System

Abstract

To provide a system that unifies data formats and facilitates data sharing across fields.SOLUTION: A system according to the embodiment comprises a collection unit, an analysis unit, a linking unit, and a monitoring unit. The collection unit collects data from each field. The analysis unit analyzes the data collected by the collection unit, and standardizes vocabulary. The linking unit performs data linkage based on the data unified by the analysis unit. The monitoring unit uses a dashboard to grasp details of the data linkage performed by the linking 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] With conventional technology, there was a lack of standardization of data formats, making it difficult to share data across fields.

[0005] The system according to the embodiment aims to unify data formats and facilitate data linkage across fields. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a linking unit, and a monitoring unit. The collection unit collects data from each field. The analysis unit analyzes the data collected by the collection unit and standardizes vocabulary. The linking unit performs data linkage based on the data standardized by the analysis unit. The monitoring unit keeps track of details of the data linkage performed by the linking unit on a dashboard. Effect of the Invention

[0007] The system according to the embodiment can unify data formats and facilitate data linkage across fields. [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 the 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) The data linkage system according to the embodiment of the present invention is a system that uses a generation AI to unify vocabulary and facilitate data linkage. This data linkage system collects data from each field and inputs it into the generation AI. The generation AI analyzes the collected data and unifies the vocabulary used in each field. For example, if city OS data and DX data are recorded in different formats, the generation AI analyzes the data and converts them into a common vocabulary. Next, data linkage is performed based on the data unified by the generation AI. This allows data from different fields to be easily linked and mutually usable. For example, city OS data and DX data are linked in a unified format, which makes the operation and management of cities more efficient. Furthermore, by understanding the exchange of data linkage on a dashboard, any analysis is possible. For example, the flow of data can be visualized on the dashboard, and it is possible to understand at a glance which data is being used in which field and how. This allows the data usage status to be monitored in real time and adjustments to be made as necessary. This mechanism promotes the unification of data formats, making it easier to link across fields such as DX and city OS. By using the generation AI, it is possible to achieve efficient and effective data linkage beyond the limits of human unification. This enables the data linkage system to efficiently collect, analyze, link, and monitor data.

[0029] The data linkage system according to the embodiment includes a collection unit, an analysis unit, a linkage unit, and a monitoring unit. The collection unit collects data in each field. The data in each field includes, for example, medical data, traffic data, and environmental data, but is not limited to such examples. The collection unit collects, for example, city OS data and DX data. The city OS data includes a smart city platform and a city management system. The DX data includes digitalization of business processes and data-driven decision-making. The analysis unit uses a generation AI to analyze the collected data and unify the vocabulary. The unification of vocabulary includes, for example, the use of a standardized dictionary and unification of synonyms, but is not limited to such examples. For example, the generation AI uses a text generation AI (for example, LLM) to unify the vocabulary of the collected data. The analysis unit can also convert the collected data into a common vocabulary using the generation AI. For example, the generation AI analyzes the city OS data and the DX data and converts them into a common vocabulary. The linkage unit performs data linkage based on the unified data. Data linkage includes, for example, the use of APIs and the unification of data formats, but is not limited to such examples. For example, the linking unit links data from different fields based on unified data. The monitoring unit visualizes the data flow on a dashboard and performs anomaly detection and performance monitoring. The dashboard includes, for example, the display of real-time data and anomaly detection alerts, but is not limited to such examples. For example, the monitoring unit visualizes the data flow on a dashboard, and at a glance, which data is being used in which field and how. The monitoring unit can also monitor the usage status of data in real time and make adjustments as necessary. As a result, the data linkage system according to the embodiment can efficiently collect, analyze, link, and monitor data.

[0030] The collection unit collects data in each field. The data in each field includes, for example, medical data, traffic data, environmental data, etc., but is not limited to such examples. The collection unit collects, for example, city OS data and DX data. The city OS data includes smart city platforms and city management systems. Specifically, the city OS data includes information on urban infrastructure management, traffic control, energy management, provision of public services, etc. These data are collected in real time from sensors and IoT devices and transmitted to a central database. The DX data includes digitalization of business processes and data-driven decision-making, etc. Specifically, the DX data includes information on the efficiency of a company's business processes, management of customer data, marketing analysis, optimization of the supply chain, etc. These data are collected from the company's internal systems and cloud services. The collection unit collects data from these diverse data sources and manages them centrally to facilitate the integration and utilization of data. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, the accuracy of traffic congestion prediction can be improved by increasing the frequency of traffic data collection. The collector can also perform data pre-processing and cleaning to ensure data quality, allowing the collector to collect data efficiently and effectively, improving overall system performance.

[0031] The analysis unit uses the generation AI to analyze the collected data and unify the vocabulary. The unification of vocabulary includes, for example, the use of a standardized dictionary and the unification of synonyms, but is not limited to such examples. Specifically, the generation AI uses a text generation AI (for example, LLM) to unify the vocabulary of the collected data. The LLM has learned a large amount of text data and can understand technical terms and synonyms in different fields and convert them into appropriate vocabulary. For example, it can unify "myocardial infarction" in medical data and "accident" in traffic data as the same "emergency situation". The analysis unit can also convert the collected data into a common vocabulary using the generation AI. For example, the generation AI analyzes city OS data and DX data and converts them into a common vocabulary. This allows data from different fields to be handled in a unified manner, making it easier to link and analyze the data. Furthermore, the analysis unit can perform preprocessing and cleaning of the data to improve the quality of the data. For example, the accuracy of the analysis results can be improved by complementing missing data and detecting and correcting outliers. The analysis unit can also perform statistical analysis of data and construct machine learning models to extract useful knowledge from the data, enabling the analysis unit to analyze collected data quickly and accurately and improve the performance of the entire system.

[0032] The linking unit performs data linking based on the unified data. Data linking includes, for example, the use of APIs and the unification of data formats, but is not limited to such examples. Specifically, the linking unit links data in different fields based on the unified data. For example, by linking medical data and traffic data, the efficiency of emergency medical services can be improved. The medical data includes information on the patient's medical history and current health condition, and the traffic data includes information on road congestion and optimal routes. The linking unit integrates these data so that emergency medical services can select the optimal route and quickly reach the patient. In addition, the linking unit unifies data formats to facilitate data exchange between different systems. For example, by using standard data formats such as JSON and XML, data exchange between different systems can be performed smoothly. Furthermore, the linking unit uses APIs to realize data linking between different systems. The API enables data exchange between different systems and improves the scalability of the system. As a result, the linking unit can efficiently link data in different fields and improve the performance of the entire system.

[0033] The monitoring unit visualizes the data flow on a dashboard and performs anomaly detection and performance monitoring. The dashboard includes, for example, but is not limited to, display of real-time data and anomaly detection alerts. Specifically, the monitoring unit visualizes the data flow on a dashboard and grasps at a glance how which data is being used in which field. For example, it is possible to display in real time how medical data is being used in which hospital and how traffic data is being used on which road. The monitoring unit can also monitor the data usage status in real time and make adjustments as necessary. For example, if an abnormality occurs in the data flow, the monitoring unit can immediately issue an alert, identify the cause of the problem, and take appropriate measures. Furthermore, the monitoring unit can monitor the data performance and identify improvements to improve the efficiency of the entire system. For example, the data processing speed and response time are monitored, and bottlenecks are identified and improved. The monitoring unit can also monitor the security of the data and take measures to prevent unauthorized access and data leakage. This allows the monitoring unit to improve the reliability and safety of the entire system.

[0034] The collection unit can collect city operating system data or digital transformation data. The city operating system data includes, for example, a smart city platform and a city management system, but is not limited to such examples. The collection unit, for example, collects city OS data. The city OS data includes information on the operation and management of a city. The digital transformation data includes, for example, digitalization of business processes and data-driven decision-making, but is not limited to such examples. The collection unit, for example, collects DX data. The DX data includes information on a company's business processes and decision-making. This allows the collection unit to efficiently collect city OS and DX data.

[0035] The analysis unit can analyze the collected data and convert it into a common vocabulary. The common vocabulary includes, for example, an industry standard glossary or a specific dictionary, but is not limited to such examples. The analysis unit can analyze, for example, the collected data and convert it into a common vocabulary. For example, the generation AI uses a text generation AI (for example, LLM) to unify the vocabulary of the collected data. The analysis unit can also use the generation AI to convert the collected data into a common vocabulary. For example, the generation AI analyzes city OS data and DX data and converts them into a common vocabulary. As a result, the analysis unit unifies the vocabulary of the data, making it easier to link the data.

[0036] The linking unit can perform data linking based on unified data. Data linking includes, for example, the use of APIs and unification of data formats, but is not limited to these examples. The linking unit, for example, links data from different fields based on unified data. For example, city OS data and DX data are linked in a unified format, which makes city operation and management more efficient. This allows the linking unit to easily link data from different fields.

[0037] The monitoring unit can visualize the data flow on a dashboard and detect anomalies and monitor performance. The dashboard includes, for example, but is not limited to, display of real-time data and anomaly detection alerts. The monitoring unit can visualize the data flow on a dashboard, for example, and grasp at a glance how which data is being used in which field. The monitoring unit can also monitor the data usage status in real time and make adjustments as necessary. This enables the monitoring unit to visualize the data flow and detect anomalies and monitor performance.

[0038] The monitoring unit can monitor the data usage status in real time and make adjustments as necessary. Examples of real-time monitoring include, but are not limited to, the use of streaming data and the setting of real-time alerts. The monitoring unit can monitor, for example, the data usage status in real time and make adjustments as necessary. For example, the monitoring unit can monitor the data usage status in real time and issue an alert if an abnormality is detected. The monitoring unit can also monitor the data usage status in real time and make adjustments if a decrease in performance is detected. This allows the monitoring unit to monitor the data usage status in real time and make appropriate adjustments.

[0039] When collecting data in each field, the collection unit can evaluate the reliability of the data and preferentially collect highly reliable data. For example, the collection unit evaluates the reliability of a data source and preferentially collects data from a highly rated data source. The collection unit can also check the consistency of the data and preferentially collect consistent data. Furthermore, the collection unit can check the recency of the data and preferentially collect the latest data. As a result, the collection unit preferentially collects highly reliable data, thereby improving the quality of the data. 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.

[0040] The collection unit can apply different collection algorithms depending on the type and format of data when collecting data. For example, in the case of text data, the collection unit applies a natural language processing algorithm to collect the data. In addition, in the case of image data, the collection unit can also apply an image recognition algorithm to collect the data. Furthermore, in the case of numerical data, the collection unit can also apply a statistical analysis algorithm to collect the data. This enables the collection unit to collect data efficiently by applying a collection algorithm depending on the type and format of data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The collection unit can adjust the collection range in consideration of the geographical data distribution when collecting data. For example, the collection unit can preferentially collect data from urban areas and perform detailed urban analysis. The collection unit can also preferentially collect data from rural areas and perform agriculture-related analysis. Furthermore, the collection unit can collect international data and perform analysis from a global perspective. This allows the collection unit to adjust the collection range in consideration of the geographical data distribution, thereby enabling efficient data collection. 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.

[0042] The collection unit can collect relevant data using social media and other external data sources when collecting data. The collection unit, for example, collects social media posts and analyzes user opinions and emotions. The collection unit can also collect data from open data sources and utilize public information. Furthermore, the collection unit can collect data provided by other companies and organizations to increase mutually usable data. As a result, the collection unit collects relevant data using social media and other external data sources, thereby improving the diversity of data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient data analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. Furthermore, the analysis unit can also apply a statistical analysis algorithm to numerical data. This allows the analysis unit to apply an analysis algorithm according to the category of data, thereby enabling efficient data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0045] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. For example, the analysis unit preferentially analyzes the latest data and provides real-time information. The analysis unit can also preferentially analyze past data and grasp long-term trends. Furthermore, the analysis unit can also preferentially analyze data from a specific period and clarify the characteristics of that period. This enables the analysis unit to determine the priority of the analysis based on the time when the data was collected, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0046] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data. The analysis unit, for example, refers to external weather data and analyzes the impact of weather conditions on the data. The analysis unit can also refer to external economic data and analyze the impact of economic conditions on the data. Furthermore, the analysis unit can also refer to external social data and analyze the impact of social factors on the data. In this way, the analysis unit improves the accuracy of the analysis by referring to the related external data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The linking unit can improve the accuracy of linking by taking into account the interrelationships of the data when linking data. The linking unit, for example, analyzes interdependence of data and improves the accuracy of linking. The linking unit can also analyze correlations of data and improve the accuracy of linking. Furthermore, the linking unit can check consistency of data and improve the accuracy of linking. Thereby, the linking unit can improve the accuracy of linking by taking into account the interrelationships of data. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The linking unit can perform linking while taking into consideration attribute information of the data submitter. For example, the linking unit evaluates the reliability of the data submitter and preferentially links highly reliable data. The linking unit can also consider the specialty field of the data submitter and preferentially link highly relevant data. Furthermore, the linking unit can evaluate the past performance of the data submitter and preferentially link data with a proven track record. As a result, the linking unit performs linking while taking into consideration attribute information of the data submitter, thereby improving the accuracy of linking. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The linking unit can adjust the linking range in consideration of the geographical data distribution when linking data. For example, the linking unit can preferentially link urban data and perform detailed urban analysis. The linking unit can also preferentially link rural data and perform agriculture-related analysis. Furthermore, the linking unit can link international data and perform analysis from a global perspective. This enables efficient data linking by adjusting the linking range in consideration of the geographical data distribution. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI.

[0050] The linking unit can improve the accuracy of linking by referring to related external data when linking data. The linking unit, for example, refers to external weather data and links while taking into consideration the impact of weather conditions on data. The linking unit can also refer to external economic data and link while taking into consideration the impact of economic conditions on data. Furthermore, the linking unit can also refer to external social data and link while taking into consideration the impact of social factors on data. Thereby, the linking unit improves the accuracy of linking by referring to related external data. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI.

[0051] During monitoring, the monitoring unit can optimize the current monitoring algorithm by referring to past monitoring data. The monitoring unit, for example, analyzes past monitoring data to identify frequently occurring problems and adjust the monitoring algorithm. The monitoring unit can also extract anomaly patterns from past monitoring data to improve the accuracy of anomaly detection. Furthermore, the monitoring unit can reevaluate monitoring priorities and optimally allocate resources based on past monitoring data. As a result, the monitoring unit improves the accuracy of the monitoring algorithm by referring to past monitoring data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI.

[0052] The monitoring unit can apply different monitoring methods to different categories of data during monitoring. For example, the monitoring unit applies a monitoring method using natural language processing to text data. The monitoring unit can also apply a monitoring method using image recognition to image data. The monitoring unit can also apply a monitoring method using statistical analysis to numerical data. This enables efficient monitoring by the monitoring unit applying a monitoring method according to the category of data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI.

[0053] During monitoring, the monitoring unit can determine the monitoring priority based on the time when the data was collected. The monitoring unit, for example, prioritizes monitoring the latest data and provides real-time information. The monitoring unit can also prioritize monitoring of past data and grasp long-term trends. Furthermore, the monitoring unit can prioritize monitoring of data from a specific period and clarify the characteristics of that period. This enables efficient monitoring by the monitoring unit determining the monitoring priority based on the time when the data was collected. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI.

[0054] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to related external data. The monitoring unit, for example, refers to external weather data and monitors the impact of weather conditions on the data. The monitoring unit can also refer to external economic data and monitor the impact of economic conditions on the data. Furthermore, the monitoring unit can also refer to external social data and monitor the impact of social factors on the data. In this way, the monitoring unit improves the accuracy of monitoring by referring to related external data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI.

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

[0056] When collecting data, the collection unit can evaluate the privacy protection level of the data and prioritize collection of data that requires privacy protection. For example, the collection unit can securely collect data that includes personal information by using encryption technology. The collection unit can also collect data that includes confidential company information by strengthening access control. Furthermore, the collection unit can collect public data that does not require privacy protection by using normal methods. This allows the collection unit to adjust the collection method depending on the privacy protection level of the data, enabling safe and efficient data collection.

[0057] When analyzing data, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, the analysis unit can evaluate the reliability of a data source and prioritize analysis of data from a highly rated data source. The analysis unit can also check the consistency of the data and prioritize analysis of consistent data. Furthermore, the analysis unit can check the recency of the data and prioritize analysis of the latest data. In this way, the analysis unit can prioritize analysis of highly reliable data, thereby improving the accuracy of the analysis results.

[0058] When data is shared, the linking unit can evaluate the security level of the data and can share the data in a highly secure manner. For example, the linking unit can use encryption technology to securely share confidential data. The linking unit can also strengthen access control for important data. Furthermore, the linking unit can use normal methods to share public data. This allows the linking unit to adjust the linking method depending on the security level of the data, enabling safe and efficient data sharing.

[0059] The monitoring unit monitors the data usage status in real time, and can automatically issue an alert if an abnormality is detected. For example, the monitoring unit immediately issues an alert if an abnormality occurs in the data flow. The monitoring unit can also issue an alert if an abnormality is detected in the data usage status. Furthermore, the monitoring unit can also issue an alert if data performance deteriorates. In this way, the monitoring unit can monitor the data usage status in real time, and quickly respond if an abnormality occurs.

[0060] The monitoring unit can determine the monitoring priority based on the time when the data was collected. For example, the monitoring unit can monitor the latest data with priority and provide real-time information. The monitoring unit can also monitor past data with priority and grasp long-term trends. Furthermore, the monitoring unit can also monitor data from a specific period with priority and clarify the characteristics of that period. In this way, the monitoring unit can determine the monitoring priority based on the time when the data was collected, thereby enabling efficient monitoring.

[0061] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to related external data. For example, the monitoring unit can refer to external weather data and monitor the impact of weather conditions on the data. The monitoring unit can also refer to external economic data and monitor the impact of economic conditions on the data. Furthermore, the monitoring unit can also refer to external social data and monitor the impact of social factors on the data. In this way, the monitoring unit can improve the accuracy of monitoring by referring to related external data.

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

[0063] Step 1: The collection department collects data from each field. Data from each field includes medical data, traffic data, environmental data, etc. The collection department collects city OS data and DX data. City OS data includes smart city platforms and city management systems, and DX data includes digitalization of business processes and data-driven decision-making. Step 2: The analysis unit uses the generation AI to analyze the collected data and unify the vocabulary. Vocabulary unification includes the use of standardized dictionaries and unification of synonyms. The generation AI uses a text generation AI (e.g., LLM) to unify the vocabulary of the collected data and convert it into a common vocabulary. Step 3: The linking unit links data based on the data unified by the analysis unit. Data linking includes the use of APIs and standardization of data formats. The linking unit links data from different fields based on the unified data. Step 4: The monitoring unit visualizes the data flow on a dashboard and detects anomalies and monitors performance. The dashboard includes features such as displaying real-time data and providing alerts for anomaly detection. The monitoring unit visualizes the data flow on a dashboard, allowing users to see at a glance how each piece of data is being used in each field. It is also possible to monitor data usage in real time and make adjustments as necessary.

[0064] (Example 2) The data linkage system according to the embodiment of the present invention is a system that uses a generation AI to unify vocabulary and facilitate data linkage. This data linkage system collects data from each field and inputs it into the generation AI. The generation AI analyzes the collected data and unifies the vocabulary used in each field. For example, if city OS data and DX data are recorded in different formats, the generation AI analyzes the data and converts them into a common vocabulary. Next, data linkage is performed based on the data unified by the generation AI. This allows data from different fields to be easily linked and mutually usable. For example, city OS data and DX data are linked in a unified format, which makes the operation and management of cities more efficient. Furthermore, by understanding the exchange of data linkage on a dashboard, any analysis is possible. For example, the flow of data can be visualized on the dashboard, and it is possible to understand at a glance which data is being used in which field and how. This allows the data usage status to be monitored in real time and adjustments to be made as necessary. This mechanism promotes the unification of data formats, making it easier to link across fields such as DX and city OS. By using the generation AI, it is possible to achieve efficient and effective data linkage beyond the limits of human unification. This enables the data linkage system to efficiently collect, analyze, link, and monitor data.

[0065] The data linkage system according to the embodiment includes a collection unit, an analysis unit, a linkage unit, and a monitoring unit. The collection unit collects data in each field. The data in each field includes, for example, medical data, traffic data, and environmental data, but is not limited to such examples. The collection unit collects, for example, city OS data and DX data. The city OS data includes a smart city platform and a city management system. The DX data includes digitalization of business processes and data-driven decision-making. The analysis unit uses a generation AI to analyze the collected data and unify the vocabulary. The unification of vocabulary includes, for example, the use of a standardized dictionary and unification of synonyms, but is not limited to such examples. For example, the generation AI uses a text generation AI (for example, LLM) to unify the vocabulary of the collected data. The analysis unit can also convert the collected data into a common vocabulary using the generation AI. For example, the generation AI analyzes the city OS data and the DX data and converts them into a common vocabulary. The linkage unit performs data linkage based on the unified data. Data linkage includes, for example, the use of APIs and the unification of data formats, but is not limited to such examples. For example, the linking unit links data from different fields based on unified data. The monitoring unit visualizes the data flow on a dashboard and performs anomaly detection and performance monitoring. The dashboard includes, for example, the display of real-time data and anomaly detection alerts, but is not limited to such examples. For example, the monitoring unit visualizes the data flow on a dashboard, and at a glance, which data is being used in which field and how. The monitoring unit can also monitor the usage status of data in real time and make adjustments as necessary. As a result, the data linkage system according to the embodiment can efficiently collect, analyze, link, and monitor data.

[0066] The collection unit collects data in each field. The data in each field includes, for example, medical data, traffic data, environmental data, etc., but is not limited to such examples. The collection unit collects, for example, city OS data and DX data. The city OS data includes smart city platforms and city management systems. Specifically, the city OS data includes information on urban infrastructure management, traffic control, energy management, provision of public services, etc. These data are collected in real time from sensors and IoT devices and transmitted to a central database. The DX data includes digitalization of business processes and data-driven decision-making, etc. Specifically, the DX data includes information on the efficiency of a company's business processes, management of customer data, marketing analysis, optimization of the supply chain, etc. These data are collected from the company's internal systems and cloud services. The collection unit collects data from these diverse data sources and manages them centrally to facilitate the integration and utilization of data. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, the accuracy of traffic congestion prediction can be improved by increasing the frequency of traffic data collection. The collector can also perform data pre-processing and cleaning to ensure data quality, allowing the collector to collect data efficiently and effectively, improving overall system performance.

[0067] The analysis unit uses the generation AI to analyze the collected data and unify the vocabulary. The unification of vocabulary includes, for example, the use of a standardized dictionary and the unification of synonyms, but is not limited to such examples. Specifically, the generation AI uses a text generation AI (for example, LLM) to unify the vocabulary of the collected data. The LLM has learned a large amount of text data and can understand technical terms and synonyms in different fields and convert them into appropriate vocabulary. For example, it can unify "myocardial infarction" in medical data and "accident" in traffic data as the same "emergency situation". The analysis unit can also convert the collected data into a common vocabulary using the generation AI. For example, the generation AI analyzes city OS data and DX data and converts them into a common vocabulary. This allows data from different fields to be handled in a unified manner, making it easier to link and analyze the data. Furthermore, the analysis unit can perform preprocessing and cleaning of the data to improve the quality of the data. For example, the accuracy of the analysis results can be improved by complementing missing data and detecting and correcting outliers. The analysis unit can also perform statistical analysis of data and construct machine learning models to extract useful knowledge from the data, enabling the analysis unit to analyze collected data quickly and accurately and improve the performance of the entire system.

[0068] The linking unit performs data linking based on the unified data. Data linking includes, for example, the use of APIs and the unification of data formats, but is not limited to such examples. Specifically, the linking unit links data in different fields based on the unified data. For example, by linking medical data and traffic data, the efficiency of emergency medical services can be improved. The medical data includes information on the patient's medical history and current health condition, and the traffic data includes information on road congestion and optimal routes. The linking unit integrates these data so that emergency medical services can select the optimal route and quickly reach the patient. In addition, the linking unit unifies data formats to facilitate data exchange between different systems. For example, by using standard data formats such as JSON and XML, data exchange between different systems can be performed smoothly. Furthermore, the linking unit uses APIs to realize data linking between different systems. The API enables data exchange between different systems and improves the scalability of the system. As a result, the linking unit can efficiently link data in different fields and improve the performance of the entire system.

[0069] The monitoring unit visualizes the data flow on a dashboard and performs anomaly detection and performance monitoring. The dashboard includes, for example, but is not limited to, display of real-time data and anomaly detection alerts. Specifically, the monitoring unit visualizes the data flow on a dashboard and grasps at a glance how which data is being used in which field. For example, it is possible to display in real time how medical data is being used in which hospital and how traffic data is being used on which road. The monitoring unit can also monitor the data usage status in real time and make adjustments as necessary. For example, if an abnormality occurs in the data flow, the monitoring unit can immediately issue an alert, identify the cause of the problem, and take appropriate measures. Furthermore, the monitoring unit can monitor the data performance and identify improvements to improve the efficiency of the entire system. For example, the data processing speed and response time are monitored, and bottlenecks are identified and improved. The monitoring unit can also monitor the security of the data and take measures to prevent unauthorized access and data leakage. This allows the monitoring unit to improve the reliability and safety of the entire system.

[0070] The collection unit can collect city operating system data or digital transformation data. The city operating system data includes, for example, a smart city platform and a city management system, but is not limited to such examples. The collection unit, for example, collects city OS data. The city OS data includes information on the operation and management of a city. The digital transformation data includes, for example, digitalization of business processes and data-driven decision-making, but is not limited to such examples. The collection unit, for example, collects DX data. The DX data includes information on a company's business processes and decision-making. This allows the collection unit to efficiently collect city OS and DX data.

[0071] The analysis unit can analyze the collected data and convert it into a common vocabulary. The common vocabulary includes, for example, an industry standard glossary or a specific dictionary, but is not limited to such examples. The analysis unit can analyze, for example, the collected data and convert it into a common vocabulary. For example, the generation AI uses a text generation AI (for example, LLM) to unify the vocabulary of the collected data. The analysis unit can also use the generation AI to convert the collected data into a common vocabulary. For example, the generation AI analyzes city OS data and DX data and converts them into a common vocabulary. As a result, the analysis unit unifies the vocabulary of the data, making it easier to link the data.

[0072] The linking unit can perform data linking based on unified data. Data linking includes, for example, the use of APIs and unification of data formats, but is not limited to these examples. The linking unit, for example, links data from different fields based on unified data. For example, city OS data and DX data are linked in a unified format, which makes city operation and management more efficient. This allows the linking unit to easily link data from different fields.

[0073] The monitoring unit can visualize the data flow on a dashboard and detect anomalies and monitor performance. The dashboard includes, for example, but is not limited to, display of real-time data and anomaly detection alerts. The monitoring unit can visualize the data flow on a dashboard, for example, and grasp at a glance how which data is being used in which field. The monitoring unit can also monitor the data usage status in real time and make adjustments as necessary. This enables the monitoring unit to visualize the data flow and detect anomalies and monitor performance.

[0074] The monitoring unit can monitor the data usage status in real time and make adjustments as necessary. Examples of real-time monitoring include, but are not limited to, the use of streaming data and the setting of real-time alerts. The monitoring unit can monitor, for example, the data usage status in real time and make adjustments as necessary. For example, the monitoring unit can monitor the data usage status in real time and issue an alert if an abnormality is detected. The monitoring unit can also monitor the data usage status in real time and make adjustments if a decrease in performance is detected. This allows the monitoring unit to monitor the data usage status in real time and make appropriate adjustments.

[0075] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can preferentially collect only important data. This allows the collection unit to adjust the timing of data collection according to the user's emotion, thereby enabling efficient data collection. 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0076] When collecting data in each field, the collection unit can evaluate the reliability of the data and preferentially collect highly reliable data. For example, the collection unit evaluates the reliability of a data source and preferentially collects data from a highly rated data source. The collection unit can also check the consistency of the data and preferentially collect consistent data. Furthermore, the collection unit can check the recency of the data and preferentially collect the latest data. As a result, the collection unit preferentially collects highly reliable data, thereby improving the quality of the data. 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.

[0077] The collection unit can apply different collection algorithms depending on the type and format of data when collecting data. For example, in the case of text data, the collection unit applies a natural language processing algorithm to collect the data. In addition, in the case of image data, the collection unit can also apply an image recognition algorithm to collect the data. Furthermore, in the case of numerical data, the collection unit can also apply a statistical analysis algorithm to collect the data. This enables the collection unit to collect data efficiently by applying a collection algorithm depending on the type and format of data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects only important data. In addition, when the user is relaxed, the collection unit can also preferentially collect detailed data. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect data that can be collected quickly. This allows the collection unit to determine the priority of data to be collected according to the user's emotion, thereby enabling efficient data collection. 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.

[0079] The collection unit can adjust the collection range in consideration of the geographical data distribution when collecting data. For example, the collection unit can preferentially collect data from urban areas and perform detailed urban analysis. The collection unit can also preferentially collect data from rural areas and perform agriculture-related analysis. Furthermore, the collection unit can collect international data and perform analysis from a global perspective. This allows the collection unit to adjust the collection range in consideration of the geographical data distribution, thereby enabling efficient data collection. 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.

[0080] The collection unit can collect relevant data using social media and other external data sources when collecting data. The collection unit, for example, collects social media posts and analyzes user opinions and emotions. The collection unit can also collect data from open data sources and utilize public information. Furthermore, the collection unit can collect data provided by other companies and organizations to increase mutually usable data. As a result, the collection unit collects relevant data using social media and other external data sources, thereby improving the diversity of data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0081] The analysis unit can estimate the user's emotion and adjust the way in which the analysis result is expressed based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, when the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, when the user is in a hurry, the analysis unit can also provide an analysis result that focuses on the main points. In this way, the analysis unit can provide an analysis result that is easy for the user to understand by adjusting the way in which the analysis result is expressed 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.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on important data. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling efficient data analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0083] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. Furthermore, the analysis unit can also apply a statistical analysis algorithm to numerical data. This allows the analysis unit to apply an analysis algorithm according to the category of data, thereby enabling efficient data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The analysis unit can estimate the user's emotion and determine the priority of the analysis results based on the estimated user's emotion. For example, when the user is nervous, the analysis unit preferentially displays important analysis results. Furthermore, when the user is relaxed, the analysis unit can also preferentially display detailed analysis results. Furthermore, when the user is in a hurry, the analysis unit can quickly display analysis results. This enables efficient data analysis by the analysis unit determining the priority of analysis 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0085] During analysis, the analysis unit can determine the priority of the analysis based on the time when the data was collected. For example, the analysis unit preferentially analyzes the latest data and provides real-time information. The analysis unit can also preferentially analyze past data and grasp long-term trends. Furthermore, the analysis unit can also preferentially analyze data from a specific period and clarify the characteristics of that period. This enables the analysis unit to determine the priority of the analysis based on the time when the data was collected, thereby enabling efficient data analysis. Part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to related external data. The analysis unit, for example, refers to external weather data and analyzes the impact of weather conditions on the data. The analysis unit can also refer to external economic data and analyze the impact of economic conditions on the data. Furthermore, the analysis unit can also refer to external social data and analyze the impact of social factors on the data. In this way, the analysis unit improves the accuracy of the analysis by referring to the related external data. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0087] The linking unit can estimate the user's emotion and adjust the data linking method based on the estimated user's emotion. For example, when the user is nervous, the linking unit provides a simple and intuitive linking method. Furthermore, when the user is relaxed, the linking unit can also provide a detailed linking method. Furthermore, when the user is in a hurry, the linking unit can also provide a method that allows quick linking. This allows the linking unit to adjust the data linking method according to the user's emotion, thereby enabling efficient data linking. 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0088] The linking unit can improve the accuracy of linking by taking into account the interrelationships of the data when linking data. The linking unit, for example, analyzes interdependence of data and improves the accuracy of linking. The linking unit can also analyze correlations of data and improve the accuracy of linking. Furthermore, the linking unit can check consistency of data and improve the accuracy of linking. Thereby, the linking unit can improve the accuracy of linking by taking into account the interrelationships of data. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI.

[0089] The linking unit can perform linking while taking into consideration attribute information of the data submitter. For example, the linking unit evaluates the reliability of the data submitter and preferentially links highly reliable data. The linking unit can also consider the specialty field of the data submitter and preferentially link highly relevant data. Furthermore, the linking unit can evaluate the past performance of the data submitter and preferentially link data with a proven track record. As a result, the linking unit performs linking while taking into consideration attribute information of the data submitter, thereby improving the accuracy of linking. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI.

[0090] The linking unit can estimate the user's emotion and determine the priority of data to be linked based on the estimated user's emotion. For example, when the user is nervous, the linking unit prioritizes linking important data. Also, when the user is relaxed, the linking unit can prioritize linking detailed data. Furthermore, when the user is in a hurry, the linking unit can prioritize linking data that can be quickly linked. This allows the linking unit to determine the priority of data to be linked according to the user's emotion, thereby enabling efficient data linking. 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.

[0091] The linking unit can adjust the linking range in consideration of the geographical data distribution when linking data. For example, the linking unit can preferentially link urban data and perform detailed urban analysis. The linking unit can also preferentially link rural data and perform agriculture-related analysis. Furthermore, the linking unit can link international data and perform analysis from a global perspective. This enables efficient data linking by adjusting the linking range in consideration of the geographical data distribution. Some or all of the above-mentioned processing in the linking unit may be performed, for example, using AI or may be performed without using AI.

[0092] The linking unit can improve the accuracy of linking by referring to related external data when linking data. The linking unit, for example, refers to external weather data and links while taking into consideration the impact of weather conditions on data. The linking unit can also refer to external economic data and link while taking into consideration the impact of economic conditions on data. Furthermore, the linking unit can also refer to external social data and link while taking into consideration the impact of social factors on data. Thereby, the linking unit improves the accuracy of linking by referring to related external data. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI.

[0093] The monitoring unit can estimate the user's emotion and adjust the display method of the monitoring result based on the estimated user's emotion. For example, when the user is nervous, the monitoring unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the monitoring unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. In this way, the monitoring unit can provide monitoring results that are easy for the user to understand by adjusting the display method of the monitoring result 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 (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0094] During monitoring, the monitoring unit can optimize the current monitoring algorithm by referring to past monitoring data. The monitoring unit, for example, analyzes past monitoring data to identify frequently occurring problems and adjust the monitoring algorithm. The monitoring unit can also extract anomaly patterns from past monitoring data to improve the accuracy of anomaly detection. Furthermore, the monitoring unit can reevaluate monitoring priorities and optimally allocate resources based on past monitoring data. As a result, the monitoring unit improves the accuracy of the monitoring algorithm by referring to past monitoring data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI.

[0095] The monitoring unit can apply different monitoring methods to different categories of data during monitoring. For example, the monitoring unit applies a monitoring method using natural language processing to text data. The monitoring unit can also apply a monitoring method using image recognition to image data. The monitoring unit can also apply a monitoring method using statistical analysis to numerical data. This enables efficient monitoring by the monitoring unit applying a monitoring method according to the category of data. Some or all of the above-mentioned processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The monitoring unit can estimate the user's emotion and determine the monitoring priority based on the estimated user's emotion. For example, when the user is nervous, the monitoring unit can display important monitoring items with priority. Also, when the user is relaxed, the monitoring unit can display detailed monitoring items with priority. Furthermore, when the user is in a hurry, the monitoring unit can quickly display the monitoring results. This allows the monitoring unit to determine the monitoring priority according to the user's emotion, thereby enabling efficient monitoring. 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.

[0097] During monitoring, the monitoring unit can determine the monitoring priority based on the time when the data was collected. The monitoring unit, for example, prioritizes monitoring the latest data and provides real-time information. The monitoring unit can also prioritize monitoring of past data and grasp long-term trends. Furthermore, the monitoring unit can prioritize monitoring of data from a specific period and clarify the characteristics of that period. This enables efficient monitoring by the monitoring unit determining the monitoring priority based on the time when the data was collected. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI.

[0098] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to related external data. The monitoring unit, for example, refers to external weather data and monitors the impact of weather conditions on the data. The monitoring unit can also refer to external economic data and monitor the impact of economic conditions on the data. Furthermore, the monitoring unit can also refer to external social data and monitor the impact of social factors on the data. In this way, the monitoring unit improves the accuracy of monitoring by referring to related external data. Some or all of the above-mentioned processing in the monitoring unit may be performed, for example, using AI or may be performed without using AI.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] When collecting data, the collection unit can evaluate the privacy protection level of the data and prioritize collection of data that requires privacy protection. For example, the collection unit can securely collect data that includes personal information by using encryption technology. The collection unit can also collect data that includes confidential company information by strengthening access control. Furthermore, the collection unit can collect public data that does not require privacy protection by using normal methods. This allows the collection unit to adjust the collection method depending on the privacy protection level of the data, enabling safe and efficient data collection.

[0101] When analyzing data, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, the analysis unit can evaluate the reliability of a data source and prioritize analysis of data from a highly rated data source. The analysis unit can also check the consistency of the data and prioritize analysis of consistent data. Furthermore, the analysis unit can check the recency of the data and prioritize analysis of the latest data. In this way, the analysis unit can prioritize analysis of highly reliable data, thereby improving the accuracy of the analysis results.

[0102] When data is shared, the linking unit can evaluate the security level of the data and can share the data in a highly secure manner. For example, the linking unit can use encryption technology to securely share confidential data. The linking unit can also strengthen access control for important data. Furthermore, the linking unit can use normal methods to share public data. This allows the linking unit to adjust the linking method depending on the security level of the data, enabling safe and efficient data sharing.

[0103] The monitoring unit monitors the data usage status in real time, and can automatically issue an alert if an abnormality is detected. For example, the monitoring unit immediately issues an alert if an abnormality occurs in the data flow. The monitoring unit can also issue an alert if an abnormality is detected in the data usage status. Furthermore, the monitoring unit can also issue an alert if data performance deteriorates. In this way, the monitoring unit can monitor the data usage status in real time, and quickly respond if an abnormality occurs.

[0104] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important data. This allows the collection unit to adjust the frequency of data collection according to the user's emotions, thereby enabling efficient data collection.

[0105] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is nervous, the analysis unit provides a simple and highly visible display method. When the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the display method of the analysis results according to the user's emotions.

[0106] The linking unit can estimate the user's emotion and adjust the method of data linking based on the estimated user's emotion. For example, the linking unit can provide a simple and intuitive linking method when the user is nervous. The linking unit can also provide a detailed linking method when the user is relaxed. Furthermore, the linking unit can also provide a method for quick linking when the user is in a hurry. In this way, the linking unit can adjust the method of data linking according to the user's emotion, thereby enabling efficient data linking.

[0107] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user's emotions. For example, when the user is nervous, the monitoring unit provides a simple and highly visible display method. When the user is relaxed, the monitoring unit can also provide a display method including detailed information. Furthermore, when the user is in a hurry, the monitoring unit can also provide a display method that focuses on the main points. In this way, the monitoring unit can provide monitoring results that are easy for the user to understand by adjusting the display method of the monitoring results according to the user's emotions.

[0108] The monitoring unit can determine the monitoring priority based on the time when the data was collected. For example, the monitoring unit can monitor the latest data with priority and provide real-time information. The monitoring unit can also monitor past data with priority and grasp long-term trends. Furthermore, the monitoring unit can also monitor data from a specific period with priority and clarify the characteristics of that period. In this way, the monitoring unit can determine the monitoring priority based on the time when the data was collected, thereby enabling efficient monitoring.

[0109] During monitoring, the monitoring unit can improve the accuracy of monitoring by referring to related external data. For example, the monitoring unit can refer to external weather data and monitor the impact of weather conditions on the data. The monitoring unit can also refer to external economic data and monitor the impact of economic conditions on the data. Furthermore, the monitoring unit can also refer to external social data and monitor the impact of social factors on the data. In this way, the monitoring unit can improve the accuracy of monitoring by referring to related external data.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The collection department collects data from each field. Data from each field includes medical data, traffic data, environmental data, etc. The collection department collects city OS data and DX data. City OS data includes smart city platforms and city management systems, and DX data includes digitalization of business processes and data-driven decision-making. Step 2: The analysis unit uses the generation AI to analyze the collected data and unify the vocabulary. Vocabulary unification includes the use of standardized dictionaries and unification of synonyms. The generation AI uses a text generation AI (e.g., LLM) to unify the vocabulary of the collected data and convert it into a common vocabulary. Step 3: The linking unit links data based on the data unified by the analysis unit. Data linking includes the use of APIs and standardization of data formats. The linking unit links data from different fields based on the unified data. Step 4: The monitoring unit visualizes the data flow on a dashboard and detects anomalies and monitors performance. The dashboard includes features such as displaying real-time data and providing alerts for anomaly detection. The monitoring unit visualizes the data flow on the dashboard, allowing users to see at a glance how each piece of data is being used in each field. It is also possible to monitor data usage in real time and make adjustments as necessary.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements including the above-mentioned collection unit, analysis unit, linkage unit, and monitoring unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects data in each field. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI and unifies the vocabulary. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs data linkage based on the unified data. The monitoring unit is realized, for example, by the control unit 46A of the smart device 14 and visualizes the flow of data on a dashboard, and performs anomaly detection and performance monitoring. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements including the above-mentioned collection unit, analysis unit, linkage unit, and monitoring unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects data in each field. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI and unifies the vocabulary. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs data linkage based on the unified data. The monitoring unit is realized, for example, by the control unit 46A of the smart glasses 214 and visualizes the flow of data on a dashboard, and performs anomaly detection and performance monitoring. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements including the above-mentioned collection unit, analysis unit, linkage unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects data in each field. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generation AI and unifies the vocabulary. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs data linkage based on the unified data. The monitoring unit is realized, for example, by the control unit 46A of the headset type terminal 314 and visualizes the flow of data on a dashboard, and performs anomaly detection and performance monitoring. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements including the above-mentioned collection unit, analysis unit, linkage unit, and monitoring unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414, and collects data in each field. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data using a generation AI and unifies the vocabulary. The linkage unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and performs data linkage based on the unified data. The monitoring unit is realized, for example, by the control unit 46A of the robot 414, and visualizes the data flow on a dashboard, and performs anomaly detection and performance monitoring. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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 in which a processor is used 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.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Appendix 1) A collection department that collects data in each field; an analysis unit that analyzes the data collected by the collection unit and standardizes vocabulary; A linking unit that performs data linking based on the data unified by the analysis unit; A monitoring unit that grasps details of the data linkage performed by the linkage unit on a dashboard. A system characterized by: (Appendix 2) The collecting unit includes: Collect city operating system data or digital transformation data 2. The system of claim 1, (Appendix 3) The analysis unit is Analyze collected data and convert it into a common vocabulary 2. The system of claim 1, (Appendix 4) The linking portion is Data integration based on unified data 2. The system of claim 1, (Appendix 5) The monitoring unit is Visualize data flow on a dashboard to detect anomalies and monitor performance 2. The system of claim 1, (Appendix 6) The monitoring unit is Monitor data usage in real time and make adjustments as needed 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting data in each field, evaluate the reliability of the data and prioritize collection of reliable data. 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting data, different collection algorithms are applied depending on the type and format of the data. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, adjust the collection range to take into account the geographical distribution of data. 2. The system of claim 1, (Appendix 12) The collecting unit includes: During data collection, utilize social media and other external data sources to gather relevant data 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and prioritize analysis results based on the estimated user emotions. 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, referencing relevant external data to improve the accuracy of the analysis 2. The system of claim 1, (Appendix 19) The linking portion is Infer user emotions and adjust data sharing methods based on the inferred user emotions 2. The system of claim 1, (Appendix 20) The linking portion is When linking data, improve the accuracy of the link by considering the interrelationships between data 2. The system of claim 1, (Appendix 21) The linking portion is When linking data, the attribute information of the data submitter is taken into consideration 2. The system of claim 1, (Appendix 22) The linking portion is Infer user sentiment and prioritize data to be linked based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The linking portion is When linking data, adjust the scope of linking by taking into account the geographical distribution of data. 2. The system of claim 1, (Appendix 24) The linking portion is When linking data, refer to related external data to improve the accuracy of the link 2. The system of claim 1, (Appendix 25) The monitoring unit is To estimate a user's emotion and adjust a display method of a monitoring result based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The monitoring unit is During monitoring, past monitoring data is referenced to optimize the current monitoring algorithm 2. The system of claim 1, (Appendix 27) The monitoring unit is During monitoring, apply different monitoring techniques to different categories of data 2. The system of claim 1, (Appendix 28) The monitoring unit is Estimate user emotions and determine monitoring priorities based on the estimated user emotions. 2. The system of claim 1, (Appendix 29) The monitoring unit is When monitoring, prioritize monitoring based on when the data was collected 2. The system of claim 1, (Appendix 30) The monitoring unit is During monitoring, refer to relevant external data to improve monitoring accuracy 2. The system of claim 1, [Explanation of symbols]

[0184] 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 department that collects data in each field; an analysis unit that analyzes the data collected by the collection unit and standardizes vocabulary; A linking unit that performs data linking based on the data unified by the analysis unit; A monitoring unit that grasps details of the data linkage performed by the linkage unit on a dashboard. A system characterized by:

2. The collecting unit includes: Collect city operating system data or digital transformation data 2. The system of claim 1 .

3. The monitoring unit is Visualize data flow on a dashboard to detect anomalies and monitor performance 2. The system of claim 1 .

4. The monitoring unit is Monitor data usage in real time and make adjustments as needed 2. The system of claim 1 .

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

6. The collecting unit includes: When collecting data in each field, evaluate the reliability of the data and prioritize collection of reliable data.

2. The system of claim 1 .

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