System

Abstract

To provide a system according to an embodiment that evaluates the reliability of a plurality of pieces of weather data, and provides a weather forecast with the highest probability.SOLUTION: A system according to an embodiment comprises a collection unit, an evaluation unit, a calculation unit, and a providing unit. The collection unit collects a plurality of pieces of weather data. The evaluation unit evaluates the reliability of each of the plurality of pieces of weather data collected by the collection unit. The calculation unit calculates a weather forecast with high probability on the basis of the reliability evaluated by the evaluation unit. The providing unit provides a user with the weather forecast calculated by the calculation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately evaluate the reliability of multiple meteorological data sets and provide the most probable weather forecast, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the reliability of multiple pieces of weather data and provide the most probable weather forecast. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an evaluation unit, a calculation unit, and a provision unit. The collection unit collects a plurality of weather data. The evaluation unit evaluates the reliability of each of the plurality of weather data collected by the collection unit. The calculation unit calculates a highly probable weather forecast based on the reliability evaluated by the evaluation unit. The provision unit provides the weather forecast calculated by the calculation unit to a user. Effect of the Invention

[0007] The system according to the embodiment can evaluate the reliability of multiple pieces of weather data and provide the most probable weather forecast. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A weather forecast system according to an embodiment of the present invention is a system that collects information from meteorological agencies, various weather information companies, and various weather forecasters around the world, averages the information, and provides the most probable weather forecast. This weather forecast system collects multiple weather data, evaluates the reliability of each of the collected weather data, calculates the most probable weather forecast based on the evaluated reliability, and provides the calculated weather forecast to a user. For example, the weather forecast system collects weather data from meteorological agencies, various weather information companies, and various weather forecasters around the world. This collected data may include AI processing. Next, the reliability of each of the collected weather data is evaluated. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. Based on the evaluated reliability, the most probable weather forecast is calculated. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the most probable weather forecast is calculated based on this weighted average. This calculation process also includes AI processing. Finally, the calculated weather forecast is provided to the user. The provision unit provides the weather forecast through a website, an application, an email, or an SMS. This provision may also include AI processing. As a result, the weather forecast system can accumulate weather data from all over the world and provide the most probable weather forecast. For example, reliability can be evaluated for each region, and weather forecasts can be calculated for each region. In addition, weather data from each meteorological agency, weather information company, and weather forecaster can be collected, converted into a unified format, and reliability can be evaluated. Furthermore, by providing weather forecasts through websites, applications, email, or SMS, users can easily obtain weather forecasts. As a result, the weather forecast system can accumulate weather data from all over the world and provide the most probable weather forecast.

[0029] The weather forecast system according to the embodiment includes a collection unit, an evaluation unit, a calculation unit, and a provision unit. The collection unit collects a plurality of weather data. The plurality of weather data includes, for example, temperature data, precipitation data, wind speed data, and the like, but is not limited to such examples. The collection unit collects, for example, weather data from meteorological agencies around the world, various weather information companies, and various weather forecasters. The collected data may include AI processing. For example, the collection unit may collect temperature data provided by each meteorological agency, collect precipitation data provided by weather information companies, and collect wind speed data provided by weather forecasters. The evaluation unit evaluates the reliability of each of the collected weather data. The reliability includes, for example, the reliability of the data provider, the consistency of the data, the accuracy of the data, and the like, but is not limited to such examples. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. For example, the evaluation unit converts the collected temperature data into a unified format and evaluates the reliability based on the converted data. The evaluation unit can also convert the collected precipitation data into a unified format and evaluate the reliability based on the converted data. The evaluation unit can also convert the collected wind speed data into a unified format and evaluate the reliability based on the converted data. The calculation unit calculates the most probable weather forecast based on the evaluated reliability. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the calculation unit calculates the most probable weather forecast based on the weighted average. This calculation process also includes AI processing. For example, the calculation unit weights the evaluated temperature data and calculates a weighted average. The calculation unit can also weight the evaluated precipitation data and calculate a weighted average. Furthermore, the calculation unit can weight the evaluated wind speed data and calculate a weighted average. The provision unit provides the calculated weather forecast to a user. The provision unit provides the weather forecast through a website, an application, an email, or an SMS. This provision may also include AI processing. For example, the provision unit provides the calculated weather forecast through a website. The provision unit can also provide the calculated weather forecast through an application.Furthermore, the providing unit can provide the calculated weather forecast through email. As a result, the weather forecasting system according to the embodiment can accumulate weather data from all over the world and provide the most probable weather forecast. For example, the reliability can be evaluated for each region, and the weather forecast can be calculated for each region. In addition, weather data from each meteorological agency, weather information company, and weather forecaster can be collected, converted into a unified format, and the reliability can be evaluated. Furthermore, by providing the weather forecast through a website, an application, email, or SMS, the user can easily obtain the weather forecast.

[0030] The collection unit collects multiple weather data. The multiple weather data include, but are not limited to, temperature data, precipitation data, and wind speed data, for example. The collection unit collects weather data from, for example, meteorological agencies around the world, various weather information companies, and various weather forecasters. The collected data may include AI processing. For example, the collection unit can collect temperature data provided by each meteorological agency, precipitation data provided by weather information companies, and wind speed data provided by weather forecasters. The collection unit acquires these data in real time and stores them in a central database. Data collection can be performed by automatically acquiring data using an API, or by periodically downloading and updating data. Furthermore, in order to ensure the quality of the data, the collection unit detects missing or abnormal values ​​in the data and performs correction or filtering as necessary. For example, if abnormal temperature data is detected, the data can be ignored or corrected based on past data. The collection unit can utilize distributed processing technology and cloud computing to perform these processes efficiently. This allows the collection unit to quickly and accurately collect large amounts of weather data and improve the performance of the entire system.

[0031] The evaluation unit evaluates the reliability of each of the collected weather data. Reliability includes, for example, the reliability of the data provider, the consistency of the data, the accuracy of the data, and the like, but is not limited to such examples. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. For example, the evaluation unit converts the collected temperature data into a unified format and evaluates the reliability based on the converted data. The evaluation unit can also convert the collected precipitation data into a unified format and evaluate the reliability based on the converted data. Furthermore, the evaluation unit can convert the collected wind speed data into a unified format and evaluate the reliability based on the converted data. When evaluating the reliability of the data using AI, the evaluation unit utilizes past data and statistical information. For example, it checks whether there are any abnormal values ​​compared with past weather data, and if an abnormality is detected, evaluates the reliability of the data as low. In addition, in order to evaluate the reliability of the data provider, past performance and evaluation of the provider can be taken into consideration. Based on these evaluation results, the evaluation unit assigns a reliability score to each data and uses it for weighting the data. This enables the evaluation unit to evaluate the reliability of the collected weather data with high accuracy, thereby improving the prediction accuracy of the entire system.

[0032] The calculation unit calculates the most probable weather forecast based on the evaluated reliability. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the calculation unit calculates the most probable weather forecast based on this weighted average. This calculation process also includes AI processing. For example, the calculation unit weights the evaluated temperature data and calculates a weighted average. The calculation unit can also weight the evaluated precipitation data and calculate a weighted average. Furthermore, the calculation unit can weight the evaluated wind speed data and calculate a weighted average. The calculation unit uses AI to integrate multiple weather data and generate the most probable weather forecast. For example, a machine learning algorithm is used to compare past weather data with current data and predict future weather patterns. Furthermore, the calculation unit identifies the most probable forecast by integrating information from different data sources and simulating multiple scenarios. This allows the calculation unit to provide a reliable weather forecast and provide information for a user to take appropriate measures.

[0033] The providing unit provides the calculated weather forecast to the user. The providing unit provides the weather forecast through a website, an application, email, or SMS. This provision may also include AI processing. For example, the providing unit provides the calculated weather forecast through a website. The providing unit may also provide the calculated weather forecast through an application. Furthermore, the providing unit may also provide the calculated weather forecast through email. The providing unit provides the weather forecast through multiple platforms, taking into consideration the convenience of the user. For example, the website provides detailed weather forecasts, past data, explanations of the forecast model, etc., so that the user can understand deeply. The application provides real-time weather forecasts based on the user's current location and important information immediately by utilizing notification functions. Periodic weather forecasts and emergency alerts are quickly transmitted through email and SMS. Furthermore, the providing unit collects user feedback and continuously improves the accuracy and convenience of the information provided. For example, a function is provided that allows users to send ratings and comments on the provided weather forecast, and the forecast model and provision method can be reviewed based on the feedback. This enables the providing unit to provide the user with a highly reliable weather forecast quickly and effectively, thereby improving the safety and convenience of the user.

[0034] The evaluation unit includes a conversion unit that converts the collected data into a unified format, and a reliability evaluation unit that evaluates reliability based on the converted data. The evaluation unit includes a conversion unit that converts the collected data into a unified format. The unified format includes, for example, a data structure, a data unit, a data format, and the like, but is not limited to such examples. For example, the conversion unit converts the collected temperature data into a unified format. The conversion unit can also convert the collected precipitation data into a unified format. Furthermore, the conversion unit can also convert the collected wind speed data into a unified format. The reliability evaluation unit evaluates reliability based on the converted data. The reliability includes, for example, the reliability of the data provider, the consistency of the data, the accuracy of the data, and the like, but is not limited to such examples. For example, the reliability evaluation unit evaluates the reliability of the converted temperature data. The reliability evaluation unit can also evaluate the reliability of the converted precipitation data. Furthermore, the reliability evaluation unit can also evaluate the reliability of the converted wind speed data. This improves the accuracy of evaluating the reliability of the collected data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can have the generation AI perform a process of converting the collected data into a unified format.

[0035] The calculation unit includes a first calculation processing unit that weights the evaluated data and calculates a weighted average, and a second calculation processing unit that calculates a weather forecast with a high probability based on the weighted average. The calculation unit includes a first calculation processing unit that weights the evaluated data and calculates a weighted average. The weighting includes, for example, the importance of the data, the reliability of the data, and the influence of the data, but is not limited to such examples. For example, the first calculation processing unit weights the evaluated temperature data and calculates a weighted average. The first calculation processing unit can also weight the evaluated precipitation data and calculate a weighted average. Furthermore, the first calculation processing unit can also weight the evaluated wind speed data and calculate a weighted average. The second calculation processing unit calculates a weather forecast with a high probability based on the weighted average. The weather forecast with a high probability includes, for example, a statistical method, a machine learning algorithm, and the like, but is not limited to such examples. For example, the second calculation processing unit calculates a temperature forecast based on the weighted average. The second calculation processing unit can also calculate a precipitation forecast based on the weighted average. Furthermore, the second calculation processing unit can also calculate a wind speed forecast based on a weighted average. This improves the accuracy of calculating the most probable weather forecast. Some or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can cause the generation AI to perform a process of weighting the evaluated data.

[0036] The evaluation unit can evaluate the reliability for each region. The evaluation unit evaluates the reliability for each region. For each region, for example, each country, each state, each city, ward, town, and village, etc. are included, but are not limited to such examples. For example, the evaluation unit evaluates the reliability for each country. The evaluation unit can also evaluate the reliability for each state. Furthermore, the evaluation unit can also evaluate the reliability for each city, ward, town, and village. As a result, by evaluating the reliability for each region, more accurate weather forecasting is possible. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can cause the generation AI to execute a process of evaluating the reliability for each region.

[0037] The calculation unit can calculate a weather forecast for each region. For example, each region includes, but is not limited to, each country, each state, each city, each town, each ward, each town, each village, and the like. For example, the calculation unit calculates a weather forecast for each country. The calculation unit can also calculate a weather forecast for each state. Furthermore, the calculation unit can also calculate a weather forecast for each city, each town, each ward, each town, each village. This allows for more accurate forecasting by calculating a weather forecast for each region. Some or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can cause the generation AI to execute a process of calculating a weather forecast for each region.

[0038] The collection unit can collect weather data from meteorological agencies, weather information companies, and weather forecasters. The weather data includes, for example, temperature data, precipitation data, wind speed data, and the like, but is not limited to such examples. For example, the collection unit can collect temperature data provided by each meteorological agency, collect precipitation data provided by weather information companies, and collect wind speed data provided by weather forecasters. This improves the accuracy of forecasts by collecting weather data from various information sources. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data provided by the meteorological agency to the generation AI and cause the generation AI to collect data.

[0039] The providing unit can provide the weather forecast through a website, an application, an email, or an SMS. The providing method includes, but is not limited to, for example, the URL of the website, the name of the application, the format of the email, and the sending method of the SMS. For example, the providing unit provides the calculated weather forecast through a website. The providing unit can also provide the calculated weather forecast through an application. Furthermore, the providing unit can also provide the calculated weather forecast through an email. This allows the user to easily obtain the forecast by providing the weather forecast in various ways. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the contents of the weather forecast to be provided through the website to the generating AI and cause the generating AI to execute the providing method.

[0040] The collection unit can analyze the past weather data collection history and select the optimal collection method. The optimal collection method includes, for example, the type of data, the collection frequency, the collection means, etc., but is not limited to such examples. For example, the collection unit identifies the most reliable data source from the past collection history and collects it preferentially. The collection unit can also improve the accuracy of data collected in a specific time period based on the past collection history. Furthermore, the collection unit can analyze the past collection history and optimize the collection frequency. As a result, the optimal collection method can be selected by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past collection history data to the generation AI and cause the generation AI to select the optimal collection method.

[0041] The collection unit can filter the weather data based on specific weather conditions or abnormal weather when collecting the weather data. The specific weather conditions or abnormal weather include, but are not limited to, typhoons, heavy rain, and extreme heat. For example, when abnormal weather occurs, the collection unit preferentially collects the data. The collection unit can also filter the data based on specific weather conditions (e.g., typhoons and heavy rain) to extract important information. Furthermore, when there is a sign of abnormal weather, the collection unit can collect the data and provide it as early warning information. In this way, important information can be preferentially collected by filtering the data based on specific weather conditions or abnormal weather. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause the generation AI to filter the data based on specific weather conditions or abnormal weather.

[0042] When collecting weather data, the collection unit can preferentially collect highly relevant data in consideration of geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and map data. For example, the collection unit collects the most relevant weather data based on the user's current location. The collection unit can also preferentially collect weather data in a specific area and provide it to a user in that area. Furthermore, the collection unit can preferentially collect data in an area where abnormal weather is occurring based on the geographical location information. This makes it possible to preferentially collect highly relevant data by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0043] The collection unit can analyze information from social media and news sites when collecting weather data, and collect related data. Social media and news sites include, but are not limited to, for example, names of specific social media and lists of news sites. For example, the collection unit can analyze posts on social media and collect information on abnormal weather. The collection unit can also analyze articles on news sites and extract important weather information. Furthermore, the collection unit can expand the collection range of weather data based on information from social media and news sites. This makes it possible to collect related data by analyzing information from social media and news sites. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information from social media and news sites to the generation AI and cause the generation AI to collect related data.

[0044] The evaluation unit can optimize the reliability evaluation algorithm by referring to past evaluation data at the time of evaluation. The past evaluation data includes, for example, past evaluation results, evaluation history, databases, etc., but is not limited to such examples. For example, the evaluation unit adjusts parameters of the reliability evaluation algorithm based on the past evaluation data. The evaluation unit can also analyze the past evaluation data and improve the accuracy of the evaluation algorithm. Furthermore, the evaluation unit can optimize the evaluation criteria by referring to the past evaluation data. As a result, the reliability evaluation algorithm can be optimized by referring to the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation data to the generation AI and cause the generation AI to execute optimization of the reliability evaluation algorithm.

[0045] The evaluation unit can perform the evaluation while taking into consideration the past reliability history of the data provider. The past reliability history of the data provider includes, for example, the evaluation history, reliability score, and past data provision performance of the data provider, but is not limited to such examples. For example, the evaluation unit evaluates the reliability of the current data based on the past reliability history of the data provider. The evaluation unit can also adjust the evaluation criteria by referring to the reliability history of the provider. Furthermore, the evaluation unit can also weight the data by taking into consideration the past reliability history. This enables more accurate evaluation by taking into consideration the past reliability history of the data provider. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past reliability history of the data provider to the generation AI and cause the generation AI to execute the reliability evaluation.

[0046] The evaluation unit can evaluate the reliability by taking into account the geographical distribution of the data during the evaluation. The geographical distribution includes, for example, the distribution range of the data, the amount of data per region, and geographical bias, but is not limited to such examples. For example, the evaluation unit evaluates the reliability of a specific region based on the geographical distribution of the data. The evaluation unit can also adjust the evaluation criteria by taking into account the geographical distribution. Furthermore, the evaluation unit can weight the data based on the geographical distribution. This enables a more accurate reliability evaluation by taking into account the geographical distribution of the data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the geographical distribution of the data to the generation AI and cause the generation AI to execute the reliability evaluation.

[0047] The evaluation unit can evaluate the reliability by referring to related scientific papers and research data during the evaluation. The scientific papers and research data include, for example, a list of specific scientific papers, names of research databases, and the like, but are not limited to such examples. For example, the evaluation unit evaluates the reliability of the data by referring to related scientific papers. The evaluation unit can also adjust the evaluation criteria based on the research data. Furthermore, the evaluation unit can also weight the data by referring to the scientific papers and research data. As a result, the accuracy of the reliability evaluation is improved by referring to the related scientific papers and research data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the scientific papers and research data to the generation AI and cause the generation AI to execute the reliability evaluation.

[0048] The calculation unit can optimize the forecast algorithm by referring to past weather forecast data during calculation. The past weather forecast data includes, for example, past forecast results, forecast history, databases, etc., but is not limited to such examples. For example, the calculation unit adjusts parameters of the forecast algorithm based on the past weather forecast data. The calculation unit can also analyze the past weather forecast data to improve the accuracy of the forecast algorithm. Furthermore, the calculation unit can also optimize the forecast criteria by referring to the past weather forecast data. As a result, the accuracy of the forecast algorithm is improved by referring to the past weather forecast data. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the past weather forecast data to the generation AI and cause the generation AI to execute optimization of the forecast algorithm.

[0049] The calculation unit can improve the accuracy of the forecast based on specific weather conditions or abnormal weather at the time of calculation. The specific weather conditions or abnormal weather include, but are not limited to, typhoons, heavy rain, and extreme heat. For example, the calculation unit adjusts the forecast algorithm based on specific weather conditions (e.g., typhoons and heavy rain). In addition, when abnormal weather occurs, the calculation unit can improve the accuracy of the forecast based on the data. Furthermore, the calculation unit can weight the forecast based on the specific weather conditions or abnormal weather. This makes it possible to improve the accuracy of the forecast based on the specific weather conditions or abnormal weather. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can cause the generation AI to execute the improvement of the accuracy of the forecast based on the specific weather conditions or abnormal weather.

[0050] The calculation unit can calculate the weather forecast for each region taking into account the geographical location information during the calculation. The geographical location information includes, but is not limited to, for example, GPS data, address information, map data, etc. For example, the calculation unit calculates a weather forecast for a specific region based on the geographical location information. The calculation unit can also adjust a forecast algorithm for each region taking into account the geographical location information. Furthermore, the calculation unit can weight the forecast for each region based on the geographical location information. In this way, the weather forecast for each region can be calculated with high accuracy by taking into account the geographical location information. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the geographical location information to the generation AI and cause the generation AI to calculate the weather forecast for each region.

[0051] The calculation unit can improve the accuracy of the forecast by referring to the related weather model and simulation data at the time of calculation. The weather model and the simulation data include, for example, the name of a specific weather model, a list of simulation data, and the like, but are not limited to such examples. For example, the calculation unit improves the accuracy of the forecast algorithm by referring to the related weather model. The calculation unit can also adjust the parameters of the forecast algorithm based on the simulation data. Furthermore, the calculation unit can also weight the forecast by referring to the weather model and the simulation data. As a result, the accuracy of the forecast is improved by referring to the related weather model and the simulation data. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the weather model and the simulation data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the forecast.

[0052] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. The past usage history includes, for example, past access history, usage patterns, databases, etc., but is not limited to such examples. For example, the providing unit selects the optimal providing method based on the user's past usage history. The providing unit can also customize the providing method by referring to the past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the most efficient providing method. As a result, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history to the generating AI and cause the generating AI to select the optimal providing method.

[0053] The providing unit can customize the provided content based on specific weather conditions or abnormal weather at the time of providing. The specific weather conditions or abnormal weather include, but are not limited to, typhoons, heavy rain, and extreme heat. For example, when abnormal weather occurs, the providing unit provides the information on the occurrence of the abnormal weather with priority. The providing unit can also provide a detailed forecast based on specific weather conditions (e.g., typhoons and heavy rain). Furthermore, when there is a sign of abnormal weather, the providing unit can also provide the information early. This enables more appropriate information provision by customizing the provided content based on specific weather conditions or abnormal weather. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can cause the generating AI to execute customization of the provided content based on specific weather conditions or abnormal weather.

[0054] The providing unit can select the optimal providing method in consideration of the device information of the user at the time of providing. The device information includes, for example, the type of device, the version of the OS, the type of browser, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides a providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. This makes it possible to select the optimal providing method by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0055] The providing unit can provide the weather forecast through social media or a news site at the time of providing. The social media or news site includes, for example, the name of a specific social media, a list of news sites, and the like, but is not limited to such examples. For example, the providing unit provides the latest weather forecast through social media. The providing unit can also post the weather forecast on a news site to provide information widely. Furthermore, the providing unit can also quickly provide information on abnormal weather through social media or a news site. As a result, by providing the weather forecast through social media or a news site, information can be provided widely. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the contents of the weather forecast to be provided through social media or a news site to the generating AI, and cause the generating AI to execute the providing method.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] When collecting weather data, the collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing. For example, if the user has frequently checked weather information during a specific time period in the past, the collection unit collects data according to that time period. In addition, if the user has a tendency to check weather information before a specific event (e.g., commuting, traveling), the collection unit can also collect data according to the event. Furthermore, the collection unit can adjust the collection timing according to a specific day of the week or season based on the user's past behavioral patterns. This allows data to be collected at a more appropriate timing by optimizing the collection timing based on the user's behavioral patterns.

[0058] The evaluation unit can take into account the past performance of the data provider when evaluating the reliability of the data. For example, the evaluation unit can highly evaluate the reliability of a data provider that has provided accurate forecasts in the past. The evaluation unit can also low the reliability of a data provider that has provided erroneous forecasts in the past. Furthermore, the evaluation unit can dynamically adjust the reliability evaluation criteria based on the past performance of the data provider. This allows for a more accurate reliability evaluation by taking into account the past performance of the data provider.

[0059] When calculating the weather forecast, the calculation unit can customize the forecast by taking into account the user's current location information. For example, the calculation unit calculates the forecast by preferentially using weather data for the area where the user is currently located. In addition, when the user is moving, the calculation unit can also provide a forecast by taking into account weather data for the destination. Furthermore, when the user frequently visits a particular area, the calculation unit can also customize the forecast based on weather data for that area. In this way, by customizing the forecast based on the user's location information, a more relevant weather forecast can be provided.

[0060] The providing unit can adjust the providing method in consideration of the remaining battery level of the user's device when providing a weather forecast. For example, the providing unit provides a simple and power-saving display method when the battery of the user's device is low. The providing unit can also provide a display method including detailed information when the battery is sufficient. Furthermore, the providing unit can adjust the frequency and content of notifications according to the remaining battery level. This makes it possible to provide more appropriate information by optimizing the providing method according to the battery status of the user's device.

[0061] When collecting weather data, the collection unit can expand the collection range based on specific weather conditions or abnormal weather. For example, if a typhoon is approaching, the collection unit will prioritize collecting data within its affected range. In addition, if heavy rain is expected, the collection unit can also collect detailed precipitation data for that area. Furthermore, if a heat wave is expected, the collection unit can also focus on collecting temperature data for that area. In this way, expanding the collection range based on specific weather conditions or abnormal weather enables more accurate weather forecasts.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects multiple weather data. The multiple weather data include, for example, temperature data, precipitation data, wind speed data, etc. The collection unit collects weather data from meteorological agencies, various weather information companies, and various weather forecasters around the world. This collected data can include AI processing. Step 2: The evaluation unit evaluates the reliability of each of the collected weather data. Reliability includes the reliability of the data source, the consistency of the data, and the accuracy of the data. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. Step 3: The calculation unit calculates the most probable weather forecast based on the evaluated reliability. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the calculation unit calculates the most probable weather forecast based on this weighted average. This calculation process also includes AI processing. Step 4: The providing unit provides the calculated weather forecast to the user. The providing unit provides the weather forecast through a website, an application, email, or SMS. This provision may also include AI processing.

[0064] (Example 2) A weather forecast system according to an embodiment of the present invention is a system that collects information from meteorological agencies, various weather information companies, and various weather forecasters around the world, averages the information, and provides the most probable weather forecast. This weather forecast system collects multiple weather data, evaluates the reliability of each of the collected weather data, calculates the most probable weather forecast based on the evaluated reliability, and provides the calculated weather forecast to a user. For example, the weather forecast system collects weather data from meteorological agencies, various weather information companies, and various weather forecasters around the world. This collected data may include AI processing. Next, the reliability of each of the collected weather data is evaluated. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. Based on the evaluated reliability, the most probable weather forecast is calculated. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the most probable weather forecast is calculated based on this weighted average. This calculation process also includes AI processing. Finally, the calculated weather forecast is provided to the user. The provision unit provides the weather forecast through a website, an application, an email, or an SMS. This provision may also include AI processing. As a result, the weather forecast system can accumulate weather data from all over the world and provide the most probable weather forecast. For example, reliability can be evaluated for each region, and weather forecasts can be calculated for each region. In addition, weather data from each meteorological agency, weather information company, and weather forecaster can be collected, converted into a unified format, and reliability can be evaluated. Furthermore, by providing weather forecasts through websites, applications, email, or SMS, users can easily obtain weather forecasts. As a result, the weather forecast system can accumulate weather data from all over the world and provide the most probable weather forecast.

[0065] The weather forecast system according to the embodiment includes a collection unit, an evaluation unit, a calculation unit, and a provision unit. The collection unit collects a plurality of weather data. The plurality of weather data includes, for example, temperature data, precipitation data, wind speed data, and the like, but is not limited to such examples. The collection unit collects, for example, weather data from meteorological agencies around the world, various weather information companies, and various weather forecasters. The collected data may include AI processing. For example, the collection unit may collect temperature data provided by each meteorological agency, collect precipitation data provided by weather information companies, and collect wind speed data provided by weather forecasters. The evaluation unit evaluates the reliability of each of the collected weather data. The reliability includes, for example, the reliability of the data provider, the consistency of the data, the accuracy of the data, and the like, but is not limited to such examples. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. For example, the evaluation unit converts the collected temperature data into a unified format and evaluates the reliability based on the converted data. The evaluation unit can also convert the collected precipitation data into a unified format and evaluate the reliability based on the converted data. The evaluation unit can also convert the collected wind speed data into a unified format and evaluate the reliability based on the converted data. The calculation unit calculates the most probable weather forecast based on the evaluated reliability. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the calculation unit calculates the most probable weather forecast based on the weighted average. This calculation process also includes AI processing. For example, the calculation unit weights the evaluated temperature data and calculates a weighted average. The calculation unit can also weight the evaluated precipitation data and calculate a weighted average. Furthermore, the calculation unit can weight the evaluated wind speed data and calculate a weighted average. The provision unit provides the calculated weather forecast to a user. The provision unit provides the weather forecast through a website, an application, an email, or an SMS. This provision may also include AI processing. For example, the provision unit provides the calculated weather forecast through a website. The provision unit can also provide the calculated weather forecast through an application.Furthermore, the providing unit can provide the calculated weather forecast through email. As a result, the weather forecasting system according to the embodiment can accumulate weather data from all over the world and provide the most probable weather forecast. For example, the reliability can be evaluated for each region, and the weather forecast can be calculated for each region. In addition, weather data from each meteorological agency, weather information company, and weather forecaster can be collected, converted into a unified format, and the reliability can be evaluated. Furthermore, by providing the weather forecast through a website, an application, email, or SMS, the user can easily obtain the weather forecast.

[0066] The collection unit collects multiple weather data. The multiple weather data include, but are not limited to, temperature data, precipitation data, and wind speed data, for example. The collection unit collects weather data from, for example, meteorological agencies around the world, various weather information companies, and various weather forecasters. The collected data may include AI processing. For example, the collection unit can collect temperature data provided by each meteorological agency, precipitation data provided by weather information companies, and wind speed data provided by weather forecasters. The collection unit acquires these data in real time and stores them in a central database. Data collection can be performed by automatically acquiring data using an API, or by periodically downloading and updating data. Furthermore, in order to ensure the quality of the data, the collection unit detects missing or abnormal values ​​in the data and performs correction or filtering as necessary. For example, if abnormal temperature data is detected, the data can be ignored or corrected based on past data. The collection unit can utilize distributed processing technology and cloud computing to perform these processes efficiently. This allows the collection unit to quickly and accurately collect large amounts of weather data and improve the performance of the entire system.

[0067] The evaluation unit evaluates the reliability of each of the collected weather data. Reliability includes, for example, the reliability of the data provider, the consistency of the data, the accuracy of the data, and the like, but is not limited to such examples. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. For example, the evaluation unit converts the collected temperature data into a unified format and evaluates the reliability based on the converted data. The evaluation unit can also convert the collected precipitation data into a unified format and evaluate the reliability based on the converted data. Furthermore, the evaluation unit can convert the collected wind speed data into a unified format and evaluate the reliability based on the converted data. When evaluating the reliability of the data using AI, the evaluation unit utilizes past data and statistical information. For example, it checks whether there are any abnormal values ​​compared with past weather data, and if an abnormality is detected, evaluates the reliability of the data as low. In addition, in order to evaluate the reliability of the data provider, past performance and evaluation of the provider can be taken into consideration. Based on these evaluation results, the evaluation unit assigns a reliability score to each data and uses it for weighting the data. This enables the evaluation unit to evaluate the reliability of the collected weather data with high accuracy, thereby improving the prediction accuracy of the entire system.

[0068] The calculation unit calculates the most probable weather forecast based on the evaluated reliability. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the calculation unit calculates the most probable weather forecast based on this weighted average. This calculation process also includes AI processing. For example, the calculation unit weights the evaluated temperature data and calculates a weighted average. The calculation unit can also weight the evaluated precipitation data and calculate a weighted average. Furthermore, the calculation unit can weight the evaluated wind speed data and calculate a weighted average. The calculation unit uses AI to integrate multiple weather data and generate the most probable weather forecast. For example, a machine learning algorithm is used to compare past weather data with current data and predict future weather patterns. Furthermore, the calculation unit identifies the most probable forecast by integrating information from different data sources and simulating multiple scenarios. This allows the calculation unit to provide a reliable weather forecast and provide information for a user to take appropriate measures.

[0069] The providing unit provides the calculated weather forecast to the user. The providing unit provides the weather forecast through a website, an application, email, or SMS. This provision may also include AI processing. For example, the providing unit provides the calculated weather forecast through a website. The providing unit may also provide the calculated weather forecast through an application. Furthermore, the providing unit may also provide the calculated weather forecast through email. The providing unit provides the weather forecast through multiple platforms, taking into consideration the convenience of the user. For example, the website provides detailed weather forecasts, past data, explanations of the forecast model, etc., so that the user can understand deeply. The application provides real-time weather forecasts based on the user's current location and important information immediately by utilizing notification functions. Periodic weather forecasts and emergency alerts are quickly transmitted through email and SMS. Furthermore, the providing unit collects user feedback and continuously improves the accuracy and convenience of the information provided. For example, a function is provided that allows users to send ratings and comments on the provided weather forecast, and the forecast model and provision method can be reviewed based on the feedback. This enables the providing unit to provide the user with a highly reliable weather forecast quickly and effectively, thereby improving the safety and convenience of the user.

[0070] The evaluation unit includes a conversion unit that converts the collected data into a unified format, and a reliability evaluation unit that evaluates reliability based on the converted data. The evaluation unit includes a conversion unit that converts the collected data into a unified format. The unified format includes, for example, a data structure, a data unit, a data format, and the like, but is not limited to such examples. For example, the conversion unit converts the collected temperature data into a unified format. The conversion unit can also convert the collected precipitation data into a unified format. Furthermore, the conversion unit can also convert the collected wind speed data into a unified format. The reliability evaluation unit evaluates reliability based on the converted data. The reliability includes, for example, the reliability of the data provider, the consistency of the data, the accuracy of the data, and the like, but is not limited to such examples. For example, the reliability evaluation unit evaluates the reliability of the converted temperature data. The reliability evaluation unit can also evaluate the reliability of the converted precipitation data. Furthermore, the reliability evaluation unit can also evaluate the reliability of the converted wind speed data. This improves the accuracy of evaluating the reliability of the collected data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can have the generation AI perform a process of converting the collected data into a unified format.

[0071] The calculation unit includes a first calculation processing unit that weights the evaluated data and calculates a weighted average, and a second calculation processing unit that calculates a weather forecast with a high probability based on the weighted average. The calculation unit includes a first calculation processing unit that weights the evaluated data and calculates a weighted average. The weighting includes, for example, the importance of the data, the reliability of the data, and the influence of the data, but is not limited to such examples. For example, the first calculation processing unit weights the evaluated temperature data and calculates a weighted average. The first calculation processing unit can also weight the evaluated precipitation data and calculate a weighted average. Furthermore, the first calculation processing unit can also weight the evaluated wind speed data and calculate a weighted average. The second calculation processing unit calculates a weather forecast with a high probability based on the weighted average. The weather forecast with a high probability includes, for example, a statistical method, a machine learning algorithm, and the like, but is not limited to such examples. For example, the second calculation processing unit calculates a temperature forecast based on the weighted average. The second calculation processing unit can also calculate a precipitation forecast based on the weighted average. Furthermore, the second calculation processing unit can also calculate a wind speed forecast based on a weighted average. This improves the accuracy of calculating the most probable weather forecast. Some or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can cause the generation AI to perform a process of weighting the evaluated data.

[0072] The evaluation unit can evaluate the reliability for each region. For example, each region includes, but is not limited to, each country, each state, each city, each town, each ward, each town, each village, and the like. For example, the evaluation unit evaluates the reliability for each country. The evaluation unit can also evaluate the reliability for each state. Furthermore, the evaluation unit can also evaluate the reliability for each city, each town, each ward, each town, each village. As a result, by evaluating the reliability for each region, more accurate weather forecasting is possible. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can cause the generation AI to execute a process of evaluating the reliability for each region.

[0073] The calculation unit can calculate a weather forecast for each region. For example, each region includes, but is not limited to, each country, each state, each city, each town, each ward, each town, each village, and the like. For example, the calculation unit calculates a weather forecast for each country. The calculation unit can also calculate a weather forecast for each state. Furthermore, the calculation unit can also calculate a weather forecast for each city, each town, each ward, each town, each village. This allows for more accurate forecasting by calculating a weather forecast for each region. Some or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can cause the generation AI to execute a process of calculating a weather forecast for each region.

[0074] The collection unit can collect weather data from meteorological agencies, weather information companies, and weather forecasters. The weather data includes, for example, temperature data, precipitation data, wind speed data, and the like, but is not limited to such examples. For example, the collection unit can collect temperature data provided by each meteorological agency, collect precipitation data provided by weather information companies, and collect wind speed data provided by weather forecasters. This improves the accuracy of forecasts by collecting weather data from various information sources. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input data provided by the meteorological agency to the generation AI and cause the generation AI to collect data.

[0075] The providing unit can provide the weather forecast through a website, an application, an email, or an SMS. The providing method includes, but is not limited to, for example, the URL of the website, the name of the application, the format of the email, and the sending method of the SMS. For example, the providing unit provides the calculated weather forecast through a website. The providing unit can also provide the calculated weather forecast through an application. Furthermore, the providing unit can also provide the calculated weather forecast through an email. This allows the user to easily obtain the forecast by providing the weather forecast in various ways. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the contents of the weather forecast to be provided through the website to the generating AI and cause the generating AI to execute the providing method.

[0076] The collection unit can estimate the user's emotion and adjust the collection timing of the weather data based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, anxiety, relaxation, and hurry. For example, when the user feels anxious, the collection unit frequently collects weather data and provides the latest information. In addition, when the user is relaxed, the collection unit can also collect weather data at a normal collection timing. Furthermore, when the user is in a hurry, the collection unit can also quickly collect weather data and provide it immediately. In this way, by adjusting the collection timing according to the user's emotion, data can be collected at a more appropriate timing. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the collection timing based on the emotion.

[0077] The collection unit can analyze the past weather data collection history and select the optimal collection method. The optimal collection method includes, for example, the type of data, the collection frequency, the collection means, etc., but is not limited to such examples. For example, the collection unit identifies the most reliable data source from the past collection history and collects it preferentially. The collection unit can also improve the accuracy of data collected in a specific time period based on the past collection history. Furthermore, the collection unit can analyze the past collection history and optimize the collection frequency. As a result, the optimal collection method can be selected by analyzing the past collection history. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the past collection history data to the generation AI and cause the generation AI to select the optimal collection method.

[0078] The collection unit can filter the weather data based on specific weather conditions or abnormal weather when collecting the weather data. The specific weather conditions or abnormal weather include, but are not limited to, typhoons, heavy rain, and extreme heat. For example, when abnormal weather occurs, the collection unit preferentially collects the data. The collection unit can also filter the data based on specific weather conditions (e.g., typhoons and heavy rain) to extract important information. Furthermore, when there is a sign of abnormal weather, the collection unit can collect the data and provide it as early warning information. In this way, important information can be preferentially collected by filtering the data based on specific weather conditions or abnormal weather. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can cause the generation AI to filter the data based on specific weather conditions or abnormal weather.

[0079] The collection unit can estimate the user's emotion and determine the priority of the weather data to be collected based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, anxiety, relaxation, and hurry. For example, when the user feels anxious, the collection unit prioritizes collecting important weather data. In addition, when the user is relaxed, the collection unit can also collect weather data with normal priority. Furthermore, when the user is in a hurry, the collection unit can also prioritize data that can be collected quickly. In this way, more appropriate data can be collected by determining the priority of data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of data based on the emotion.

[0080] When collecting weather data, the collection unit can preferentially collect highly relevant data in consideration of geographical location information. Examples of geographical location information include, but are not limited to, GPS data, address information, and map data. For example, the collection unit collects the most relevant weather data based on the user's current location. The collection unit can also preferentially collect weather data in a specific area and provide it to a user in that area. Furthermore, the collection unit can preferentially collect data in an area where abnormal weather is occurring based on the geographical location information. This makes it possible to preferentially collect highly relevant data by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0081] The collection unit can analyze information from social media and news sites when collecting weather data, and collect related data. Social media and news sites include, but are not limited to, for example, names of specific social media and lists of news sites. For example, the collection unit can analyze posts on social media and collect information on abnormal weather. The collection unit can also analyze articles on news sites and extract important weather information. Furthermore, the collection unit can expand the collection range of weather data based on information from social media and news sites. This makes it possible to collect related data by analyzing information from social media and news sites. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input information from social media and news sites to the generation AI and cause the generation AI to collect related data.

[0082] The evaluation unit can estimate the user's emotion and adjust the reliability evaluation criteria of the data based on the estimated user's emotion. The user's emotion includes, for example, anxiety, relaxation, hurry, etc., but is not limited to such examples. For example, the evaluation unit applies strict reliability evaluation criteria when the user feels anxious. In addition, the evaluation unit can also apply normal reliability evaluation criteria when the user is relaxed. Furthermore, the evaluation unit can also apply criteria that allow for quick evaluation when the user is in a hurry. This allows for more appropriate evaluation by adjusting the reliability evaluation criteria according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the reliability evaluation criteria based on the emotion.

[0083] The evaluation unit can optimize the reliability evaluation algorithm by referring to past evaluation data at the time of evaluation. The past evaluation data includes, for example, past evaluation results, evaluation history, databases, etc., but is not limited to such examples. For example, the evaluation unit adjusts parameters of the reliability evaluation algorithm based on the past evaluation data. The evaluation unit can also analyze the past evaluation data and improve the accuracy of the evaluation algorithm. Furthermore, the evaluation unit can optimize the evaluation criteria by referring to the past evaluation data. As a result, the reliability evaluation algorithm can be optimized by referring to the past evaluation data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation data to the generation AI and cause the generation AI to execute optimization of the reliability evaluation algorithm.

[0084] The evaluation unit can perform the evaluation while taking into consideration the past reliability history of the data provider. The past reliability history of the data provider includes, for example, the evaluation history, reliability score, and past data provision performance of the data provider, but is not limited to such examples. For example, the evaluation unit evaluates the reliability of the current data based on the past reliability history of the data provider. The evaluation unit can also adjust the evaluation criteria by referring to the reliability history of the provider. Furthermore, the evaluation unit can also weight the data by taking into consideration the past reliability history. This enables more accurate evaluation by taking into consideration the past reliability history of the data provider. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the past reliability history of the data provider to the generation AI and cause the generation AI to execute the reliability evaluation.

[0085] The evaluation unit can estimate the user's emotion and adjust the order of displaying the reliability evaluation results based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, anxiety, relaxation, and haste. For example, when the user feels anxious, the evaluation unit preferentially displays data with high reliability. Furthermore, when the user is relaxed, the evaluation unit can also display the evaluation results in a normal order. Furthermore, when the user is in a hurry, the evaluation unit can also quickly display the evaluation results. This makes it possible to provide more appropriate information by adjusting the display order of the evaluation results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the evaluation results based on the emotion.

[0086] The evaluation unit can evaluate the reliability by taking into account the geographical distribution of the data during the evaluation. The geographical distribution includes, for example, the distribution range of the data, the amount of data per region, and geographical bias, but is not limited to such examples. For example, the evaluation unit evaluates the reliability of a specific region based on the geographical distribution of the data. The evaluation unit can also adjust the evaluation criteria by taking into account the geographical distribution. Furthermore, the evaluation unit can weight the data based on the geographical distribution. This enables a more accurate reliability evaluation by taking into account the geographical distribution of the data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the geographical distribution of the data to the generation AI and cause the generation AI to execute the reliability evaluation.

[0087] The evaluation unit can evaluate the reliability by referring to related scientific papers and research data during the evaluation. The scientific papers and research data include, for example, a list of specific scientific papers, names of research databases, and the like, but are not limited to such examples. For example, the evaluation unit evaluates the reliability of the data by referring to related scientific papers. The evaluation unit can also adjust the evaluation criteria based on the research data. Furthermore, the evaluation unit can also weight the data by referring to the scientific papers and research data. As a result, the accuracy of the reliability evaluation is improved by referring to the related scientific papers and research data. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using AI, or may be performed without using AI. For example, the evaluation unit can input the scientific papers and research data to the generation AI and cause the generation AI to execute the reliability evaluation.

[0088] The calculation unit can estimate the user's emotion and adjust the calculation method of the weather forecast based on the estimated user's emotion. The user's emotion includes, for example, anxiety, relaxation, hurry, etc., but is not limited to such examples. For example, the calculation unit provides a detailed weather forecast when the user feels anxious. Furthermore, the calculation unit can also provide a normal weather forecast when the user is relaxed. Furthermore, the calculation unit can apply a method that can perform calculation quickly when the user is in a hurry. In this way, by adjusting the calculation method according to the user's emotion, a more appropriate weather forecast can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the calculation method of the weather forecast based on the emotion.

[0089] The calculation unit can optimize the forecast algorithm by referring to past weather forecast data during calculation. The past weather forecast data includes, for example, past forecast results, forecast history, databases, etc., but is not limited to such examples. For example, the calculation unit adjusts parameters of the forecast algorithm based on the past weather forecast data. The calculation unit can also analyze the past weather forecast data to improve the accuracy of the forecast algorithm. Furthermore, the calculation unit can also optimize the forecast criteria by referring to the past weather forecast data. As a result, the accuracy of the forecast algorithm is improved by referring to the past weather forecast data. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the past weather forecast data to the generation AI and cause the generation AI to execute optimization of the forecast algorithm.

[0090] The calculation unit can improve the accuracy of the forecast based on specific weather conditions or abnormal weather at the time of calculation. The specific weather conditions or abnormal weather include, but are not limited to, typhoons, heavy rain, and extreme heat. For example, the calculation unit adjusts the forecast algorithm based on specific weather conditions (e.g., typhoons and heavy rain). In addition, when abnormal weather occurs, the calculation unit can improve the accuracy of the forecast based on the data. Furthermore, the calculation unit can weight the forecast based on the specific weather conditions or abnormal weather. This makes it possible to improve the accuracy of the forecast based on the specific weather conditions or abnormal weather. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI or may be performed without using AI. For example, the calculation unit can cause the generation AI to execute the improvement of the accuracy of the forecast based on the specific weather conditions or abnormal weather.

[0091] The calculation unit can estimate the user's emotion and adjust the display method of the weather forecast based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, anxiety, relaxation, and hurry. For example, when the user feels anxious, the calculation unit provides a display method including detailed information. Furthermore, when the user is relaxed, the calculation unit can also provide a normal display method. Furthermore, when the user is in a hurry, the calculation unit can also provide a display method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method based on the emotion.

[0092] The calculation unit can calculate the weather forecast for each region taking into account the geographical location information during the calculation. The geographical location information includes, but is not limited to, for example, GPS data, address information, map data, etc. For example, the calculation unit calculates a weather forecast for a specific region based on the geographical location information. The calculation unit can also adjust a forecast algorithm for each region taking into account the geographical location information. Furthermore, the calculation unit can weight the forecast for each region based on the geographical location information. In this way, the weather forecast for each region can be calculated with high accuracy by taking into account the geographical location information. A part or all of the above-mentioned processing in the calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the calculation unit can input the geographical location information to the generation AI and cause the generation AI to calculate the weather forecast for each region.

[0093] The calculation unit can improve the accuracy of the forecast by referring to the related weather model and simulation data at the time of calculation. The weather model and the simulation data include, for example, the name of a specific weather model, a list of simulation data, and the like, but are not limited to such examples. For example, the calculation unit improves the accuracy of the forecast algorithm by referring to the related weather model. The calculation unit can also adjust the parameters of the forecast algorithm based on the simulation data. Furthermore, the calculation unit can also weight the forecast by referring to the weather model and the simulation data. As a result, the accuracy of the forecast is improved by referring to the related weather model and the simulation data. A part or all of the above-mentioned processing in the calculation unit may be performed, for example, using AI, or may be performed without using AI. For example, the calculation unit can input the weather model and the simulation data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the forecast.

[0094] The providing unit can estimate the user's emotion and adjust the weather forecast providing method based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, anxiety, relaxation, and hurry. For example, the providing unit provides a detailed weather forecast when the user is feeling anxious. Also, the providing unit can provide a normal weather forecast when the user is relaxed. Furthermore, the providing unit can apply a method that can provide information quickly when the user is in a hurry. This enables more appropriate information provision by adjusting the providing method according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the providing method based on the emotion.

[0095] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. The past usage history includes, for example, past access history, usage patterns, databases, etc., but is not limited to such examples. For example, the providing unit selects the optimal providing method based on the user's past usage history. The providing unit can also customize the providing method by referring to the past usage history. Furthermore, the providing unit can analyze the user's past usage history and select the most efficient providing method. As a result, the optimal providing method can be selected by referring to the user's past usage history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past usage history to the generating AI and cause the generating AI to select the optimal providing method.

[0096] The providing unit can customize the provided content based on specific weather conditions or abnormal weather at the time of providing. The specific weather conditions or abnormal weather include, but are not limited to, typhoons, heavy rain, and extreme heat. For example, when abnormal weather occurs, the providing unit provides the information on the occurrence of the abnormal weather with priority. The providing unit can also provide a detailed forecast based on specific weather conditions (e.g., typhoons and heavy rain). Furthermore, when there is a sign of abnormal weather, the providing unit can also provide the information early. This enables more appropriate information provision by customizing the provided content based on specific weather conditions or abnormal weather. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI or may be performed without using AI. For example, the providing unit can cause the generating AI to execute customization of the provided content based on specific weather conditions or abnormal weather.

[0097] The providing unit can estimate the user's emotion and adjust the frequency of providing the weather forecast based on the estimated user's emotion. The user's emotion includes, for example, anxiety, relaxation, hurry, etc., but is not limited to such examples. For example, the providing unit frequently provides the weather forecast when the user feels anxious. Also, the providing unit can provide the weather forecast at a normal providing frequency when the user is relaxed. Furthermore, the providing unit can apply a frequency that can provide the weather forecast quickly when the user is in a hurry. This enables more appropriate information provision by adjusting the providing frequency according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the providing frequency based on the emotion.

[0098] The providing unit can select the optimal providing method in consideration of the device information of the user at the time of providing. The device information includes, for example, the type of device, the version of the OS, the type of browser, and the like, but is not limited to such examples. For example, when the user is using a smartphone, the providing unit provides a providing method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, when the user is using a smartwatch, the providing unit can also provide a simple and highly visible providing method. This makes it possible to select the optimal providing method by considering the device information of the user. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the device information of the user to the generating AI and cause the generating AI to select the optimal providing method.

[0099] The providing unit can provide the weather forecast through social media or a news site at the time of providing. The social media or news site includes, for example, the name of a specific social media, a list of news sites, and the like, but is not limited to such examples. For example, the providing unit provides the latest weather forecast through social media. The providing unit can also post the weather forecast on a news site to provide information widely. Furthermore, the providing unit can also quickly provide information on abnormal weather through social media or a news site. As a result, by providing the weather forecast through social media or a news site, information can be provided widely. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the contents of the weather forecast to be provided through social media or a news site to the generating AI, and cause the generating AI to execute the providing method.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] When collecting weather data, the collection unit can analyze the user's past behavioral patterns and determine the optimal collection timing. For example, if the user has frequently checked weather information during a specific time period in the past, the collection unit collects data according to that time period. In addition, if the user has a tendency to check weather information before a specific event (e.g., commuting, traveling), the collection unit can also collect data according to the event. Furthermore, the collection unit can adjust the collection timing according to a specific day of the week or season based on the user's past behavioral patterns. This allows data to be collected at a more appropriate timing by optimizing the collection timing based on the user's behavioral patterns.

[0102] The evaluation unit can take into account the past performance of the data provider when evaluating the reliability of the data. For example, the evaluation unit can highly evaluate the reliability of a data provider that has provided accurate forecasts in the past. The evaluation unit can also low the reliability of a data provider that has provided erroneous forecasts in the past. Furthermore, the evaluation unit can dynamically adjust the reliability evaluation criteria based on the past performance of the data provider. This allows for a more accurate reliability evaluation by taking into account the past performance of the data provider.

[0103] When calculating the weather forecast, the calculation unit can customize the forecast by taking into account the user's current location information. For example, the calculation unit calculates the forecast by preferentially using weather data for the area where the user is currently located. In addition, when the user is moving, the calculation unit can also provide a forecast by taking into account weather data for the destination. Furthermore, when the user frequently visits a particular area, the calculation unit can also customize the forecast based on weather data for that area. In this way, by customizing the forecast based on the user's location information, a more relevant weather forecast can be provided.

[0104] The providing unit can adjust the providing method in consideration of the remaining battery level of the user's device when providing a weather forecast. For example, the providing unit provides a simple and power-saving display method when the battery of the user's device is low. The providing unit can also provide a display method including detailed information when the battery is sufficient. Furthermore, the providing unit can adjust the frequency and content of notifications according to the remaining battery level. This makes it possible to provide more appropriate information by optimizing the providing method according to the battery status of the user's device.

[0105] The collection unit can estimate the user's emotions when collecting weather data, and adjust the type of data to be collected based on the estimated emotions. For example, if the user feels anxious, the collection unit can prioritize collecting detailed weather data (e.g., wind speed, precipitation). Also, if the user feels relaxed, the collection unit can collect basic weather data (e.g., temperature, weather). Furthermore, if the user is in a hurry, the collection unit can prioritize data that can be collected quickly. In this way, more appropriate information can be provided by adjusting the type of data to be collected according to the user's emotions.

[0106] The evaluation unit can estimate the user's emotions when evaluating the reliability of data, and adjust the evaluation criteria based on the estimated emotions. For example, the evaluation unit can apply strict evaluation criteria when the user feels anxious. The evaluation unit can also apply normal evaluation criteria when the user feels relaxed. Furthermore, the evaluation unit can also apply criteria that allow for quick evaluation when the user is in a hurry. This allows for more appropriate reliability evaluation by adjusting the evaluation criteria according to the user's emotions.

[0107] The calculation unit can estimate the user's emotions when calculating the weather forecast, and adjust the level of detail of the forecast based on the estimated emotions. For example, the calculation unit can provide a detailed forecast when the user is feeling anxious. The calculation unit can also provide a basic forecast when the user is relaxed. Furthermore, the calculation unit can provide a concise forecast that focuses on the main points when the user is in a hurry. This makes it possible to provide more appropriate information by adjusting the level of detail of the forecast according to the user's emotions.

[0108] The providing unit can estimate the user's emotions when providing a weather forecast, and adjust the frequency of provision based on the estimated emotions. For example, the providing unit can provide the weather forecast frequently when the user feels anxious. Also, the providing unit can provide the weather forecast at a normal frequency when the user is relaxed. Furthermore, the providing unit can apply a frequency that allows for quick provision when the user is in a hurry. This makes it possible to provide more appropriate information by adjusting the frequency of provision according to the user's emotions.

[0109] The providing unit can estimate the user's emotions when providing a weather forecast, and adjust the providing method based on the estimated emotions. For example, if the user feels anxious, the providing unit can provide a providing method including detailed information. If the user feels relaxed, the providing unit can also provide a normal providing method. Furthermore, if the user is in a hurry, the providing unit can also provide a providing method that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the providing method according to the user's emotions.

[0110] When collecting weather data, the collection unit can expand the collection range based on specific weather conditions or abnormal weather. For example, if a typhoon is approaching, the collection unit will prioritize collecting data within its affected range. In addition, if heavy rain is expected, the collection unit can also collect detailed precipitation data for that area. Furthermore, if a heat wave is expected, the collection unit can also focus on collecting temperature data for that area. In this way, expanding the collection range based on specific weather conditions or abnormal weather enables more accurate weather forecasts.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects multiple weather data. The multiple weather data include, for example, temperature data, precipitation data, wind speed data, etc. The collection unit collects weather data from meteorological agencies, various weather information companies, and various weather forecasters around the world. This collected data can include AI processing. Step 2: The evaluation unit evaluates the reliability of each of the collected weather data. Reliability includes the reliability of the data source, the consistency of the data, and the accuracy of the data. The evaluation unit converts the collected data into a unified format and evaluates the reliability based on the converted data. This evaluation includes AI processing. Step 3: The calculation unit calculates the most probable weather forecast based on the evaluated reliability. The calculation unit weights the evaluated data and calculates a weighted average. Furthermore, the calculation unit calculates the most probable weather forecast based on this weighted average. This calculation process also includes AI processing. Step 4: The providing unit provides the calculated weather forecast to the user. The providing unit provides the weather forecast through a website, an application, email, or SMS. This provision may also include AI processing.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] For example, the collection unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects weather data using the camera 42 and microphone 38B of the smart device 14, and converts the data into a unified format by the control unit 46A. The evaluation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the collected data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and weights the evaluated data and calculates a weighted average. The provision unit is realized by, for example, the control unit 46A of the smart device 14, and provides the calculated weather forecast to the user through a website or application. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

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

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] For example, the collection unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects weather data using the camera 42 and microphone 238 of the smart glasses 214, and converts the data into a unified format by the control unit 46A. The evaluation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the collected data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and weights the evaluated data and calculates a weighted average. The provision unit is realized by, for example, the control unit 46A of the smart glasses 214, and provides the calculated weather forecast to the user through a website or application. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the collection unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects weather data using the camera 42 and microphone 238 of the headset type terminal 314, and converts the data into a unified format by the control unit 46A. The evaluation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the collected data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and weights the evaluated data and calculates a weighted average. The provision unit is realized by, for example, the control unit 46A of the headset type terminal 314, and provides the calculated weather forecast to the user through a website or application. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] For example, the collection unit is realized by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects weather data using the camera 42 and microphone 238 of the robot 414, and converts the data into a unified format by the control unit 46A. The evaluation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and evaluates the reliability of the collected data. The calculation unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and weights the evaluated data and calculates a weighted average. The provision unit is realized by, for example, the control unit 46A of the robot 414, and provides the calculated weather forecast to the user through a website or application. The correspondence between each unit and the device or control unit is not limited to the above example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The above specific processes are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit that collects a plurality of meteorological data; an evaluation unit that evaluates the reliability of each of the plurality of meteorological data collected by the collection unit; A calculation unit that calculates a highly probable weather forecast based on the reliability evaluated by the evaluation unit; a providing unit that provides a user with the weather forecast calculated by the calculation unit. A system characterized by: (Appendix 2) The evaluation unit is A conversion unit that converts the collected data into a unified format; A reliability evaluation unit is provided to evaluate reliability based on the converted data. 2. The system of claim 1, (Appendix 3) The calculation unit is a first calculation processing unit that weights the evaluated data and calculates a weighted average; A second calculation processing unit is provided to calculate a highly probable weather forecast based on a weighted average. 2. The system of claim 1, (Appendix 4) The evaluation unit is Evaluating reliability by region 2. The system of claim 1, (Appendix 5) The calculation unit is Calculate weather forecasts for each region 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collecting weather data from the Meteorological Agency, weather information companies, and weather forecasters 2. The system of claim 1, (Appendix 7) The providing unit is Providing weather forecasts via website, app, email and SMS 2. The system of claim 1, (Appendix 8) The collecting unit includes: Inferring user emotions and adjusting the timing of weather data collection based on the user emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze past weather data collection history and select the optimal collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: As weather data is collected, it can be filtered based on specific weather conditions or extreme weather events. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Estimate user emotions and prioritize weather data to be collected based on the user emotions. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting weather data, take geographic location information into account to prioritize collection of relevant data 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting weather data, information from social media and news sites is analyzed to collect relevant data. 2. The system of claim 1, (Appendix 14) The evaluation unit is Infer user sentiment and adjust data reliability criteria based on user sentiment 2. The system of claim 1, (Appendix 15) The evaluation unit is During evaluation, the reliability evaluation algorithm is optimized by referring to past evaluation data. 2. The system of claim 1, (Appendix 16) The evaluation unit is During the evaluation, the past reliability history of the data provider is taken into account. 2. The system of claim 1, (Appendix 17) The evaluation unit is Infer user sentiment and adjust the order in which trust evaluation results are displayed based on the user sentiment 2. The system of claim 1, (Appendix 18) The evaluation unit is During the evaluation, the geographical distribution of data is taken into account to assess its reliability. 2. The system of claim 1, (Appendix 19) The evaluation unit is During the evaluation, we refer to relevant scientific papers and research data to assess reliability. 2. The system of claim 1, (Appendix 20) The calculation unit is Infer user emotions and adjust the way weather forecasts are calculated based on the user emotions 2. The system of claim 1, (Appendix 21) The calculation unit is When calculating, the forecast algorithm is optimized by referencing past weather forecast data. 2. The system of claim 1, (Appendix 22) The calculation unit is During calculation, we improve the accuracy of our forecasts based on specific weather conditions and extreme weather events. 2. The system of claim 1, (Appendix 23) The calculation unit is Infer user emotions and adjust how weather forecasts are displayed based on the user's emotions 2. The system of claim 1, (Appendix 24) The calculation unit is During the calculation, geographic location information is taken into account to calculate weather forecasts for each region. 2. The system of claim 1, (Appendix 25) The calculation unit is When calculating, the accuracy of forecasts is improved by referring to relevant weather models and simulation data. 2. The system of claim 1, (Appendix 26) The providing unit is Infer user emotions and adjust how weather forecasts are delivered based on the user's emotions 2. The system of claim 1, (Appendix 27) The providing unit is When providing the service, the optimal method of providing the service is selected by referring to the user's past usage history. 2. The system of claim 1, (Appendix 28) The providing unit is Customize offers based on specific weather conditions or extreme weather events at the time of offer 2. The system of claim 1, (Appendix 29) The providing unit is Infer user emotions and adjust the frequency of weather forecasts based on the user emotions 2. The system of claim 1, (Appendix 30) The providing unit is When providing information, the optimal method of providing the information is selected taking into account the user's device information. 2. The system of claim 1, (Appendix 31) The providing unit is Provide weather forecasts via social media and news sites as they become available 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects a plurality of meteorological data; an evaluation unit that evaluates the reliability of each of the plurality of meteorological data collected by the collection unit; A calculation unit that calculates a highly probable weather forecast based on the reliability evaluated by the evaluation unit; a providing unit that provides a user with the weather forecast calculated by the calculation unit. A system characterized by:

2. The evaluation unit is A conversion unit that converts the collected data into a unified format; A reliability evaluation unit is provided to evaluate reliability based on the converted data.

2. The system of claim 1 .

3. The calculation unit is a first calculation processing unit that weights the evaluated data and calculates a weighted average; A second calculation processing unit is provided to calculate a highly probable weather forecast based on a weighted average.

2. The system of claim 1 .

4. The evaluation unit is Evaluating reliability by region 2. The system of claim 1 .

5. The calculation unit is Calculate weather forecasts for each region 2. The system of claim 1 .

6. The collecting unit includes: Collecting weather data from meteorological agencies, weather information companies, and weather forecasters 2. The system of claim 1 .

7. The providing unit is Providing weather forecasts via website, app, email and SMS 2. The system of claim 1 .

8. The collecting unit includes: Inferring user emotions and adjusting the timing of weather data collection based on the user emotions 2. The system of claim 1 .

9. The collecting unit includes: Analyze past weather data collection history and select the optimal collection method 2. The system of claim 1 .

10. The collecting unit includes: As weather data is collected, it can be filtered based on specific weather conditions or extreme weather events.

2. The system of claim 1 .

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