to be provided by determining the priority of traffic information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0080] When collecting traffic information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting traffic information closest to the user's current location. The collection unit can also prioritize collecting traffic information related to the route from the user's home to the hospital. Furthermore, the collection unit can collect the most efficient traffic information based on the user's geographical location information. This makes it possible to provide more appropriate traffic information by taking the user's geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location data to the generation AI and cause the generation AI to collect highly relevant information.

[0081] The collection unit can analyze the user's social media activities and collect related information when collecting traffic information. For example, the collection unit prioritizes collecting traffic information mentioned by the user on social media. The collection unit can also collect traffic information related to a specific time period from the user's social media activities. Furthermore, the collection unit can collect optimal traffic information based on the user's social media activities. This makes it possible to provide more appropriate traffic information by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media data into the generation AI and cause the generation AI to collect related information.

[0082] The calculation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the calculation unit can prioritize calculating the shortest route. Furthermore, if the user is relaxed, the calculation unit can prioritize calculating a scenic route. Furthermore, if the user is in a hurry, the calculation unit can prioritize calculating the quickest route. This allows the route calculation algorithm to be adjusted according to the user's emotions, providing a more appropriate route. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit can be performed using, for example, an AI, or without an AI. For example, the calculation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0083] The calculation unit can analyze past route data and select the optimal calculation method. For example, the calculation unit identifies and calculates the most efficient route for a specific time period from the past route data. The calculation unit can also select the most efficient calculation method based on the past route data. Furthermore, the calculation unit can analyze the past route data and select a calculation method that suits a specific event or weather conditions. This enables more efficient route calculation by analyzing the past route data. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input past route data into a generation AI and have the generation AI select the optimal calculation method.

[0084] The calculation unit can take specific events and weather information into consideration when calculating a route. For example, if a specific event is being held, the calculation unit can prioritize calculating a route around the event. The calculation unit can also prioritize calculating a route for bad weather based on weather information. Furthermore, the calculation unit can calculate a route according to specific events and weather conditions and provide an optimal route. This makes it possible to provide a more appropriate route by taking specific events and weather information into consideration. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input event information and weather data into the generation AI and have the generation AI perform route calculation.

[0085] The calculation unit can estimate the user's emotion and adjust the display method of the calculation results based on the estimated user emotion. For example, if the user is stressed, the calculation unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the calculation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the calculation unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the calculation results according to the user's emotion, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calculation unit can be performed using, for example, an AI, or without an AI. For example, the calculation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0086] When calculating a route, the calculation unit can calculate an optimal route by taking into account the user's geographical location information. The calculation unit, for example, calculates the most efficient route from the user's current location. The calculation unit can also calculate the optimal route by taking into account the distance from the user's home to the hospital. Furthermore, the calculation unit can calculate the most efficient route based on the user's geographical location information. This makes it possible to provide a more appropriate route by taking into account the user's geographical location information. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the calculation unit can input the user's geographical location data into the generation AI and cause the generation AI to calculate the optimal route.

[0087] The calculation unit can analyze the user's social media activity and take related information into consideration when calculating a route. For example, the calculation unit can calculate a route taking into account places mentioned by the user on social media. The calculation unit can also calculate a route relevant to a specific time period from the user's social media activity. Furthermore, the calculation unit can calculate an optimal route based on the user's social media activity. This makes it possible to provide a more appropriate route by taking the user's social media activity into consideration. Some or all of the above-described processing in the calculation unit can be performed using, for example, AI, or can be performed without using AI. For example, the calculation unit can input the user's social media data into the generation AI and cause the generation AI to calculate a route taking into account related information.

[0088] The operation unit can estimate the user's emotions and adjust the operation schedule based on the estimated user emotions. For example, if the user is feeling stressed, the operation unit can prioritize the earliest operation schedule. Furthermore, if the user is relaxed, the operation unit can also suggest an operation schedule that meets the user's preferences. Furthermore, if the user is in a hurry, the operation unit can prioritize an operation schedule that can accommodate the user's needs most quickly. This allows for more appropriate operation by adjusting the operation schedule according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the operation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the operation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] The operation unit can analyze past operation data and select the optimal operation method. For example, the operation unit identifies the most efficient operation method for a specific time period from the past operation data and operates the vehicle. The operation unit can also select the most efficient operation method based on the past operation data. Furthermore, the operation unit can analyze the past operation data and select an operation method that suits a specific event or weather conditions. In this way, analyzing the past operation data enables more efficient operation. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input past operation data into a generation AI and have the generation AI select the optimal operation method.

[0090] The operation unit can operate the vehicle while taking into consideration specific events and weather information. For example, when a specific event is being held, the operation unit prioritizes operation of routes in the vicinity of the event. The operation unit can also prioritize operation of routes in bad weather based on weather information. Furthermore, the operation unit can adjust the operation route and perform optimal operation in accordance with the specific event and weather conditions. This enables more appropriate operation by taking into consideration the specific event and weather information. Some or all of the above-mentioned processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input event information and weather data into the generation AI and have the generation AI adjust the operation route.

[0091] The operation unit can estimate the user's emotions and prioritize routes based on the estimated user emotions. For example, if the user is feeling stressed, the operation unit can prioritize the fastest route. Furthermore, if the user is relaxed, the operation unit can prioritize the scenic route. Furthermore, if the user is in a hurry, the operation unit can prioritize the quickest route. This enables more appropriate operation by prioritizing routes according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the operation unit can be performed using, for example, an AI, or without an AI. For example, the operation unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] During operation, the operation unit can select the optimal route by taking into account the user's geographical location information. For example, the operation unit selects the most efficient route from the user's current location. The operation unit can also select the optimal route by taking into account the distance from the user's home to the hospital. Furthermore, the operation unit can select the most efficient route based on the user's geographical location information. This enables more appropriate operation by taking into account the user's geographical location information. Some or all of the above-described processing in the operation unit may be performed using, for example, AI, or may be performed without using AI. For example, the operation unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal route.

[0093] The operation department can analyze the user's social media activity and take related information into consideration when operating the vehicle. For example, the operation department selects a route taking into consideration locations mentioned by the user on social media. The operation department can also select a route relevant to a specific time period based on the user's social media activity. Furthermore, the operation department can select an optimal route based on the user's social media activity. This enables more appropriate operation by taking the user's social media activity into consideration. Some or all of the above-described processing in the operation department can be performed using, for example, AI, or can be performed without using AI. For example, the operation department can input the user's social media data into a generation AI and have the generation AI select a route taking related information into consideration.

[0094] The analysis unit can estimate the user's emotions and adjust the medical record analysis algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can quickly analyze the medical record and provide the results. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis and provide the results. Furthermore, if the user is in a hurry, the analysis unit can perform a key analysis and provide the results. This enables more appropriate analysis by adjusting the medical record analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0095] The analysis unit can analyze past medical record data and select the optimal analysis method. For example, the analysis unit selects an analysis method based on a specific medical history from the past medical record data. The analysis unit can also select the most efficient analysis method based on the past medical record data. Furthermore, the analysis unit can analyze the past medical record data and select an analysis method according to a specific health condition. This enables more efficient analysis by analyzing past medical record data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past medical record data into a generation AI and have the generation AI select the optimal analysis method.

[0096] The analysis unit can take into account specific medical history and health conditions when analyzing medical records. For example, the analysis unit may prioritize analysis of medical records of users with specific medical histories. The analysis unit can also select the optimal analysis method based on the user's health condition. Furthermore, the analysis unit can analyze medical records according to specific medical history and health conditions and provide optimal results. This enables more appropriate analysis by taking specific medical history and health conditions into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's medical history data into the generation AI and have the generation AI perform the analysis.

[0097] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0098] When analyzing medical records, the analysis unit can perform optimal analysis by taking into account the user's geographical location information. The analysis unit, for example, selects the most efficient analysis method based on the user's current location. The analysis unit can also select the optimal analysis method by taking into account the distance from the user's home to the hospital. Furthermore, the analysis unit can select the most efficient analysis method based on the user's geographical location information. This enables more appropriate analysis by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's geographical location data into the generation AI and have the generation AI select the optimal analysis method.

[0099] When analyzing the medical record, the analysis unit can analyze the user's social media activity and take related information into consideration when performing the analysis. For example, the analysis unit can analyze the medical record taking into consideration health information mentioned by the user on social media. The analysis unit can also analyze the medical record by taking into consideration information related to a specific health condition from the user's social media activity. Furthermore, the analysis unit can select the optimal analysis method based on the user's social media activity. This enables more appropriate analysis by taking the user's social media activity into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's social media data into the generation AI and have the generation AI perform an analysis taking into consideration related information.

[0100] The selection unit can estimate the user's emotions and adjust the medicine selection algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can quickly select and provide a medicine. Furthermore, if the user is relaxed, the selection unit can also select a medicine based on detailed information. Furthermore, if the user is in a hurry, the selection unit can select a medicine based on key points. This allows the medicine selection algorithm to be adjusted according to the user's emotions, thereby providing a more appropriate medicine. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0101] The selection unit can analyze past drug selection data and select the optimal selection method. For example, the selection unit selects a selection method based on a specific medical history from the past drug selection data. The selection unit can also select the most efficient selection method based on the past drug selection data. Furthermore, the selection unit can analyze past drug selection data and select a selection method according to a specific health condition. This enables more efficient drug selection by analyzing past drug selection data. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input past drug selection data into the generation AI and have the generation AI select the optimal selection method.

[0102] The selection unit can select a medicine taking into consideration a specific medical history or health condition. For example, the selection unit preferentially selects medicines for users with a specific medical history. The selection unit can also select the most appropriate medicine based on the user's health condition. Furthermore, the selection unit can select medicines according to a specific medical history or health condition and provide optimal results. This makes it possible to provide more appropriate medicines by taking into consideration a specific medical history or health condition. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's medical history data into the generation AI and have the generation AI select a medicine.

[0103] The selection unit can estimate the user's emotions and adjust the display method of the selection results based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the selection unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the selection unit can provide a display method that focuses on the main points. This allows for adjusting the display method of the selection results according to the user's emotions, thereby providing more appropriate information. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the selection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0104] When selecting a medicine, the selection unit can select the most appropriate medicine by taking into account the user's geographical location information. For example, the selection unit selects the most efficient medicine based on the user's current location. The selection unit can also select the most appropriate medicine by taking into account the distance from the user's home to the hospital. Furthermore, the selection unit can select the most efficient medicine based on the user's geographical location information. This makes it possible to provide a more appropriate medicine by taking into account the user's geographical location information. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal medicine.

[0105] When selecting a medicine, the selection unit can analyze the user's social media activity and take related information into consideration. For example, the selection unit selects a medicine by taking into consideration health information mentioned by the user on social media. The selection unit can also select a medicine by taking into consideration information related to a specific health condition from the user's social media activity. Furthermore, the selection unit can select an optimal medicine based on the user's social media activity. This makes it possible to provide a more appropriate medicine by taking into consideration the user's social media activity. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the user's social media data into the generation AI and cause the generation AI to select a medicine by taking into consideration related information.

[0106] The providing unit can estimate the user's emotions and adjust the method of providing medicine based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide medicine quickly. Furthermore, if the user is relaxed, the providing unit can provide medicine based on detailed information. Furthermore, if the user is in a hurry, the providing unit can provide medicine based on key points. This allows for adjusting the method of providing medicine according to the user's emotions, thereby providing more appropriate medicine. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0107] The provision unit can analyze past drug delivery data and select the optimal delivery method. For example, the provision unit selects a delivery method based on a specific medical history from the past drug delivery data. The provision unit can also select the most efficient delivery method based on the past drug delivery data. Furthermore, the provision unit can analyze the past drug delivery data and select a delivery method according to a specific health condition. This enables more efficient drug delivery by analyzing the past drug delivery data. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input past drug delivery data into a generation AI and cause the generation AI to select the optimal delivery method.

[0108] The provision unit can provide medicine taking into consideration a specific health condition or lifestyle situation. For example, the provision unit provides the most appropriate medicine to a user with a specific health condition. The provision unit can also adjust the timing of medicine provision to match the user's lifestyle. Furthermore, the provision unit can customize the method of providing medicine depending on the user's health condition or lifestyle situation. This makes it possible to provide more appropriate medicine by taking into consideration the specific health condition or lifestyle situation. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's health condition data into the generation AI and have the generation AI adjust the delivery method.

[0109] The providing unit can estimate the user's emotions and prioritize the medications to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize the medication that will take effect most quickly. Furthermore, if the user is relaxed, the providing unit can also provide medications based on detailed information. Furthermore, if the user is in a hurry, the providing unit can prioritize the medications that will take effect quickly. This allows for more appropriate medications to be provided by prioritizing medications according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0110] When providing medicine, the provision unit can select the optimal delivery method by taking into account the user's geographical location information. For example, the provision unit selects the most efficient drug delivery method based on the user's current location. The provision unit can also select the optimal drug delivery method by taking into account the distance from the user's home to the hospital. Furthermore, the provision unit can select the most efficient drug delivery method based on the user's geographical location information. This makes it possible to provide more appropriate medicine by taking into account the user's geographical location information. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's geographical location data into the generation AI and cause the generation AI to select the optimal delivery method.

[0111] The provision unit can analyze the user's social media activity and provide the medicine while taking into consideration related information. For example, the provision unit can provide the medicine while taking into consideration health information mentioned by the user on social media. The provision unit can also provide the medicine while taking into consideration information related to a specific health condition from the user's social media activity. Furthermore, the provision unit can provide the optimal medicine based on the user's social media activity. This makes it possible to provide a more appropriate medicine by taking into consideration the user's social media activity. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's social media data into the generation AI and cause the generation AI to provide the medicine while taking into consideration related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, calculation unit, operation unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires the user's reservation information using the reception device 38 of the smart device 14. The collection unit collects data from traffic sensors via the communication I / F 26 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The operation unit controls the autonomous driving system using the control unit 46A of the smart device 14. The analysis unit analyzes the medical record using the specific processing unit 290 of the data processing device 12. The selection unit selects a medicine using the specific processing unit 290 of the data processing device 12. The provision unit provides the medicine using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, calculation unit, operation unit, analysis unit, selection unit, and provision unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires the user's reservation information using the microphone 238 of the smart glasses 214. The collection unit collects data from traffic sensors via the communication I / F 26 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The operation unit controls the autonomous driving system using the control unit 46A of the smart glasses 214. The analysis unit analyzes the medical record using the specific processing unit 290 of the data processing device 12. The selection unit selects a medicine using the specific processing unit 290 of the data processing device 12. The provision unit provides the medicine using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, calculation unit, operation unit, analysis unit, selection unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires the user's reservation information using the microphone 238 of the headset type terminal 314. The collection unit collects data from traffic sensors via the communication I / F 26 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The operation unit controls the autonomous driving system using the control unit 46A of the headset type terminal 314. The analysis unit analyzes the medical record using the specific processing unit 290 of the data processing device 12. The selection unit selects a medicine using the specific processing unit 290 of the data processing device 12. The provision unit provides the medicine using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, collection unit, calculation unit, operation unit, analysis unit, selection unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires the user's reservation information using the microphone 238 of the robot 414. The collection unit collects data from traffic sensors via the communication I / F 26 of the data processing device 12. The calculation unit calculates the optimal route using the specific processing unit 290 of the data processing device 12. The operation unit controls the autonomous driving system using the control unit 46A of the robot 414. The analysis unit analyzes the medical record using the specific processing unit 290 of the data processing device 12. The selection unit selects a medicine using the specific processing unit 290 of the data processing device 12. The provision unit provides the medicine using the speaker 240 of the robot 414.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The acquisition unit can analyze the user's voice data and enable reservations to be made via voice commands. For example, when a user makes a reservation via voice, the acquisition unit uses voice recognition technology to analyze the reservation details and complete the reservation procedure. The acquisition unit can also change or cancel the reservation based on the voice command. Furthermore, the acquisition unit can analyze the voice data, infer the user's intention, and suggest optimal reservation options. In this way, utilizing voice data improves user convenience and enables a smoother reservation procedure.

[0114] The acquisition unit can estimate the user's emotions and send reservation reminders based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can send frequent reminders to ensure the user does not forget the reservation. Also, if the user is relaxed, the acquisition unit can reduce the frequency of sending reminders to reduce the user's burden. Furthermore, if the user is in a hurry, the acquisition unit can immediately send a reminder to encourage a quick response. This makes it possible to send reminders according to the user's emotions, allowing for more effective reservation management.

[0115] The acquisition unit can automatically generate an optimal reservation plan based on the user's past reservation history. For example, the acquisition unit can analyze the time periods and days of the week that the user has frequently used in the past and propose an optimal reservation plan. The acquisition unit can also preferentially propose specific doctors or medical departments based on the user's past reservation history. Furthermore, the acquisition unit can automatically generate an optimal hospital visit schedule based on the user's past reservation history. This makes it possible to provide more efficient and personalized reservation plans by utilizing the user's past reservation history.

[0116] The acquisition unit can dynamically change reservation priorities based on the user's current health condition and lifestyle. For example, if the user suddenly becomes unwell, the acquisition unit can prioritize the earliest reservation slot. The acquisition unit can also flexibly adjust reservation slots to suit the user's lifestyle. Furthermore, the acquisition unit can also make it easy to cancel or change reservations depending on the user's health condition and lifestyle. This enables flexible reservation management according to the user's health condition and lifestyle.

[0117] The acquisition unit can estimate the user's emotions and customize the reservation confirmation process based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit can provide a simple and quick confirmation process. If the user is feeling relaxed, the acquisition unit can provide detailed confirmation options so that the user can complete the reservation with peace of mind. Furthermore, if the user is in a hurry, the acquisition unit can prioritize voice input or one-tap confirmation. This makes it possible to provide a flexible reservation confirmation process according to the user's emotions.

[0118] The acquisition unit can suggest the optimal bus stop location based on the user's geographical location information. For example, the acquisition unit can automatically search for and suggest the bus stop closest to the user's current location. The acquisition unit can also suggest the optimal bus stop location taking into account the route from the user's home to the hospital. Furthermore, the acquisition unit can also suggest the optimal bus stop based on the user's geographical location information and taking into account the bus stop's congestion status. This makes it possible to select a more efficient and convenient bus stop by utilizing the user's geographical location information.

[0119] The acquisition unit can analyze the user's social media activity and make reservation suggestions based on the user's interests. For example, the acquisition unit can prioritize suggestions of medical facilities or departments mentioned by the user on social media. The acquisition unit can also predict and suggest a tendency to make reservations at specific time periods or days of the week based on the user's social media activity. Furthermore, the acquisition unit can suggest reservation options tailored to the user's interests based on the user's social media activity. This makes it possible to make more personalized reservation suggestions by utilizing the user's social media activity.

[0120] The collection unit can estimate the user's emotions and customize the method of collecting traffic information based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting the most important traffic information. Also, if the user is relaxed, the collection unit can collect and provide detailed traffic information. Furthermore, if the user is in a hurry, the collection unit can collect traffic information in real time and provide it immediately. This enables flexible collection of traffic information according to the user's emotions.

[0121] The collection unit can optimize the method of collecting traffic information according to specific events or weather conditions based on past traffic data. For example, the collection unit can analyze traffic patterns when specific events are held from past data and collect information on a priority basis. The collection unit can also prioritize collecting traffic information during bad weather based on past weather data. Furthermore, the collection unit can analyze past traffic data and select the most efficient information collection method for specific time periods or days of the week. This makes it possible to collect traffic information more efficiently and effectively by utilizing past data.

[0122] When collecting traffic information, the collection unit can select the optimal information collection point based on the user's geographical location information. For example, the collection unit automatically selects the traffic information collection point closest to the user's current location. The collection unit can also select the optimal information collection point taking into account the user's route from their home to the hospital. Furthermore, the collection unit can dynamically adjust the traffic information collection range based on the user's geographical location information. This makes it possible to collect traffic information more efficiently and effectively by utilizing the user's geographical location information.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The acquisition unit acquires the reservation status when a user makes a bus reservation. For example, the acquisition unit acquires the information entered into the reservation form when a user makes an online reservation. The acquisition unit can also acquire information entered by an operator in the case of a telephone reservation. Furthermore, the acquisition unit can also acquire information on reservation cancellations. Step 2: The collection unit collects traffic information and obtains congestion prediction data. For example, the collection unit collects real-time data from traffic sensors. The collection unit can also make congestion predictions based on past traffic data. Furthermore, the collection unit can also make congestion predictions taking weather information into account. Step 3: The calculation unit uses the generation AI to calculate the optimal route based on the reservation status and traffic congestion forecast data. For example, the calculation unit inputs the reservation status and traffic congestion forecast data and calculates the route to reach the destination in the shortest time. The calculation unit can also calculate the most efficient route taking into account traffic conditions. Furthermore, the calculation unit can also calculate a scenic route according to the user's preference. Step 4: The operation department uses autonomous driving technology to operate the bus based on the calculated route. For example, the operation department controls the autonomous driving system based on the route information provided by the calculation department. The operation department can also monitor traffic conditions in real time and recalculate the route as necessary. Furthermore, the operation department is equipped with an obstacle detection system to ensure user safety. Step 5: The analysis unit uses the generation AI to analyze the medical record after the consultation. For example, the analysis unit performs text analysis of the contents of the medical record and extracts diagnosis results and prescription information. The analysis unit can also analyze trends in diagnosis results based on past medical record data. Furthermore, the analysis unit can integrate and analyze multiple medical record data to comprehensively evaluate the user's health condition. Step 6: The selection unit uses the generative AI to select a drug based on the analysis results. For example, the selection unit selects the most appropriate drug taking into account the user's medical history and allergy information. The selection unit can also select the optimal combination from multiple drugs taking into account drug side effects and drug interactions. Furthermore, the selection unit can also select generic drugs according to the user's preference. Step 7: The providing unit provides the selected medicine to the user. For example, the providing unit prepares the medicine so that it can be picked up on the bus. The providing unit can also adjust the method of providing the medicine according to the user's preference. Furthermore, the providing unit has a function of notifying the user of information regarding the provision of the medicine.

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0147] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0163] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] 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.

[0188] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires bus reservation status; a collection unit that collects congestion prediction data; a calculation unit that calculates a route based on the data acquired by the acquisition unit and the collection unit; an operation unit that operates a bus based on the route calculated by the calculation unit; an analysis unit that analyzes medical records after examinations; a selection unit that selects a medicine based on the results of the analysis by the analysis unit; a providing unit that provides the medicine selected by the selecting unit. A system characterized by:

2. The acquisition unit Estimate user emotions and adjust bus reservation timing based on the estimated user emotions 2. The system of claim 1.

3. The acquisition unit Analyze the user's past booking history and select the optimal acquisition method 2. The system of claim 1.

4. The acquisition unit Filtering appointments based on the user's current health and living situation 2. The system of claim 1.

5. The acquisition unit Estimate the user's emotions and determine the priority of reservations to be acquired based on the estimated user emotions.

2. The system of claim 1.

6. The acquisition unit When retrieving reservations, consider the user's geographic location to prioritize the most relevant reservations.

2. The system of claim 1.

7. The acquisition unit When taking a booking, analyze the user's social media activity and take relevant bookings 2. The system of claim 1.

8. The collecting unit The system estimates the user's emotions and adjusts the timing of collecting traffic information based on the estimated user emotions.

2. The system of claim 1.

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