System

Abstract

An object of the system according to the embodiment is to efficiently integrate a wide variety of data of a local government and convert the data into a machine-readable format.SOLUTION: A system includes a data collection part, a data analysis part, a data conversion part, a data integration part, and a data standardization part. The data collection unit collects data of a plurality of formats. The data analysis unit analyzes the data collected by the data collection unit. The data conversion unit converts the data analyzed by the data analysis unit into a machine-readable format. The data integration unit integrates the data converted by the data conversion unit. The data standardization unit stores the data integrated by the data integration unit in a unified format.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of efficiently integrating the wide variety of data held by local governments and converting it into a machine-readable format.

[0005] The system according to the embodiment aims to efficiently integrate a wide variety of data from local governments and convert it into a machine-readable format. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, a data conversion unit, a data integration unit, and a data standardization unit. The data collection unit collects data in multiple formats. The data analysis unit analyzes the data collected by the data collection unit. The data conversion unit converts the data analyzed by the data analysis unit into a machine-readable format. The data integration unit consolidates the data converted by the data conversion unit. The data standardization unit saves the data consolidated by the data integration unit in a unified format. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently integrate a wide variety of data from a local government and convert it into a machine-readable format. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The data conversion system according to an embodiment of the present invention uses a generation AI to analyze data in a wide variety of formats held by local governments, converting it into machine-readable data, and accelerating the shift to open data in local governments nationwide. As a result, the data conversion system can efficiently convert local government data into machine-readable data, accelerating the shift to open data in local governments nationwide.

[0029] The data conversion system according to the embodiment includes a data collection unit, a data analysis unit, a data conversion unit, a data integration unit, and a data standardization unit. The data collection unit collects data in multiple formats. For example, it scans and digitizes paper data. It can also convert PDF and JPG format data into text data using image analysis technology. It can also import Excel and CSV format data directly and format the data as needed. The data analysis unit analyzes the data collected by the data collection unit. For example, a generation AI classifies and formats the data based on instructions regarding the data format and content. The data analysis unit can also check the consistency and integrity of the data and eliminate unreliable data. The data conversion unit converts the data analyzed by the data analysis unit into a machine-readable format. For example, it can convert scanned paper data, PDF, and JPG format data into text data using OCR technology. It can also convert Excel and CSV format data into a unified format. The data integration unit integrates the data converted by the data conversion unit. For example, it can convert data of the same category provided by different local governments into a unified format and centrally manage it. The data standardization unit stores the data integrated by the data integration unit in a unified format. For example, it adds meta information to the data and creates a data catalog. As a result, the data conversion system according to the embodiment can efficiently convert data in a wide variety of formats into machine-readable data, accelerating the shift to open data in local governments across the country. For example, it will make it easier for local governments to share data, accelerating the use of data that is useful for solving local issues and formulating policies. Furthermore, data standardization will enable data analysis and comparison on a national scale, which is expected to lead to more effective policy implementation.

[0030] The data collection unit can evaluate the reliability of data as it collects it and automatically filter out unreliable data. For example, when the generation AI collects data, it analyzes the source and provider of the data and prioritizes the collection of reliable data. For example, it prioritizes data from official local government websites. To evaluate the reliability of the data, the generation AI checks the consistency and integrity of the data and automatically eliminates unreliable data. For example, it filters out contradictory or incomplete data. Furthermore, when the generation AI collects data, it evaluates the frequency and recency of the data and automatically filters out old or infrequently updated data. For example, it prioritizes the collection of the most recent data. This improves data quality by eliminating unreliable data.

[0031] The data collection unit can detect duplicate data when collecting data and automatically eliminate duplicate data. For example, when the generation AI collects data, it analyzes the content of the data and automatically detects duplicate data. For example, it eliminates duplicates when data with the same content is provided in multiple formats. To detect duplicate data, the generation AI also analyzes the data's meta information (e.g., creation date and time, source, etc.) and automatically eliminates duplicate data. For example, it combines data from the same source into one. Furthermore, when the generation AI collects data, it compares the content of the data and automatically integrates duplicate data. For example, it combines the same data provided in different formats into one. This eliminates duplicate data, enabling efficient data management.

[0032] The data collection unit can also collect audio data or video data and convert this data into text data. In the data collection unit, for example, the generation AI collects audio data and converts it into text data using voice recognition technology. For example, recordings of meetings are converted into text. The generation AI also collects video data and converts it into text data using video analysis technology. For example, the content of a presentation video is converted into text. A system is also built in which audio data and video data are collected and the generation AI converts them into text data. For example, the content of the audio and video is automatically analyzed and saved as text data. This ensures data diversity by converting audio data and video data into text data as well.

[0033] The data analysis unit can understand the context of the data when analyzing it and perform data conversion based on the context. In the data analysis unit, for example, the generation AI analyzes the context of the data and performs data conversion based on the context. For example, it understands the context of the sentence and converts it into appropriate text data. Furthermore, to understand the context of the data, the generation AI refers to related information and performs data conversion based on the context. For example, it maintains data consistency by referring to related databases. Furthermore, a system is constructed in which the generation AI analyzes the context of the data and performs data conversion based on the context. For example, it understands the meaning of the data and converts it into an appropriate format. In this way, by understanding the context of the data, more accurate data conversion becomes possible.

[0034] The data analysis unit can detect outliers in the data during data analysis and automatically correct them. For example, the generation AI detects outliers in the data during data analysis and automatically corrects them. For example, it uses statistical methods to identify outliers and replace them with appropriate values. In addition, to detect outliers in the data, the generation AI refers to past data and automatically corrects them. For example, it identifies outliers by comparing with past data. In addition, a system is built in which the generation AI detects outliers in the data during data analysis and automatically corrects them. For example, it detects and corrects outliers in real time. This makes it possible to improve the accuracy of the data by automatically correcting outliers.

[0035] The data integration unit can evaluate the consistency of data when integrating data and automatically correct inconsistent data. For example, the data integration unit evaluates the consistency of data when the generation AI integrates data and automatically corrects inconsistent data. For example, it converts data in different formats into a unified format. In addition, to evaluate the consistency of data, the generation AI analyzes the content of the data and automatically corrects inconsistent data. For example, it unifies data in different unit systems. In addition, a system is constructed in which the generation AI evaluates the consistency of data when integrating data and automatically corrects inconsistent data. For example, rules are set to maintain data consistency. This makes it possible to maintain data consistency by correcting inconsistent data.

[0036] The data standardization unit automatically generates meta information for data when standardizing data, thereby improving the searchability of the data. For example, the generation AI automatically generates meta information for data when integrating data, improving the searchability of the data. For example, it adds meta information such as the date and time of data creation and the source of the data. In addition, to generate meta information for data, the generation AI analyzes the contents of the data and automatically generates appropriate meta information. For example, it automatically adds data categories and keywords. In addition, the generation AI automatically generates meta information for data when integrating data, building a system that improves the searchability of data. For example, it creates a search index for data based on the meta information. In this way, generating meta information improves the searchability of data.

[0037] The data standardization unit can automatically convert different data formats during data standardization and integrate them into a unified format. In this unit, for example, the generation AI automatically converts different data formats and integrates them into a unified format. For example, data in PDF or JPG format is converted into text data and integrated. In addition, to convert data formats, the generation AI uses a conversion model that supports various data formats and integrates them into a unified format. For example, data in Excel or CSV format is converted into a unified format. In addition, a system is built in which the generation AI automatically converts different data formats and integrates them into a unified format. For example, data format conversion rules are set and automatically applied. This unifies different data formats to maintain data consistency.

[0038] The data standardization unit can detect data redundancy during data standardization and automatically eliminate redundant data. For example, the data standardization unit detects data redundancy and automatically eliminates redundant data when the generation AI is integrating data. For example, if multiple pieces of data with the same content exist, duplicates will be eliminated. In addition, to detect data redundancy, the generation AI analyzes the content of the data and automatically eliminates redundant data. For example, if the same information is provided in different formats, redundant parts will be deleted. In addition, a system is built in which the generation AI detects data redundancy during data integration and automatically eliminates redundant data. For example, an algorithm that detects data duplication will be introduced. This will enable efficient data management by eliminating redundant data.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The data collection unit can collect data from IoT devices in real time. For example, it can collect data from environmental sensors to monitor changes in temperature and humidity in real time. It can also collect data from traffic sensors to understand traffic volume and congestion information in real time. It can also collect data from health monitoring devices to monitor an individual's health condition in real time. This makes it possible to collect data in real time, which is expected to enable rapid response.

[0041] The data analysis unit can use natural language processing technology to perform sentiment analysis of text data. For example, it can analyze social media posts to understand user sentiment. It can also analyze customer feedback to evaluate sentiment toward products and services. It can also analyze news articles to understand trends in social sentiment. This makes it possible to extract sentiment from text data and apply it to a variety of fields.

[0042] The data conversion unit can automatically translate data in different languages ​​and provide the data in a unified language. For example, English data can be translated into Japanese, or French data can be translated into English. Furthermore, it is possible to build a multilingual database and centrally manage data in different languages. This makes it possible to handle data in different languages ​​in a unified manner, facilitating international data sharing.

[0043] The Data Integration Department can use blockchain technology to ensure data reliability and transparency. For example, data change history can be recorded on the blockchain to prevent unauthorized changes. The blockchain can also be used to track the source of data, making the origin of the data clear. Furthermore, the data sharing process can be managed with the blockchain to ensure transparency. This improves data reliability and transparency and enables safe data sharing.

[0044] The Data Standardization Department can provide data visualization tools to make data easier to understand. For example, it can visually display data trends using graphs and charts, or plot data on a map to understand geographical distribution. It can also provide interactive dashboards that allow users to analyze data while manipulating it. This improves data visualization and promotes understanding and utilization of data.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The data collection unit collects data in multiple formats. For example, paper data can be scanned and digitized. PDF and JPG format data can also be converted to text using image analysis technology. Excel and CSV format data can also be imported directly and formatted as needed. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI classifies and formats the data based on instructions regarding the format and content of the data. The data analysis unit can also check the consistency and integrity of the data and eliminate unreliable data. Step 3: The data conversion unit converts the data analyzed by the data analysis unit into a machine-readable format. For example, scanned paper data, PDF, or JPG format data can be converted into text using OCR technology. Excel or CSV format data can also be converted into a unified format. Step 4: The Data Integration Department integrates the data converted by the Data Conversion Department. For example, data of the same category provided by different local governments is converted into a unified format and managed centrally. Step 5: The data standardization unit stores the data integrated by the data integration unit in a unified format, for example by adding meta-information to the data and creating a data catalog.

[0047] (Example 2) The data conversion system according to an embodiment of the present invention uses a generation AI to analyze data in a wide variety of formats held by local governments, converting it into machine-readable data, and accelerating the shift to open data in local governments nationwide. As a result, the data conversion system can efficiently convert local government data into machine-readable data, accelerating the shift to open data in local governments nationwide.

[0048] The data conversion system according to the embodiment includes a data collection unit, a data analysis unit, a data conversion unit, a data integration unit, and a data standardization unit. The data collection unit collects data in multiple formats. For example, it scans and digitizes paper data. It can also convert PDF and JPG format data into text data using image analysis technology. It can also import Excel and CSV format data directly and format the data as needed. The data analysis unit analyzes the data collected by the data collection unit. For example, a generation AI classifies and formats the data based on instructions regarding the data format and content. The data analysis unit can also check the consistency and integrity of the data and eliminate unreliable data. The data conversion unit converts the data analyzed by the data analysis unit into a machine-readable format. For example, it can convert scanned paper data, PDF, and JPG format data into text data using OCR technology. It can also convert Excel and CSV format data into a unified format. The data integration unit integrates the data converted by the data conversion unit. For example, it can convert data of the same category provided by different local governments into a unified format and centrally manage it. The data standardization unit stores the data integrated by the data integration unit in a unified format. For example, it adds meta information to the data and creates a data catalog. As a result, the data conversion system according to the embodiment can efficiently convert data in a wide variety of formats into machine-readable data, accelerating the shift to open data in local governments across the country. For example, it will make it easier for local governments to share data, accelerating the use of data that is useful for solving local issues and formulating policies. Furthermore, data standardization will enable data analysis and comparison on a national scale, which is expected to lead to more effective policy implementation.

[0049] The data collection unit can evaluate the reliability of data as it collects it and automatically filter out unreliable data. For example, when the generation AI collects data, it analyzes the source and provider of the data and prioritizes the collection of reliable data. For example, it prioritizes data from official local government websites. To evaluate the reliability of the data, the generation AI checks the consistency and integrity of the data and automatically eliminates unreliable data. For example, it filters out contradictory or incomplete data. Furthermore, when the generation AI collects data, it evaluates the frequency and recency of the data and automatically filters out old or infrequently updated data. For example, it prioritizes the collection of the most recent data. This improves data quality by eliminating unreliable data.

[0050] The data collection unit can detect duplicate data when collecting data and automatically eliminate duplicate data. For example, when the generation AI collects data, it analyzes the content of the data and automatically detects duplicate data. For example, it eliminates duplicates when data with the same content is provided in multiple formats. To detect duplicate data, the generation AI also analyzes the data's meta information (e.g., creation date and time, source, etc.) and automatically eliminates duplicate data. For example, it combines data from the same source into one. Furthermore, when the generation AI collects data, it compares the content of the data and automatically integrates duplicate data. For example, it combines the same data provided in different formats into one. This eliminates duplicate data, enabling efficient data management.

[0051] The data collection unit can also collect audio data or video data and convert this data into text data. In the data collection unit, for example, the generation AI collects audio data and converts it into text data using voice recognition technology. For example, recordings of meetings are converted into text. The generation AI also collects video data and converts it into text data using video analysis technology. For example, the content of a presentation video is converted into text. A system is also built in which audio data and video data are collected and the generation AI converts them into text data. For example, the content of the audio and video is automatically analyzed and saved as text data. This ensures data diversity by converting audio data and video data into text data as well.

[0052] The data analysis unit can understand the context of the data when analyzing it and perform data conversion based on the context. In the data analysis unit, for example, the generation AI analyzes the context of the data and performs data conversion based on the context. For example, it understands the context of the sentence and converts it into appropriate text data. Furthermore, to understand the context of the data, the generation AI refers to related information and performs data conversion based on the context. For example, it maintains data consistency by referring to related databases. Furthermore, a system is constructed in which the generation AI analyzes the context of the data and performs data conversion based on the context. For example, it understands the meaning of the data and converts it into an appropriate format. In this way, by understanding the context of the data, more accurate data conversion becomes possible.

[0053] The data analysis unit can detect outliers in the data during data analysis and automatically correct them. For example, the generation AI detects outliers in the data during data analysis and automatically corrects them. For example, it uses statistical methods to identify outliers and replace them with appropriate values. In addition, to detect outliers in the data, the generation AI refers to past data and automatically corrects them. For example, it identifies outliers by comparing with past data. In addition, a system is built in which the generation AI detects outliers in the data during data analysis and automatically corrects them. For example, it detects and corrects outliers in real time. This makes it possible to improve the accuracy of the data by automatically correcting outliers.

[0054] The data integration unit can evaluate the consistency of data when integrating data and automatically correct inconsistent data. For example, the data integration unit evaluates the consistency of data when the generation AI integrates data and automatically corrects inconsistent data. For example, it converts data in different formats into a unified format. In addition, to evaluate the consistency of data, the generation AI analyzes the content of the data and automatically corrects inconsistent data. For example, it unifies data in different unit systems. In addition, a system is constructed in which the generation AI evaluates the consistency of data when integrating data and automatically corrects inconsistent data. For example, rules are set to maintain data consistency. This makes it possible to maintain data consistency by correcting inconsistent data.

[0055] The data standardization unit automatically generates meta information for data when standardizing data, thereby improving the searchability of the data. For example, the generation AI automatically generates meta information for data when integrating data, improving the searchability of the data. For example, it adds meta information such as the date and time of data creation and the source of the data. In addition, to generate meta information for data, the generation AI analyzes the contents of the data and automatically generates appropriate meta information. For example, it automatically adds data categories and keywords. In addition, the generation AI automatically generates meta information for data when integrating data, building a system that improves the searchability of data. For example, it creates a search index for data based on the meta information. In this way, generating meta information improves the searchability of data.

[0056] The data standardization unit can automatically convert different data formats during data standardization and integrate them into a unified format. In this unit, for example, the generation AI automatically converts different data formats and integrates them into a unified format. For example, data in PDF or JPG format is converted into text data and integrated. In addition, to convert data formats, the generation AI uses a conversion model that supports various data formats and integrates them into a unified format. For example, data in Excel or CSV format is converted into a unified format. In addition, a system is built in which the generation AI automatically converts different data formats and integrates them into a unified format. For example, data format conversion rules are set and automatically applied. This unifies different data formats to maintain data consistency.

[0057] The data standardization unit can detect data redundancy during data standardization and automatically eliminate redundant data. For example, the data standardization unit detects data redundancy and automatically eliminates redundant data when the generation AI is integrating data. For example, if multiple pieces of data with the same content exist, duplicates will be eliminated. In addition, to detect data redundancy, the generation AI analyzes the content of the data and automatically eliminates redundant data. For example, if the same information is provided in different formats, redundant parts will be deleted. In addition, a system is built in which the generation AI detects data redundancy during data integration and automatically eliminates redundant data. For example, an algorithm that detects data duplication will be introduced. This will enable efficient data management by eliminating redundant data.

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

[0059] The data collection unit can estimate the user's emotions and determine the priority of data collection based on the estimated emotions. For example, if the user is feeling anxious, it will prioritize collecting highly reliable data. If the user is in a hurry, it will prioritize data that can be collected quickly. It can also prioritize collecting data in a specific category in which the user is interested. This enables flexible data collection according to the user's emotions.

[0060] The data analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is confused, the analysis results can be displayed in an easy-to-understand manner. If the user is excited, detailed analysis results can be provided. Furthermore, if the user is calm, a normal analysis method can be used. This makes it possible to perform appropriate data analysis according to the user's emotions.

[0061] The data conversion unit can estimate the user's emotions and adjust the accuracy of data conversion based on the estimated emotions. For example, if the user is cautious, the conversion accuracy can be increased. If the user is in a hurry, the conversion speed can be prioritized. Furthermore, if the user is relaxed, a balanced conversion can be performed. This enables flexible data conversion according to the user's emotions.

[0062] The data integration unit can estimate the user's emotions and adjust the data integration method based on the estimated emotions. For example, if the user is feeling anxious, the integration results can be explained in detail. If the user is in a hurry, the integration results can be provided quickly. Furthermore, it can prioritize the integration of data in specific categories in which the user is interested. This enables flexible data integration according to the user's emotions.

[0063] The data standardization unit can estimate the user's emotions and adjust the data standardization method based on the estimated emotions. For example, if the user is cautious, the accuracy of standardization can be increased. If the user is in a hurry, the speed of standardization can be prioritized. Furthermore, if the user is relaxed, balanced standardization can be performed. This enables flexible data standardization according to the user's emotions.

[0064] The data collection unit can collect data from IoT devices in real time. For example, it can collect data from environmental sensors to monitor changes in temperature and humidity in real time. It can also collect data from traffic sensors to understand traffic volume and congestion information in real time. It can also collect data from health monitoring devices to monitor an individual's health condition in real time. This makes it possible to collect data in real time, which is expected to enable rapid response.

[0065] The data analysis unit can use natural language processing technology to perform sentiment analysis of text data. For example, it can analyze social media posts to understand user sentiment. It can also analyze customer feedback to evaluate sentiment toward products and services. It can also analyze news articles to understand trends in social sentiment. This makes it possible to extract sentiment from text data and apply it to a variety of fields.

[0066] The data conversion unit can automatically translate data in different languages ​​and provide the data in a unified language. For example, English data can be translated into Japanese, or French data can be translated into English. Furthermore, it is possible to build a multilingual database and centrally manage data in different languages. This makes it possible to handle data in different languages ​​in a unified manner, facilitating international data sharing.

[0067] The Data Integration Department can use blockchain technology to ensure data reliability and transparency. For example, data change history can be recorded on the blockchain to prevent unauthorized changes. The blockchain can also be used to track the source of data, making the origin of the data clear. Furthermore, the data sharing process can be managed with the blockchain to ensure transparency. This improves data reliability and transparency and enables safe data sharing.

[0068] The Data Standardization Department can provide data visualization tools to make data easier to understand. For example, it can visually display data trends using graphs and charts, or plot data on a map to understand geographical distribution. It can also provide interactive dashboards that allow users to analyze data while manipulating it. This improves data visualization and promotes understanding and utilization of data.

[0069] The processing flow of the second embodiment will be briefly explained below.

[0070] Step 1: The data collection unit collects data in multiple formats. For example, paper data can be scanned and digitized. PDF and JPG format data can also be converted to text using image analysis technology. Excel and CSV format data can also be imported directly and formatted as needed. Step 2: The data analysis unit analyzes the data collected by the data collection unit. For example, the generation AI classifies and formats the data based on instructions regarding the format and content of the data. The data analysis unit can also check the consistency and integrity of the data and eliminate unreliable data. Step 3: The data conversion unit converts the data analyzed by the data analysis unit into a machine-readable format. For example, scanned paper data, PDF, or JPG format data can be converted into text using OCR technology. Excel or CSV format data can also be converted into a unified format. Step 4: The Data Integration Department integrates the data converted by the Data Conversion Department. For example, data of the same category provided by different local governments is converted into a unified format and managed centrally. Step 5: The data standardization unit stores the data integrated by the data integration unit in a unified format, for example by adding meta-information to the data and creating a data catalog.

[0071] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0072] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0073] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0074] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0075] 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.

[0076] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0077] 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.

[0078] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0079] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0081] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0082] 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.

[0083] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0084] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0085] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0086] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0087] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0089] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0090] 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.

[0091] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0092] 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.

[0093] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0094] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0095] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0096] 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.

[0097] 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.

[0098] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0099] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0100] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0101] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0103] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0104] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0105] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0106] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0107] 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.

[0108] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0110] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0111] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0112] 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.

[0113] 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.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0121] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0122] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0123] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0124] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0125] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0126] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0127] 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.

[0128] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0129] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0130] 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.

[0131] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0132] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0133] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0134] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0135] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0136] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0137] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0138] 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 data collection unit that collects data in multiple formats; a data analysis unit that analyzes the data collected by the data collection unit; a data conversion unit that converts the data analyzed by the data analysis unit into a machine-readable format; a data integration unit that integrates the data converted by the data conversion unit; a data standardization unit that stores the data integrated by the data integration unit in a unified format. A system characterized by:

2. The data collection unit Evaluate data reliability during data collection and automatically filter out unreliable data 2. The system of claim 1.

3. The data collection unit Detects and automatically eliminates duplicate data during data collection 2. The system of claim 1.

4. The data collection unit When collecting data, audio or video data is also collected and converted into text data.

2. The system of claim 1.

5. The data analysis unit Understand the context of data during data analysis and transform data based on that context 2. The system of claim 1.

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