system

Abstract

The system according to this embodiment aims to control the sounding of alarms based on weather information. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, a control unit, and a supply unit. The collection unit collects weather information from the surrounding area. The analysis unit analyzes the weather information collected by the collection unit. The control unit controls the sounding of an alarm based on the analysis results obtained by the analysis unit. The supply unit provides the alarm function controlled by the control unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is impossible to control the ringing of an alarm based on weather information, which may interfere with the comfortable activities of users.

[0005] The system according to the embodiment aims to control the ringing of an alarm based on weather information.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a control unit, and a provision unit. The collection unit collects weather information around the current location. The analysis unit analyzes the weather information collected by the collection unit. The control unit controls the ringing of the alarm based on the analysis result obtained by the analysis unit. The provision unit provides the alarm function controlled by the control unit. [Effects of the Invention]

[0007] The system according to this embodiment can control the sounding of alarms based on weather information. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An alarm control system according to an embodiment of the present invention is a system that uses a generating AI to control the sounding of a wake-up alarm based on weather information around the user's current location. In this alarm control system, the user sets an alarm, and at the set alarm time, the generating AI collects and analyzes real-time weather information around the user's current location. If it is raining around the user's current location at the set alarm time, the alarm will not sound. This allows the user to enjoy a comfortable early morning walk without having to get up on a rainy day. For example, when the user sets an alarm, they only need to set the time and day of the week, just like with a normal alarm. The generating AI uses web crawling or API connection to obtain weather information for the current location and determines whether it is raining. The generating AI analyzes the collected weather information and controls the sounding of the alarm. For example, if it is raining at the set alarm time, the alarm will not sound, and the user can continue sleeping. This mechanism allows the user to wake up comfortably on a sunny day and enjoy an early morning walk. On rainy days, the alarm does not sound, so the user can get enough sleep without having to get up. This improves the user's health and comfortable lifestyle. Furthermore, this feature will be integrated into the app provided by Healthcare Technologies. This will allow users to easily control alarm sounds using the app. For example, simply opening the app and setting an alarm will automatically trigger the generation AI to collect weather information and control the alarm's sounding. In this way, by utilizing generation AI to control wake-up alarms based on weather information around the user's current location, users can enjoy a more comfortable early morning walk. Moreover, integrating this feature into Healthcare Technologies' app will improve user convenience and increase the app's value. As a result, the alarm control system can improve users' health and well-being.

[0029] The alarm control system according to the embodiment comprises a collection unit, an analysis unit, a control unit, and a provision unit. The collection unit collects weather information around the current location. The collection unit obtains weather information for the current location, for example, by using web crawling or API connection. The collection unit obtains weather data, for example, and determines whether it is raining. The collection unit can also collect weather information in real time using generative AI. For example, the collection unit collects data from weather information sites on the internet using web crawling technology. The collection unit can also obtain real-time weather information from weather data providers using API connection. The collection unit can also adjust the frequency of weather information collection using generative AI. For example, the collection unit can increase the frequency of weather information collection based on the user's sentiment. The analysis unit analyzes the weather information collected by the collection unit. For example, the analysis unit analyzes the collected weather information and determines whether it is raining. The analysis unit can also analyze the weather information using generative AI. For example, the analysis unit determines whether it is raining based on the collected weather data. The analysis unit can also adjust the method of analyzing weather information using generative AI. For example, the analysis unit can adjust the method of analyzing weather information based on the user's emotions. The control unit controls the alarm sounding based on the analysis results obtained by the analysis unit. For example, the control unit will not sound the alarm if it is raining at the set alarm time. The control unit can also control the alarm sounding using generative AI. For example, the control unit controls the alarm sounding based on the weather information obtained by the analysis unit. The control unit can also adjust the method of controlling the alarm sounding using generative AI. For example, the control unit can adjust the method of controlling the alarm sounding based on the user's emotions. The provision unit provides the alarm function controlled by the control unit. For example, the provision unit provides the alarm function to Healthcare Technologies' app. The provision unit can also adjust the method of providing the alarm function using generative AI. For example, the provision unit can adjust the method of providing the alarm function based on the user's emotions.As a result, the alarm control system according to this embodiment can improve the user's health and comfortable lifestyle.

[0030] The data collection unit collects weather information for the user's current location. For example, it obtains weather information for the current location using web crawling or API connections. Specifically, it uses web crawling technology to collect data from weather information websites on the internet. This allows the data collection unit to obtain the latest weather information in real time. Furthermore, it can also obtain real-time weather information from weather data providers using API connections. Using API connections, the data collection unit can quickly obtain reliable weather data. The data collection unit can also adjust the frequency of weather information collection using generative AI. For example, the data collection unit can increase the frequency of weather information collection based on the user's emotions. The generative AI analyzes the user's emotions and, when the user is stressed or anxious, increases the frequency of weather information collection to provide the user with a sense of security. This enables the data collection unit to flexibly collect weather information according to the user's needs. Furthermore, the data collection unit can centrally manage the collected weather information and integrate it with other systems and departments. For example, the collected weather information can be stored on a cloud server and made accessible to the analysis and control units. This allows the collection unit to efficiently and effectively collect weather information, improving the overall performance of the system.

[0031] The analysis department analyzes weather information collected by the data collection department. For example, the analysis department analyzes the collected weather information to determine whether it is raining. Specifically, it analyzes parameters such as temperature, humidity, and precipitation based on the collected meteorological data to understand the current weather conditions. The analysis department can also analyze weather information using generative AI. Generative AI has the ability to learn from vast amounts of meteorological data and identify weather patterns. For example, based on past meteorological data, generative AI can predict current weather conditions and determine with high accuracy whether it is raining. Furthermore, the analysis department can also adjust the method of analyzing weather information using generative AI. For example, the analysis department can adjust the method of analyzing weather information based on the user's emotions. By analyzing the user's emotions, the generative AI can provide a sense of security to the user by providing more detailed weather information when the user is feeling stressed or anxious. This allows the analysis department to achieve flexible weather information analysis that meets the user's needs. In addition, the analysis department can also utilize historical data and statistical information to perform long-term weather forecasts and trend analyses. This allows the analysis unit to not only grasp weather conditions in real time, but also to handle long-term weather forecasts and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The control unit controls the alarm's sounding based on the analysis results obtained by the analysis unit. For example, if it is raining at the set alarm time, the control unit will not sound the alarm. Specifically, the control unit controls the alarm's sounding based on weather information provided by the analysis unit. For example, if it is raining at the set alarm time, the control unit can prevent the user from waking up in the rain by controlling the alarm not to sound. The control unit can also control the alarm's sounding using generative AI. Generative AI has the ability to learn the user's emotions and behavioral patterns and provide the optimal timing for the alarm to sound. For example, if the user is feeling stressed or anxious, the generative AI can reduce the user's stress by delaying the alarm's sounding. Furthermore, the control unit can also adjust the alarm's sounding control method using generative AI. For example, the control unit can adjust the alarm's sounding control method based on the user's emotions. The generative AI can analyze the user's emotions and support a comfortable life for the user by adjusting the alarm's sounding when the user is relaxed or focused. This allows the control unit to achieve flexible alarm sounding control that meets the user's needs.

[0033] The service provider provides an alarm function controlled by the control unit. For example, the service provider provides an alarm function to the Healthcare Technologies app. Specifically, the service provider provides the alarm function, controlled by the control unit, to the user's smartphone or tablet. The service provider can also adjust the method of providing the alarm function using generative AI. Generative AI has the ability to learn the user's emotions and behavioral patterns and provide the optimal alarm delivery method. For example, if the user is feeling stressed or anxious, the generative AI can reduce the user's stress by adjusting the alarm volume and melody. Furthermore, the service provider can also adjust the method of providing the alarm function using generative AI. For example, the service provider can adjust the method of providing the alarm function based on the user's emotions. The generative AI analyzes the user's emotions and can support the user's comfortable life by adjusting the alarm volume and melody when the user is relaxed or focused. This allows the service provider to provide a flexible alarm function tailored to the user's needs. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the alarm function. For example, by adjusting the alarm volume and melody based on feedback from users who utilize the alarm function, user satisfaction can be improved. This allows the service provider to enhance users' health and well-being.

[0034] The data collection unit can collect weather information for the current location using web crawling or API connections. For example, the data collection unit can collect data from weather information websites on the internet using web crawling technology. For example, the data collection unit can obtain real-time weather information using the API of a specific weather data provider. The data collection unit can also adjust the frequency of weather information collection using generative AI. For example, the data collection unit can increase the frequency of weather information collection based on the user's sentiment. This allows for the collection of real-time weather information using web crawling or API connections. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input weather information collected using web crawling technology into a generative AI, which can then adjust the frequency of weather information collection.

[0035] The analysis unit can analyze the collected weather information and determine whether it is raining or not. For example, the analysis unit can determine whether it is raining or not based on the collected weather data. The analysis unit can also analyze the weather information using a generative AI. For example, the analysis unit inputs the collected weather data into the generative AI, and the generative AI determines whether it is raining or not. This allows for an accurate determination of whether it is raining or not by analyzing the collected weather information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input the collected weather information into a generative AI, and the generative AI can analyze the weather information.

[0036] The control unit can choose not to sound the alarm if it is raining at the set alarm time. For example, the control unit can determine whether it is raining at the set alarm time and, if so, not sound the alarm. The control unit can also control the alarm's sounding using a generation AI. For example, the control unit can control the alarm's sounding based on weather information obtained by the analysis unit. This allows the user to enjoy a comfortable early morning walk without being forced to get up, by preventing the alarm from sounding if it is raining at the set alarm time. Some or all of the above-described processes in the control unit may be performed using a generation AI or not. For example, the control unit can input weather information obtained by the analysis unit into a generation AI, which can then control the alarm's sounding.

[0037] The service provider can provide an alarm function to Healthcare Technologies' app. For example, the service provider can integrate the alarm function into the app, allowing users to easily control when the alarm sounds. The service provider can also use generative AI to adjust how the alarm function is provided. For example, the service provider can adjust how the alarm function is provided based on the user's emotions. This allows users to easily control when the alarm sounds by providing the alarm function to Healthcare Technologies' app. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the method for providing the alarm function into the generative AI, which can then adjust the method for providing the alarm function.

[0038] The data collection unit can analyze the user's past weather data usage history and select the optimal collection method. For example, the data collection unit may prioritize the weather information collection method that the user has frequently used in the past. For example, the data collection unit may suggest the optimal collection method for a specific time period based on the user's past weather data usage history. For example, the data collection unit may analyze the user's past weather data usage history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past weather data usage history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or not. For example, the data collection unit can input the user's past weather data usage history into a generation AI, and the generation AI can select the optimal collection method.

[0039] The data collection unit can filter weather information based on the user's current activity schedule. For example, the data collection unit can refer to the user's calendar information and prioritize collecting weather information relevant to the current activity schedule. For example, the data collection unit can filter out unnecessary weather information based on the user's current activity schedule. For example, the data collection unit can prioritize collecting information related to specific weather conditions according to the user's current activity schedule. This allows the system to provide highly relevant information by filtering weather information based on the user's current activity schedule. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's calendar information into a generation AI, which can then filter the weather information.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting weather information. For example, the data collection unit prioritizes the collection of the most relevant weather information based on the user's current location. For example, the data collection unit prioritizes the collection of highly relevant weather information based on the user's travel plans. For example, the data collection unit prioritizes the collection of highly relevant weather information based on the user's past travel history. This allows the system to provide highly relevant weather information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant weather information.

[0041] The data collection unit can analyze the user's social media activity and collect relevant weather information when collecting weather information. For example, the data collection unit can analyze the user's social media posts and collect relevant weather information. For example, the data collection unit can refer to the user's location information on social media and collect relevant weather information. For example, the data collection unit can analyze the posts of the user's friends on social media and collect relevant weather information. In this way, relevant weather information can be provided by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, and the generative AI can collect relevant weather information.

[0042] The analysis unit can adjust the level of detail of its analysis based on the importance of the weather information. For example, the analysis unit will perform a detailed analysis for important weather information. For example, the analysis unit will perform a concise analysis for general weather information. For example, the analysis unit will perform a rapid analysis for urgent weather information. By adjusting the level of detail of the analysis based on the importance of the weather information, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the weather information into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of weather information during analysis. For example, for rainy weather information, the analysis unit applies an analysis algorithm that emphasizes precipitation amount and probability of precipitation. For example, for sunny weather information, the analysis unit applies an analysis algorithm that emphasizes temperature and ultraviolet radiation. For example, for snowy weather information, the analysis unit applies an analysis algorithm that emphasizes snow depth and road surface conditions. By applying different analysis algorithms depending on the category of weather information, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category of weather information into a generation AI, and the generation AI can apply different analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the timing of weather information collection. For example, the analysis unit may prioritize the analysis of the most recent weather information. For example, the analysis unit may analyze current weather information by referring to past weather information. For example, the analysis unit may prioritize the analysis of weather information collected during a specific time period. This enables efficient analysis by determining the priority of analysis based on the timing of weather information collection. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the timing of weather information collection into a generation AI, and the generation AI can determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of weather information during the analysis. For example, the analysis unit may prioritize analyzing weather information around the current location. For example, the analysis unit may prioritize analyzing weather information related to the user's travel plans. For example, the analysis unit may prioritize analyzing weather information related to the user's past travel history. By adjusting the order of analysis based on the relevance of weather information, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance of weather information into a generative AI, and the generative AI can adjust the order of analysis.

[0046] The control unit can improve the accuracy of control by considering the interrelationships of weather information during control. For example, the control unit combines rain information and wind speed information to control the sounding of an alarm. For example, the control unit combines temperature information and humidity information to control the sounding of an alarm. For example, the control unit combines snowfall information and road surface condition information to control the sounding of an alarm. This improves the accuracy of control by considering the interrelationships of weather information. Some or all of the above processing in the control unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the control unit can input the interrelationships of weather information into a generation AI, which can then improve the accuracy of control.

[0047] The control unit can perform control while considering the user's past alarm usage history. For example, if the user has ignored an alarm in the past, the control unit will emphasize the alarm sounding. For example, if the user has frequently used the alarm in the past, the control unit will sound the alarm normally. For example, the control unit will analyze the user's past alarm usage history and propose the optimal sounding method. This makes optimal alarm control possible by considering the user's past alarm usage history. Some or all of the above processing in the control unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the control unit can input the user's past alarm usage history into a generation AI, and the generation AI can propose the optimal sounding method.

[0048] The control unit can perform control while considering the geographical distribution of weather information. For example, the control unit can control the sounding of an alarm based on weather information around the current location. For example, the control unit can control the sounding of an alarm based on the user's travel plans. For example, the control unit can control the sounding of an alarm based on the user's past travel history. This makes it possible to perform more accurate alarm control by considering the geographical distribution of weather information. Some or all of the above processing in the control unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the control unit can input the geographical distribution of weather information to a generation AI, and the generation AI can perform the control.

[0049] The control unit can improve the accuracy of control by referring to relevant literature on weather information during control. For example, the control unit controls the sounding of alarms by referring to the latest research results on weather information. For example, the control unit controls the sounding of alarms by referring to past research results on weather information. For example, the control unit analyzes relevant literature on weather information and proposes the optimal control method. As a result, the accuracy of control is improved by referring to relevant literature on weather information. Some or all of the above processing in the control unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the control unit can input relevant literature on weather information into a generating AI, and the generating AI can improve the accuracy of control.

[0050] The service provider can select the optimal service method by referring to the user's past alarm usage history at the time of service provision. For example, the service provider may prioritize providing alarm functions that the user has frequently used in the past. For example, the service provider may propose the optimal service method for a specific time period based on the user's past alarm usage history. For example, the service provider may analyze the user's past alarm usage history and select the most efficient service method. This allows the service provider to provide the optimal alarm function by referring to the user's past alarm usage history. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider may input the user's past alarm usage history into a generation AI, which can then select the optimal service method.

[0051] The service provider can customize the means of providing the alarm function based on the user's current lifestyle at the time of provision. For example, if the user is busy, the service provider may provide a simple alarm function. For example, if the user is relaxed, the service provider may provide a detailed alarm function. The service provider customizes the means of providing the alarm function according to the user's lifestyle. By customizing the means of providing the alarm function based on the user's current lifestyle, a more appropriate alarm function can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's current lifestyle into a generative AI, and the generative AI can customize the means of providing the alarm function.

[0052] The service provider can provide the most appropriate alarm function at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide the most relevant alarm function based on the user's current location. For example, the service provider can provide the most appropriate alarm function based on the user's travel plans. For example, the service provider can provide the most appropriate alarm function based on the user's past travel history. In this way, the service provider can provide the most appropriate alarm function by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's geographical location information into a generation AI, and the generation AI can provide the most appropriate alarm function.

[0053] The service provider can analyze the user's social media activity and propose a means of providing an alarm function at the time of provision. For example, the service provider can analyze the content of the user's social media posts and propose the optimal alarm function. For example, the service provider can refer to the user's location information on social media and propose the optimal alarm function. For example, the service provider can analyze the content of posts by the user's friends on social media and propose the optimal alarm function. In this way, the service provider can provide the optimal alarm function by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, and the generative AI can propose a means of providing an alarm function.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can collect user health data and combine it with weather information to control alarm activation. For example, the unit monitors the user's heart rate and sleep quality, and analyzes this data in conjunction with weather information. If the user is fatigued, the unit can delay the alarm activation. If the user has had enough sleep, the unit can activate the alarm as usual. This allows for more appropriate waking by adjusting the alarm activation according to the user's health condition.

[0056] The analysis department can analyze a user's past weather data usage history and select the optimal analysis method. For example, the analysis department prioritizes selecting weather information analysis methods that the user has frequently used in the past. The analysis department proposes the optimal analysis method for a specific time period based on the user's past weather data usage history. The analysis department analyzes the user's past weather data usage history and selects the most efficient analysis method. In this way, the optimal analysis method can be selected by analyzing the user's past weather data usage history.

[0057] The control unit can refer to the user's calendar information and control the alarm's sounding based on the user's current activity schedule. For example, the control unit will emphasize the alarm if the user has an important meeting or appointment. The control unit will also tone down the alarm if the user is on holiday. The control unit can adjust the alarm's sounding time based on the user's calendar information. This allows for more appropriate alarm control by tailoring the alarm's sounding to the user's current activity schedule.

[0058] The service provider can analyze users' social media activity and provide relevant alarm functions. For example, the service provider can analyze the content of users' social media posts and provide relevant alarm functions. The service provider can refer to users' location information on social media and provide relevant alarm functions. The service provider can analyze the content of posts by users' friends on social media and provide relevant alarm functions. In this way, by analyzing users' social media activity, relevant alarm functions can be provided.

[0059] The service provider can provide the most appropriate alarm function by considering the user's geographical location. For example, the service provider can provide the most relevant alarm function based on the user's current location. The service provider can provide the most appropriate alarm function based on the user's travel plans. The service provider can provide the most appropriate alarm function based on the user's past travel history. In this way, the service provider can provide the most appropriate alarm function by considering the user's geographical location.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit gathers weather information for the current location. The data collection unit obtains weather information for the current location using web crawling or API connections. For example, the data collection unit can collect data from weather information websites on the internet or obtain real-time weather information from weather data providers. The data collection unit can also adjust the frequency of weather information collection using generative AI. Step 2: The analysis unit analyzes the weather information collected by the collection unit. The analysis unit analyzes the collected weather information and determines whether it is raining or not. The analysis unit can also adjust the method of analyzing the weather information using generative AI. Step 3: The control unit controls the alarm's activation based on the analysis results obtained by the analysis unit. For example, if it is raining at the alarm's set time, the control unit will not activate the alarm. The control unit can also use generated AI to adjust the alarm's activation control method. Step 4: The service unit provides an alarm function controlled by the control unit. For example, the service unit provides an alarm function to Healthcare Technologies' app. The service unit can also use generative AI to adjust how the alarm function is provided.

[0062] (Example of form 2) An alarm control system according to an embodiment of the present invention is a system that uses a generating AI to control the sounding of a wake-up alarm based on weather information around the user's current location. In this alarm control system, the user sets an alarm, and at the set alarm time, the generating AI collects and analyzes real-time weather information around the user's current location. If it is raining around the user's current location at the set alarm time, the alarm will not sound. This allows the user to enjoy a comfortable early morning walk without having to get up on a rainy day. For example, when the user sets an alarm, they only need to set the time and day of the week, just like with a normal alarm. The generating AI uses web crawling or API connection to obtain weather information for the current location and determines whether it is raining. The generating AI analyzes the collected weather information and controls the sounding of the alarm. For example, if it is raining at the set alarm time, the alarm will not sound, and the user can continue sleeping. This mechanism allows the user to wake up comfortably on a sunny day and enjoy an early morning walk. On rainy days, the alarm does not sound, so the user can get enough sleep without having to get up. This improves the user's health and comfortable lifestyle. Furthermore, this feature will be integrated into the app provided by Healthcare Technologies. This will allow users to easily control alarm sounds using the app. For example, simply opening the app and setting an alarm will automatically trigger the generation AI to collect weather information and control the alarm's sounding. In this way, by utilizing generation AI to control wake-up alarms based on weather information around the user's current location, users can enjoy a more comfortable early morning walk. Moreover, integrating this feature into Healthcare Technologies' app will improve user convenience and increase the app's value. As a result, the alarm control system can improve users' health and well-being.

[0063] The alarm control system according to the embodiment comprises a collection unit, an analysis unit, a control unit, and a provision unit. The collection unit collects weather information around the current location. The collection unit obtains weather information for the current location, for example, by using web crawling or API connection. The collection unit obtains weather data, for example, and determines whether it is raining. The collection unit can also collect weather information in real time using generative AI. For example, the collection unit collects data from weather information sites on the internet using web crawling technology. The collection unit can also obtain real-time weather information from weather data providers using API connection. The collection unit can also adjust the frequency of weather information collection using generative AI. For example, the collection unit can increase the frequency of weather information collection based on the user's sentiment. The analysis unit analyzes the weather information collected by the collection unit. For example, the analysis unit analyzes the collected weather information and determines whether it is raining. The analysis unit can also analyze the weather information using generative AI. For example, the analysis unit determines whether it is raining based on the collected weather data. The analysis unit can also adjust the method of analyzing weather information using generative AI. For example, the analysis unit can adjust the method of analyzing weather information based on the user's emotions. The control unit controls the alarm sounding based on the analysis results obtained by the analysis unit. For example, the control unit will not sound the alarm if it is raining at the set alarm time. The control unit can also control the alarm sounding using generative AI. For example, the control unit controls the alarm sounding based on the weather information obtained by the analysis unit. The control unit can also adjust the method of controlling the alarm sounding using generative AI. For example, the control unit can adjust the method of controlling the alarm sounding based on the user's emotions. The provision unit provides the alarm function controlled by the control unit. For example, the provision unit provides the alarm function to Healthcare Technologies' app. The provision unit can also adjust the method of providing the alarm function using generative AI. For example, the provision unit can adjust the method of providing the alarm function based on the user's emotions.As a result, the alarm control system according to this embodiment can improve the user's health and comfortable lifestyle.

[0064] The data collection unit collects weather information for the user's current location. For example, it obtains weather information for the current location using web crawling or API connections. Specifically, it uses web crawling technology to collect data from weather information websites on the internet. This allows the data collection unit to obtain the latest weather information in real time. Furthermore, it can also obtain real-time weather information from weather data providers using API connections. Using API connections, the data collection unit can quickly obtain reliable weather data. The data collection unit can also adjust the frequency of weather information collection using generative AI. For example, the data collection unit can increase the frequency of weather information collection based on the user's emotions. The generative AI analyzes the user's emotions and, when the user is stressed or anxious, increases the frequency of weather information collection to provide the user with a sense of security. This enables the data collection unit to flexibly collect weather information according to the user's needs. Furthermore, the data collection unit can centrally manage the collected weather information and integrate it with other systems and departments. For example, the collected weather information can be stored on a cloud server and made accessible to the analysis and control units. This allows the collection unit to efficiently and effectively collect weather information, improving the overall performance of the system.

[0065] The analysis department analyzes weather information collected by the data collection department. For example, the analysis department analyzes the collected weather information to determine whether it is raining. Specifically, it analyzes parameters such as temperature, humidity, and precipitation based on the collected meteorological data to understand the current weather conditions. The analysis department can also analyze weather information using generative AI. Generative AI has the ability to learn from vast amounts of meteorological data and identify weather patterns. For example, based on past meteorological data, generative AI can predict current weather conditions and determine with high accuracy whether it is raining. Furthermore, the analysis department can also adjust the method of analyzing weather information using generative AI. For example, the analysis department can adjust the method of analyzing weather information based on the user's emotions. By analyzing the user's emotions, the generative AI can provide a sense of security to the user by providing more detailed weather information when the user is feeling stressed or anxious. This allows the analysis department to achieve flexible weather information analysis that meets the user's needs. In addition, the analysis department can also utilize historical data and statistical information to perform long-term weather forecasts and trend analyses. This allows the analysis unit to not only grasp weather conditions in real time, but also to handle long-term weather forecasts and anomaly detection, thereby improving the reliability and safety of the entire system.

[0066] The control unit controls the alarm's sounding based on the analysis results obtained by the analysis unit. For example, if it is raining at the set alarm time, the control unit will not sound the alarm. Specifically, the control unit controls the alarm's sounding based on weather information provided by the analysis unit. For example, if it is raining at the set alarm time, the control unit can prevent the user from waking up in the rain by controlling the alarm not to sound. The control unit can also control the alarm's sounding using generative AI. Generative AI has the ability to learn the user's emotions and behavioral patterns and provide the optimal timing for the alarm to sound. For example, if the user is feeling stressed or anxious, the generative AI can reduce the user's stress by delaying the alarm's sounding. Furthermore, the control unit can also adjust the alarm's sounding control method using generative AI. For example, the control unit can adjust the alarm's sounding control method based on the user's emotions. The generative AI can analyze the user's emotions and support a comfortable life for the user by adjusting the alarm's sounding when the user is relaxed or focused. This allows the control unit to achieve flexible alarm sounding control that meets the user's needs.

[0067] The service provider provides an alarm function controlled by the control unit. For example, the service provider provides an alarm function to the Healthcare Technologies app. Specifically, the service provider provides the alarm function, controlled by the control unit, to the user's smartphone or tablet. The service provider can also adjust the method of providing the alarm function using generative AI. Generative AI has the ability to learn the user's emotions and behavioral patterns and provide the optimal alarm delivery method. For example, if the user is feeling stressed or anxious, the generative AI can reduce the user's stress by adjusting the alarm volume and melody. Furthermore, the service provider can also adjust the method of providing the alarm function using generative AI. For example, the service provider can adjust the method of providing the alarm function based on the user's emotions. The generative AI analyzes the user's emotions and can support the user's comfortable life by adjusting the alarm volume and melody when the user is relaxed or focused. This allows the service provider to provide a flexible alarm function tailored to the user's needs. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the alarm function. For example, by adjusting the alarm volume and melody based on feedback from users who utilize the alarm function, user satisfaction can be improved. This allows the service provider to enhance users' health and well-being.

[0068] The data collection unit can collect weather information for the current location using web crawling or API connections. For example, the data collection unit can collect data from weather information websites on the internet using web crawling technology. For example, the data collection unit can obtain real-time weather information using the API of a specific weather data provider. The data collection unit can also adjust the frequency of weather information collection using generative AI. For example, the data collection unit can increase the frequency of weather information collection based on the user's sentiment. This allows for the collection of real-time weather information using web crawling or API connections. Some or all of the above-described processes in the data collection unit may be performed using generative AI or not. For example, the data collection unit can input weather information collected using web crawling technology into a generative AI, which can then adjust the frequency of weather information collection.

[0069] The analysis unit can analyze the collected weather information and determine whether it is raining or not. For example, the analysis unit can determine whether it is raining or not based on the collected weather data. The analysis unit can also analyze the weather information using a generative AI. For example, the analysis unit inputs the collected weather data into the generative AI, and the generative AI determines whether it is raining or not. This allows for an accurate determination of whether it is raining or not by analyzing the collected weather information. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or they may be performed without using a generative AI. For example, the analysis unit can input the collected weather information into a generative AI, and the generative AI can analyze the weather information.

[0070] The control unit can choose not to sound the alarm if it is raining at the set alarm time. For example, the control unit can determine whether it is raining at the set alarm time and, if so, not sound the alarm. The control unit can also control the alarm's sounding using a generation AI. For example, the control unit can control the alarm's sounding based on weather information obtained by the analysis unit. This allows the user to enjoy a comfortable early morning walk without being forced to get up, by preventing the alarm from sounding if it is raining at the set alarm time. Some or all of the above-described processes in the control unit may be performed using a generation AI or not. For example, the control unit can input weather information obtained by the analysis unit into a generation AI, which can then control the alarm's sounding.

[0071] The service provider can provide an alarm function to Healthcare Technologies' app. For example, the service provider can integrate the alarm function into the app, allowing users to easily control when the alarm sounds. The service provider can also use generative AI to adjust how the alarm function is provided. For example, the service provider can adjust how the alarm function is provided based on the user's emotions. This allows users to easily control when the alarm sounds by providing the alarm function to Healthcare Technologies' app. Some or all of the above processing in the service provider may be performed using generative AI or not. For example, the service provider can input the method for providing the alarm function into the generative AI, which can then adjust the method for providing the alarm function.

[0072] The data collection unit can estimate the user's emotions and adjust the frequency of weather information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit increases the frequency of weather information collection to provide more detailed information. For example, if the user is relaxed, the data collection unit decreases the frequency of weather information collection to provide only the minimum necessary information. For example, if the user is in a hurry, the data collection unit optimizes the frequency of weather information collection to quickly provide the necessary information. In this way, by adjusting the frequency of weather information collection according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the frequency of weather information collection.

[0073] The data collection unit can analyze the user's past weather data usage history and select the optimal collection method. For example, the data collection unit may prioritize the weather information collection method that the user has frequently used in the past. For example, the data collection unit may suggest the optimal collection method for a specific time period based on the user's past weather data usage history. For example, the data collection unit may analyze the user's past weather data usage history and select the most efficient collection method. In this way, the optimal collection method can be selected by analyzing the user's past weather data usage history. Some or all of the above processing in the data collection unit may be performed using a generation AI, or not. For example, the data collection unit can input the user's past weather data usage history into a generation AI, and the generation AI can select the optimal collection method.

[0074] The data collection unit can filter weather information based on the user's current activity schedule. For example, the data collection unit can refer to the user's calendar information and prioritize collecting weather information relevant to the current activity schedule. For example, the data collection unit can filter out unnecessary weather information based on the user's current activity schedule. For example, the data collection unit can prioritize collecting information related to specific weather conditions according to the user's current activity schedule. This allows the system to provide highly relevant information by filtering weather information based on the user's current activity schedule. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's calendar information into a generation AI, which can then filter the weather information.

[0075] The data collection unit can estimate the user's emotions and determine the priority of weather information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important weather information. For example, if the user is relaxed, the data collection unit will prioritize collecting general weather information. For example, if the user is in a hurry, the data collection unit will prioritize collecting weather information that can be collected quickly. This allows for the provision of more important information by prioritizing the weather information to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using or without a generative AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the priority of weather information to collect.

[0076] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting weather information. For example, the data collection unit prioritizes the collection of the most relevant weather information based on the user's current location. For example, the data collection unit prioritizes the collection of highly relevant weather information based on the user's travel plans. For example, the data collection unit prioritizes the collection of highly relevant weather information based on the user's past travel history. This allows the system to provide highly relevant weather information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant weather information.

[0077] The data collection unit can analyze the user's social media activity and collect relevant weather information when collecting weather information. For example, the data collection unit can analyze the user's social media posts and collect relevant weather information. For example, the data collection unit can refer to the user's location information on social media and collect relevant weather information. For example, the data collection unit can analyze the posts of the user's friends on social media and collect relevant weather information. In this way, relevant weather information can be provided by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI, and the generative AI can collect relevant weather information.

[0078] The analysis unit can estimate the user's emotions and adjust the weather information analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit adjusts the analysis method to provide detailed weather information. For example, if the user is relaxed, the analysis unit adjusts the analysis method to provide concise weather information. For example, if the user is in a hurry, the analysis unit adjusts the analysis method to provide analysis results quickly. In this way, by adjusting the weather information analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the weather information analysis method.

[0079] The analysis unit can adjust the level of detail of its analysis based on the importance of the weather information. For example, the analysis unit will perform a detailed analysis for important weather information. For example, the analysis unit will perform a concise analysis for general weather information. For example, the analysis unit will perform a rapid analysis for urgent weather information. By adjusting the level of detail of the analysis based on the importance of the weather information, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance of the weather information into the generation AI, and the generation AI can adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the category of weather information during analysis. For example, for rainy weather information, the analysis unit applies an analysis algorithm that emphasizes precipitation amount and probability of precipitation. For example, for sunny weather information, the analysis unit applies an analysis algorithm that emphasizes temperature and ultraviolet radiation. For example, for snowy weather information, the analysis unit applies an analysis algorithm that emphasizes snow depth and road surface conditions. By applying different analysis algorithms depending on the category of weather information, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the category of weather information into a generation AI, and the generation AI can apply different analysis algorithms.

[0081] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the display method of the analysis results.

[0082] The analysis unit can determine the priority of analysis based on the timing of weather information collection. For example, the analysis unit may prioritize the analysis of the most recent weather information. For example, the analysis unit may analyze current weather information by referring to past weather information. For example, the analysis unit may prioritize the analysis of weather information collected during a specific time period. This enables efficient analysis by determining the priority of analysis based on the timing of weather information collection. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input the timing of weather information collection into a generation AI, and the generation AI can determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of weather information during the analysis. For example, the analysis unit may prioritize analyzing weather information around the current location. For example, the analysis unit may prioritize analyzing weather information related to the user's travel plans. For example, the analysis unit may prioritize analyzing weather information related to the user's past travel history. By adjusting the order of analysis based on the relevance of weather information, efficient analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the relevance of weather information into a generative AI, and the generative AI can adjust the order of analysis.

[0084] The control unit can estimate the user's emotions and adjust the alarm sounding control method based on the estimated user emotions. For example, if the user is stressed, the control unit will sound the alarm less loudly. For example, if the user is relaxed, the control unit will sound the alarm normally. For example, if the user is in a hurry, the control unit will sound the alarm more loudly. This allows for more appropriate alarm control by adjusting the alarm sounding control method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the control unit may be performed using the generative AI or not. For example, the control unit can input user emotion data into the generative AI, and the generative AI can adjust the alarm sounding control method.

[0085] The control unit can improve the accuracy of control by considering the interrelationships of weather information during control. For example, the control unit combines rain information and wind speed information to control the sounding of an alarm. For example, the control unit combines temperature information and humidity information to control the sounding of an alarm. For example, the control unit combines snowfall information and road surface condition information to control the sounding of an alarm. This improves the accuracy of control by considering the interrelationships of weather information. Some or all of the above processing in the control unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the control unit can input the interrelationships of weather information into a generation AI, which can then improve the accuracy of control.

[0086] The control unit can perform control while considering the user's past alarm usage history. For example, if the user has ignored an alarm in the past, the control unit will emphasize the alarm sounding. For example, if the user has frequently used the alarm in the past, the control unit will sound the alarm normally. For example, the control unit will analyze the user's past alarm usage history and propose the optimal sounding method. This makes optimal alarm control possible by considering the user's past alarm usage history. Some or all of the above processing in the control unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the control unit can input the user's past alarm usage history into a generation AI, and the generation AI can propose the optimal sounding method.

[0087] The control unit can estimate the user's emotions and adjust the order in which alarm sounding control results are displayed based on the estimated user emotions. For example, if the user is tense, the control unit will prioritize displaying important control results. For example, if the user is relaxed, the control unit will prioritize displaying general control results. For example, if the user is in a hurry, the control unit will prioritize displaying control results that can be displayed quickly. This allows for the provision of more appropriate information by adjusting the order in which alarm sounding control results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, 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 control unit may be performed using or without a generative AI. For example, the control unit can input user emotion data into a generative AI, which can then adjust the order in which alarm sounding control results are displayed.

[0088] The control unit can perform control while considering the geographical distribution of weather information. For example, the control unit can control the sounding of an alarm based on weather information around the current location. For example, the control unit can control the sounding of an alarm based on the user's travel plans. For example, the control unit can control the sounding of an alarm based on the user's past travel history. This makes it possible to perform more accurate alarm control by considering the geographical distribution of weather information. Some or all of the above processing in the control unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the control unit can input the geographical distribution of weather information to a generation AI, and the generation AI can perform the control.

[0089] The control unit can improve the accuracy of control by referring to relevant literature on weather information during control. For example, the control unit controls the sounding of alarms by referring to the latest research results on weather information. For example, the control unit controls the sounding of alarms by referring to past research results on weather information. For example, the control unit analyzes relevant literature on weather information and proposes the optimal control method. As a result, the accuracy of control is improved by referring to relevant literature on weather information. Some or all of the above processing in the control unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the control unit can input relevant literature on weather information into a generating AI, and the generating AI can improve the accuracy of control.

[0090] The service provider can estimate the user's emotions and adjust how the alarm function is delivered based on the estimated emotions. For example, if the user is stressed, the service provider may reduce the intensity of the alarm function. If the user is relaxed, the service provider may deliver the alarm function normally. If the user is in a hurry, the service provider may deliver the alarm function quickly. By adjusting the alarm function delivery method according to the user's emotions, a more appropriate alarm function can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using the generative AI or not. For example, the service provider can input user emotion data into the generative AI, which can then adjust how the alarm function is delivered.

[0091] The service provider can select the optimal service method by referring to the user's past alarm usage history at the time of service provision. For example, the service provider may prioritize providing alarm functions that the user has frequently used in the past. For example, the service provider may propose the optimal service method for a specific time period based on the user's past alarm usage history. For example, the service provider may analyze the user's past alarm usage history and select the most efficient service method. This allows the service provider to provide the optimal alarm function by referring to the user's past alarm usage history. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider may input the user's past alarm usage history into a generation AI, which can then select the optimal service method.

[0092] The service provider can customize the means of providing the alarm function based on the user's current lifestyle at the time of provision. For example, if the user is busy, the service provider may provide a simple alarm function. For example, if the user is relaxed, the service provider may provide a detailed alarm function. The service provider customizes the means of providing the alarm function according to the user's lifestyle. By customizing the means of providing the alarm function based on the user's current lifestyle, a more appropriate alarm function can be provided. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's current lifestyle into a generative AI, and the generative AI can customize the means of providing the alarm function.

[0093] The service provider can estimate the user's emotions and determine the priority of alarm functions based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize important alarm functions. For example, if the user is relaxed, the service provider will prioritize general alarm functions. For example, if the user is in a hurry, the service provider will prioritize alarm functions that can be delivered quickly. This allows for the provision of more important alarm functions by prioritizing alarm functions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then determine the priority of alarm functions.

[0094] The service provider can provide the most appropriate alarm function at the time of delivery, taking into account the user's geographical location information. For example, the service provider can provide the most relevant alarm function based on the user's current location. For example, the service provider can provide the most appropriate alarm function based on the user's travel plans. For example, the service provider can provide the most appropriate alarm function based on the user's past travel history. In this way, the service provider can provide the most appropriate alarm function by taking into account the user's geographical location information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's geographical location information into a generation AI, and the generation AI can provide the most appropriate alarm function.

[0095] The service provider can analyze the user's social media activity and propose a means of providing an alarm function at the time of provision. For example, the service provider can analyze the content of the user's social media posts and propose the optimal alarm function. For example, the service provider can refer to the user's location information on social media and propose the optimal alarm function. For example, the service provider can analyze the content of posts by the user's friends on social media and propose the optimal alarm function. In this way, the service provider can provide the optimal alarm function by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provider can input the user's social media activity data into a generative AI, and the generative AI can propose a means of providing an alarm function.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can collect user health data and combine it with weather information to control alarm activation. For example, the unit monitors the user's heart rate and sleep quality, and analyzes this data in conjunction with weather information. If the user is fatigued, the unit can delay the alarm activation. If the user has had enough sleep, the unit can activate the alarm as usual. This allows for more appropriate waking by adjusting the alarm activation according to the user's health condition.

[0098] The analysis department can analyze a user's past weather data usage history and select the optimal analysis method. For example, the analysis department prioritizes selecting weather information analysis methods that the user has frequently used in the past. The analysis department proposes the optimal analysis method for a specific time period based on the user's past weather data usage history. The analysis department analyzes the user's past weather data usage history and selects the most efficient analysis method. In this way, the optimal analysis method can be selected by analyzing the user's past weather data usage history.

[0099] The control unit can refer to the user's calendar information and control the alarm's sounding based on the user's current activity schedule. For example, the control unit will emphasize the alarm if the user has an important meeting or appointment. The control unit will also tone down the alarm if the user is on holiday. The control unit can adjust the alarm's sounding time based on the user's calendar information. This allows for more appropriate alarm control by tailoring the alarm's sounding to the user's current activity schedule.

[0100] The service provider can analyze users' social media activity and provide relevant alarm functions. For example, the service provider can analyze the content of users' social media posts and provide relevant alarm functions. The service provider can refer to users' location information on social media and provide relevant alarm functions. The service provider can analyze the content of posts by users' friends on social media and provide relevant alarm functions. In this way, by analyzing users' social media activity, relevant alarm functions can be provided.

[0101] The data collection unit can estimate the user's emotions and adjust the frequency of weather information collection based on those emotions. For example, if the user is stressed, the data collection unit increases the frequency of weather information collection to provide more detailed information. If the user is relaxed, the data collection unit decreases the frequency of weather information collection to provide only the minimum necessary information. If the user is in a hurry, the data collection unit optimizes the frequency of weather information collection to provide the necessary information quickly. In this way, by adjusting the frequency of weather information collection according to the user's emotions, more appropriate information can be provided.

[0102] The analysis unit can estimate the user's emotions and adjust the weather information analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit adjusts the analysis method to provide detailed weather information. If the user is relaxed, the analysis unit adjusts the analysis method to provide concise weather information. If the user is in a hurry, the analysis unit adjusts the analysis method to provide analysis results quickly. In this way, by adjusting the weather information analysis method according to the user's emotions, more appropriate analysis results can be provided.

[0103] The control unit can estimate the user's emotions and adjust the alarm sounding method based on the estimated emotions. For example, if the user is stressed, the control unit will sound the alarm less loudly. If the user is relaxed, the control unit will sound the alarm normally. If the user is in a hurry, the control unit will sound the alarm more loudly. This allows for more appropriate alarm control by adjusting the alarm sounding method according to the user's emotions.

[0104] The system can estimate the user's emotions and adjust how the alarm function is delivered based on those estimates. For example, if the user is stressed, the system will reduce the intensity of the alarm. If the user is relaxed, the system will deliver the alarm normally. If the user is in a hurry, the system will deliver the alarm quickly. By adjusting the alarm delivery method according to the user's emotions, the system can provide a more appropriate alarm function.

[0105] The system can estimate the user's emotions and prioritize alarm functions based on those emotions. For example, if the user is stressed, the system will prioritize important alarm functions. If the user is relaxed, the system will prioritize general alarm functions. If the user is in a hurry, the system will prioritize alarm functions that can be delivered quickly. This allows the system to prioritize alarm functions according to the user's emotions, thereby providing more important alarm functions.

[0106] The service provider can provide the most appropriate alarm function by considering the user's geographical location. For example, the service provider can provide the most relevant alarm function based on the user's current location. The service provider can provide the most appropriate alarm function based on the user's travel plans. The service provider can provide the most appropriate alarm function based on the user's past travel history. In this way, the service provider can provide the most appropriate alarm function by considering the user's geographical location.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The data collection unit gathers weather information for the current location. The data collection unit obtains weather information for the current location using web crawling or API connections. For example, the data collection unit can collect data from weather information websites on the internet or obtain real-time weather information from weather data providers. The data collection unit can also adjust the frequency of weather information collection using generative AI. Step 2: The analysis unit analyzes the weather information collected by the collection unit. The analysis unit analyzes the collected weather information and determines whether it is raining or not. The analysis unit can also adjust the method of analyzing the weather information using generative AI. Step 3: The control unit controls the alarm's activation based on the analysis results obtained by the analysis unit. For example, if it is raining at the alarm's set time, the control unit will not activate the alarm. The control unit can also use generated AI to adjust the alarm's activation control method. Step 4: The service unit provides an alarm function controlled by the control unit. For example, the service unit provides an alarm function to Healthcare Technologies' app. The service unit can also use generative AI to adjust how the alarm function is provided.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the collection unit, analysis unit, control unit, and provision unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects weather information around the current location using the camera 42 and communication I / F 44 of the smart device 14, and analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The control unit controls the sounding of an alarm by the specific processing unit 290 of the data processing unit 12, for example, and the provision unit provides the alarm function by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, analysis unit, control unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects weather information around the current location using the camera 42 and communication I / F 44 of the smart glasses 214, and analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The control unit controls the sounding of an alarm using the specific processing unit 290 of the data processing unit 12, for example, and the provision unit provides the alarm function using the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, analysis unit, control unit, and provision unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects weather information around the current location using the camera 42 and communication I / F 44 of the headset terminal 314, and analyzes the collected information using the specific processing unit 290 of the data processing unit 12. The control unit controls the sounding of alarms using the specific processing unit 290 of the data processing unit 12, for example, and the provision unit provides the alarm function using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, control unit, and supply unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects weather information around the current location using the camera 42 and communication I / F 44 of the robot 414, and analyzes the collected information by the specific processing unit 290 of the data processing unit 12. The control unit controls the sounding of alarms by the specific processing unit 290 of the data processing unit 12, for example, and the supply unit provides the alarm function by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A collection unit that collects weather information about the current location, An analysis unit analyzes the weather information collected by the aforementioned collection unit, A control unit that controls the sounding of an alarm based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides an alarm function controlled by the control unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect weather information for your current location using web crawling and API connections. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected weather information is analyzed to determine whether it is raining or not. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, If it is raining at the set alarm time, the alarm will not sound. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Providing an alarm function to Healthcare Technologies' app. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of weather information collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past weather data usage history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting weather information, filter it based on the user's current activity plans. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of weather information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting weather information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting weather information, we analyze users' social media activity and collect relevant weather information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is We estimate the user's emotions and adjust the weather information analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the weather information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the category of weather information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the weather information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of weather information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The control unit, The system estimates the user's emotions and adjusts the alarm control method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The control unit, During control, the accuracy of the control system is improved by considering the interrelationships of weather information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The control unit, During control, the system takes into account the user's past alarm usage history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, The system estimates the user's emotions and adjusts the order in which alarm sound control results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, During control, the control system takes into account the geographical distribution of weather information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During control, we improve control accuracy by referring to relevant literature on weather information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the alarm function is delivered based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the alarm, the system will refer to the user's past alarm usage history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the method of delivering the alarm function will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of alarm functions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the system will offer the optimal alarm function, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, we will analyze the user's social media activity and propose a method for providing the alarm function. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects weather information about the current location, An analysis unit analyzes the weather information collected by the aforementioned collection unit, A control unit that controls the sounding of an alarm based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides an alarm function controlled by the control unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect weather information for your current location using web crawling and API connections. The system according to feature 1.

3. The aforementioned analysis unit is The collected weather information is analyzed to determine whether it is raining or not. The system according to feature 1.

4. The control unit, If it is raining at the set alarm time, the alarm will not sound. The system according to feature 1.

5. The aforementioned supply unit is, Providing an alarm function to Healthcare Technologies' app. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of weather information collection based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past weather data usage history to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting weather information, filter it based on the user's current activity plans. The system according to feature 1.

Images