system

Abstract

The system according to this embodiment aims to create new value and services by integrating and utilizing diverse data within a corporate group. [Solution] The system according to the embodiment comprises a collection unit, a storage unit, an analysis unit, and a creation unit. The collection unit collects data from each company of the SoftBank Group. The storage unit stores the data collected by the collection unit. The analysis unit analyzes the data stored in the storage unit. The creation unit creates new value and services based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that diverse data within a corporate group has not been fully utilized integratively to create new value and services.

[0005] The system according to the embodiment aims to integratively utilize diverse data within a corporate group to create new value and services.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a storage unit, an analysis unit, and a creation unit. The data collection unit collects data from each company within the SoftBank Group. The storage unit stores the data collected by the data collection unit. The analysis unit analyzes the data stored in the storage unit. The creation unit creates new value and services based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can create new value and services by integrating and utilizing diverse data within a corporate group. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The data infrastructure system according to an embodiment of the present invention is a system that brings together all of SoftBank's assets, such as its communication infrastructure, data centers, and AI processing platform, as well as the assets of its group companies, to create an integrated data infrastructure called "SB DataBank." SB DataBank stores diverse data from the SoftBank Group, and this data is analyzed by artificial superintelligence (ASI). ASI extracts insights far exceeding human knowledge and uses them to create new value and services. For example, it integrates SoftBank's assets, such as its communication infrastructure, data centers, and AI processing platform, to construct the data infrastructure "SB DataBank." Next, it collects data from each company in the SoftBank Group and stores it in SB DataBank. This data includes, for example, communication data, payment data, search data, and medical data. Then, the stored data is analyzed by artificial superintelligence (ASI). ASI analyzes vast amounts of data and extracts insights that surpass human knowledge. For example, it can analyze medical data to discover new treatments or analyze consumer purchasing data to propose new marketing strategies. Furthermore, it creates new value and services based on the insights obtained by ASI. For example, in the medical field, AI could propose new treatments based on analyzed data, and in the financial field, it could develop new financial products based on consumer purchasing data. This would allow the data infrastructure system to maximize the use of SoftBank Group's assets and generate new value and services through artificial superintelligence (ASI). This would enable the provision of new value to various sectors, both for businesses (B2B) and consumers (B2C). For example, applications are expected in fields such as healthcare, real estate, logistics, finance, automotive, and retail. This would allow the data infrastructure system to maximize the use of SoftBank Group's assets and generate new value and services through artificial superintelligence (ASI).

[0029] The data infrastructure system according to the embodiment comprises a collection unit, a storage unit, an analysis unit, and a creation unit. The collection unit collects data from each company of the SoftBank Group. The collection unit can collect data such as communication data, payment data, search data, and medical data. For example, to collect communication data, the collection unit can acquire data from the communication infrastructure. The collection unit can also acquire data from the payment system to collect payment data. Furthermore, the collection unit can acquire data from the search engine to collect search data. For example, the collection unit can acquire call history and message content from the communication infrastructure. It can acquire credit card transaction and electronic money transaction data from the payment system. It can acquire search keywords and search history from the search engine. The storage unit stores the data collected by the collection unit. For example, the storage unit can store data in a database. For example, the storage unit can store text data, numerical data, image data, etc., in the database. The storage unit can also specify the data storage format. For example, the storage unit can specify a specific file format to store text data in the database. It can specify a specific data format to store numerical data. To save image data, a specific image format is specified. The analysis unit analyzes the data stored in the storage unit. For example, the analysis unit can analyze medical data to discover new treatments. For example, the analysis unit uses machine learning algorithms to analyze medical data. The analysis unit can also analyze consumer purchase data to propose new marketing strategies. For example, the analysis unit analyzes medical records and test results to discover new treatments by analyzing medical data. It analyzes purchase history and purchase patterns to propose new marketing strategies by analyzing consumer purchase data. The creation unit creates new value and services based on the analysis results obtained by the analysis unit. For example, the creation unit can propose new treatments based on data analyzed by AI. For example, the creation unit proposes drug therapies or new surgical techniques to propose new treatments based on data analyzed by AI.Furthermore, the creation unit can also develop new financial products based on consumer purchasing data. For example, the creation unit can develop new investment products or insurance products based on consumer purchasing data in order to develop new financial products. In this way, the data infrastructure system according to the embodiment can collect, store, analyze, and create data from each company in the SoftBank Group, thereby generating new value and services.

[0030] The Data Collection Unit collects data from various companies within the SoftBank Group. For example, it can collect data such as communication data, payment data, search data, and medical data. Specifically, to collect communication data, it obtains data from communication infrastructure. This infrastructure includes base stations and communication servers, from which it obtains detailed data such as call history, message content, and data traffic. To collect payment data, it obtains data from payment systems. These include credit card payment systems and electronic money payment systems, from which it obtains detailed data such as transaction date and time, transaction amount, and transaction location. To collect search data, it obtains data from search engines. Search engines contain detailed data such as search keywords entered by users, search history, and clicked links. To collect medical data, it obtains data from electronic medical record systems and testing equipment at medical institutions. This includes medical records, test results, and prescription information. The Data Collection Unit collects this data in real time and transmits it to a central database. Security and privacy protection are crucial for data collection, and data encryption and access control are applied. Furthermore, the Data Collection Unit performs data integrity checks and error checks to ensure data quality. This allows the data collection unit to efficiently collect high-quality data from diverse data sources, thereby strengthening the data infrastructure of the entire system.

[0031] The storage unit stores the data collected by the collection unit. The storage unit can, for example, store data in a database. Specifically, it uses relational databases or NoSQL databases to store text data, numerical data, image data, etc. Relational databases define relationships between tables and manipulate data using SQL queries to maintain data integrity and consistency. NoSQL databases, on the other hand, offer scalability and flexibility, and can efficiently store large amounts of data. The storage unit can also specify the data storage format. For example, it can specify JSON or XML format for text data, CSV or Parquet format for numerical data, and JPEG or PNG format for image data. Furthermore, the storage unit has data backup and recovery functions to protect against data loss or corruption. Access control and encryption are applied to data storage to ensure data security. The storage unit can speed up data retrieval and searching by utilizing data indexing and caching functions. This allows the storage unit to efficiently store large amounts of data and access it quickly when needed.

[0032] The analysis department analyzes data stored in the data storage department. For example, the analysis department can analyze medical data to discover new treatments. Specifically, it uses machine learning algorithms to analyze medical records and test results to predict patient symptoms and treatment effectiveness. This involves techniques such as regression analysis, clustering, and deep learning. To propose new marketing strategies by analyzing consumer purchase data, the department analyzes purchase history and patterns. For example, it uses collaborative filtering and association rule mining to identify customer preferences and purchasing trends and provide personalized product recommendations. As part of data preprocessing, the analysis department imputes missing values, removes outliers, and normalizes the data. Furthermore, to visualize the analysis results, it generates graphs and charts to allow for an intuitive understanding of data trends and patterns. The analysis department also performs streaming analysis of real-time data to support immediate decision-making. In this way, the analysis department can analyze stored data from multiple perspectives and provide valuable insights.

[0033] The Creation Department creates new value and services based on the analysis results obtained by the Analysis Department. Specifically, it can propose new treatment methods based on data analyzed by AI. For example, it can propose drug therapies or new surgical techniques based on medical records and test results analyzed by AI. This includes a process in which AI predicts the patient's symptoms and treatment effectiveness and selects the optimal treatment method. To develop new financial products based on consumer purchasing data, it analyzes purchase history and purchasing patterns to develop new investment and insurance products. For example, AI analyzes the consumer's risk profile and proposes customized financial products that meet individual needs. To realize these new values ​​and services, the Creation Department develops prototypes and conducts demonstration experiments to verify their practical application. Furthermore, the Creation Department collects feedback from users and continuously improves and optimizes its services. As a result, the Creation Department can quickly create and put into practical use new value and services based on analysis results.

[0034] The data collection unit can collect data such as communication data, payment data, search data, and medical data. For example, to collect communication data, the data collection unit can acquire data from communication infrastructure. For example, the data collection unit can acquire call history and message content. The data collection unit can also acquire data from payment systems to collect payment data. For example, the data collection unit can acquire data on credit card transactions and electronic money transactions. The data collection unit can also acquire data from search engines to collect search data. For example, the data collection unit can acquire search keywords and search history. The data collection unit can also acquire data from medical institutions to collect medical data. For example, the data collection unit can acquire medical records and test results. By collecting diverse data in this way, analysis in a wide range of fields becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input call history and message content acquired from communication infrastructure into AI and have the AI ​​perform data collection.

[0035] The analysis department can analyze medical data to discover new treatments. For example, the analysis department uses machine learning algorithms to analyze medical data. For example, the analysis department analyzes medical records and test results. The analysis department can also use data mining techniques to analyze medical data and discover new treatments. For example, the analysis department analyzes medical records and test results using data mining techniques. The analysis department can also use natural language processing techniques to analyze medical data and discover new treatments. For example, the analysis department analyzes medical records and test results using natural language processing techniques. This makes it possible to discover new treatments through the analysis of medical data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input medical records and test results into AI and have the AI ​​discover new treatments.

[0036] The analytics department can analyze consumer purchase data and propose new marketing strategies. For example, the analytics department may use machine learning algorithms to analyze consumer purchase data. For instance, it might analyze purchase history and patterns. The analytics department can also use data mining techniques to analyze consumer purchase data and propose new marketing strategies. For example, it might analyze purchase history and patterns using data mining techniques. Furthermore, the analytics department can use natural language processing techniques to analyze consumer purchase data and propose new marketing strategies. For example, it might analyze purchase history and patterns using natural language processing techniques. This enables the proposal of new marketing strategies through the analysis of consumer purchase data. Some or all of the above processes in the analytics department may be performed using AI, or not. For example, the analytics department could input purchase history and patterns into an AI and have the AI ​​propose new marketing strategies.

[0037] The development unit can propose new treatments based on data analyzed by AI. For example, the development unit proposes drug therapies or new surgical techniques based on data analyzed by AI. For example, the development unit uses machine learning algorithms to propose new treatments based on data analyzed by AI. The development unit can also use data mining techniques to propose new treatments based on data analyzed by AI. For example, the development unit analyzes the data analyzed by AI using data mining techniques and proposes new treatments. The development unit can also use natural language processing techniques to propose new treatments based on data analyzed by AI. For example, the development unit analyzes the data analyzed by AI using natural language processing techniques and proposes new treatments. This makes it possible to propose new treatments based on data analyzed by AI. Some or all of the above processes in the development unit may be performed using AI, or not using AI. For example, the development unit can input the data analyzed by AI into the AI ​​and have the AI ​​propose new treatments.

[0038] The product development unit can develop new financial products based on consumer purchase data. For example, the product development unit can develop new investment products or insurance products based on consumer purchase data. For example, the product development unit can use machine learning algorithms to develop new financial products based on consumer purchase data. The product development unit can also use data mining techniques to develop new financial products based on consumer purchase data. For example, the product development unit can analyze consumer purchase data using data mining techniques and develop new financial products. The product development unit can also use natural language processing techniques to develop new financial products based on consumer purchase data. For example, the product development unit can analyze consumer purchase data using natural language processing techniques and develop new financial products. This makes it possible to develop new financial products based on consumer purchase data. Some or all of the above processes in the product development unit may be performed using AI, for example, or without AI. For example, the product development unit can input consumer purchase data into AI and have the AI ​​develop new financial products.

[0039] The data collection unit can analyze each company's data collection history and select the optimal collection method. For example, the data collection unit can analyze each company's past data collection history to identify the most efficient collection method. For example, the data collection unit can evaluate the time and cost of collection from each company's data collection history and select the optimal method. The data collection unit can also propose improvements to the collection method based on each company's data collection history and optimize it. For example, the data collection unit can analyze each company's past data collection history to identify the most efficient collection method. For example, the data collection unit can evaluate the time and cost of collection from each company's data collection history and select the optimal method. The data collection unit can also propose improvements to the collection method based on each company's data collection history and optimize it. In this way, the optimal collection method can be selected by analyzing each company's data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each company's data collection history into AI and have the AI ​​select the optimal collection method.

[0040] The data collection unit can filter data based on each company's current business status and areas of interest during data collection. For example, the data collection unit can understand each company's current business status and collect only relevant data. For example, the data collection unit can prioritize the collection of necessary data based on each company's areas of interest. The data collection unit can also adjust the scope of data collection according to each company's business status and areas of interest. For example, the data collection unit can understand each company's current business status and collect only relevant data. For example, the data collection unit can prioritize the collection of necessary data based on each company's areas of interest. The data collection unit can also adjust the scope of data collection according to each company's business status and areas of interest. This allows for the collection of highly relevant data by filtering the data based on each company's business status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each company's business status and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can prioritize the collection of region-related data based on the location of each company. For example, the data collection unit can prioritize the collection of nearby data by considering the geographical location information of each company. Furthermore, the data collection unit can also collect region-specific data based on the geographical location information of each company. For example, the data collection unit can prioritize the collection of region-related data based on the location of each company. For example, the data collection unit can prioritize the collection of nearby data by considering the geographical location information of each company. Furthermore, the data collection unit can also collect region-specific data based on the geographical location information of each company. This allows for the priority collection of highly relevant data by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of each company into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0042] The data collection unit can analyze each company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze each company's social media activities and collect relevant data. For example, the data collection unit can identify trends on each company's social media and prioritize the collection of relevant data. Furthermore, the data collection unit can adjust the scope of data collected based on each company's social media activities. This allows for the collection of relevant data by analyzing each company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input each company's social media activities into an AI and have the AI ​​collect the relevant data.

[0043] The storage unit can adjust the level of detail of data storage based on its importance. For example, the storage unit can store highly important data in detail and less important data in a simplified manner. The storage unit can also adjust the frequency of storage according to the importance of the data. Furthermore, the storage unit can take multiple backups of highly important data and limit less important data to a single backup. For example, the storage unit can store highly important data in detail and less important data in a simplified manner. The storage unit can also adjust the frequency of storage according to the importance of the data. Furthermore, the storage unit can take multiple backups of highly important data and limit less important data to a single backup. This enables efficient data management by adjusting the level of detail of storage based on the importance of the data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of storage.

[0044] The storage unit can apply different storage algorithms depending on the data category during storage. For example, the storage unit can apply a high-precision storage algorithm to medical data and a high-speed storage algorithm to communication data. For example, the storage unit can select the optimal compression algorithm depending on the data category. The storage unit can also apply different security measures based on the data category. For example, the storage unit can apply a high-precision storage algorithm to medical data and a high-speed storage algorithm to communication data. For example, the storage unit can select the optimal compression algorithm depending on the data category. The storage unit can also apply different security measures based on the data category. This enables efficient data management by applying different storage algorithms depending on the data category. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the data category into the AI ​​and have the AI ​​execute the application of the storage algorithm.

[0045] The storage unit can determine the priority of data storage based on the data submission date. For example, the storage unit can prioritize storing the latest data and postpone older data. The storage unit can adjust the frequency of storage based on the data submission date. The storage unit can also prioritize storing data with a recent submission date and postpone data with a distant submission date. For example, the storage unit can prioritize storing the latest data and postpone older data. The storage unit can adjust the frequency of storage based on the data submission date. The storage unit can also prioritize storing data with a recent submission date and postpone data with a distant submission date. This allows the storage unit to prioritize storing the latest data by determining the priority of storage based on the data submission date. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the data submission date into the AI ​​and have the AI ​​determine the priority of storage.

[0046] The storage unit can adjust the storage order based on the relevance of the data during storage. For example, the storage unit can prioritize storing highly relevant data and postpone storing less relevant data. The storage unit can also adjust the storage frequency based on the relevance of the data. Furthermore, the storage unit can efficiently manage data by grouping highly relevant data together. For example, the storage unit can prioritize storing highly relevant data and postpone storing less relevant data. For example, the storage unit can adjust the storage frequency based on the relevance of the data. Furthermore, the storage unit can efficiently manage data by grouping highly relevant data together. This enables efficient data management by adjusting the storage order based on the relevance of the data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the storage order.

[0047] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can analyze the interrelationships between data and perform analysis based on highly relevant data. For example, the analysis unit can optimize its analysis algorithm by considering the interrelationships between data. Furthermore, the analysis unit can improve the reliability of its analysis results based on the interrelationships between data. For example, the analysis unit can analyze the interrelationships between data and perform analysis based on highly relevant data. For example, the analysis unit can optimize its analysis algorithm by considering the interrelationships between data. Furthermore, the analysis unit can improve the reliability of its analysis results based on the interrelationships between data. As a result, the accuracy of the analysis is improved by considering the interrelationships between data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between data into AI and have AI perform the task of improving the accuracy of the analysis.

[0048] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can improve the accuracy of the analysis based on the attribute information of the data submitter. For example, the analysis unit can optimize the analysis algorithm by considering the attribute information of the data submitter. Furthermore, the analysis unit can also improve the reliability of the analysis results based on the attribute information of the data submitter. For example, the analysis unit can improve the accuracy of the analysis based on the attribute information of the data submitter. For example, the analysis unit can optimize the analysis algorithm by considering the attribute information of the data submitter. Furthermore, the analysis unit can improve the reliability of the analysis results by considering the attribute information of the data submitter. As a result, the accuracy of the analysis is improved by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information of the data submitter into AI and have AI perform the improvement of the accuracy of the analysis.

[0049] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze the geographical distribution of the data and perform analysis while considering the characteristics of each region. For example, the analysis unit can analyze region-specific trends based on the geographical distribution of the data. Furthermore, the analysis unit can improve the reliability of the analysis results by considering the geographical distribution of the data. For example, the analysis unit can analyze the geographical distribution of the data and perform analysis while considering the characteristics of each region. For example, the analysis unit can analyze region-specific trends based on the geographical distribution of the data. Furthermore, the analysis unit can improve the reliability of the analysis results by considering the geographical distribution of the data. This makes it possible to analyze region-specific trends by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical distribution of the data into AI and have AI perform improvements to the accuracy of the analysis.

[0050] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data. For example, the analysis unit can optimize its analysis algorithm based on relevant literature on the data. The analysis unit can also improve the reliability of its analysis results by referring to relevant literature on the data. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data. For example, the analysis unit can optimize its analysis algorithm based on relevant literature on the data. The analysis unit can also improve the reliability of its analysis results by referring to relevant literature on the data. As a result, the accuracy of the analysis is improved by referring to relevant literature on the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature on the data into AI and have AI perform the analysis accuracy improvement.

[0051] The creation unit can improve the accuracy of creation by considering the interrelationships of data during the creation process. For example, the creation unit can analyze the interrelationships of data and create value or services based on highly relevant data. For example, the creation unit can optimize the creation algorithm by considering the interrelationships of data. Furthermore, the creation unit can improve the reliability of the creation results based on the interrelationships of data. For example, the creation unit can analyze the interrelationships of data and create value or services based on highly relevant data. For example, the creation unit can optimize the creation algorithm by considering the interrelationships of data. Furthermore, the creation unit can improve the reliability of the creation results based on the interrelationships of data. As a result, the accuracy of creation is improved by considering the interrelationships of data. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the interrelationships of data into AI and have AI perform the task of improving the accuracy of creation.

[0052] The creation unit can perform data creation while considering the attribute information of the data submitter. The creation unit can, for example, improve the accuracy of creation based on the attribute information of the data submitter. The creation unit can, for example, optimize the creation algorithm while considering the attribute information of the data submitter. Furthermore, the creation unit can also improve the reliability of the creation results based on the attribute information of the data submitter. For example, the creation unit can improve the accuracy of creation based on the attribute information of the data submitter. The creation unit can, for example, optimize the creation algorithm while considering the attribute information of the data submitter. Furthermore, the creation unit can also improve the reliability of the creation results based on the attribute information of the data submitter. As a result, the accuracy of creation is improved by considering the attribute information of the data submitter. Some or all of the above processing in the creation unit may be performed using AI, for example, or without using AI. For example, the creation unit can input the attribute information of the data submitter into AI and have AI perform the task of improving the accuracy of creation.

[0053] The creation unit can perform creation while considering the geographical distribution of data. For example, the creation unit can analyze the geographical distribution of data and create value and services while considering the characteristics of each region. For example, the creation unit can create region-specific value and services based on the geographical distribution of data. Furthermore, the creation unit can improve the reliability of the creation results by considering the geographical distribution of data. For example, the creation unit can analyze the geographical distribution of data and create value and services while considering the characteristics of each region. For example, the creation unit can create region-specific value and services based on the geographical distribution of data. Furthermore, the creation unit can improve the reliability of the creation results by considering the geographical distribution of data. In this way, region-specific value and services can be created by considering the geographical distribution of data. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the geographical distribution of data into AI and have AI perform improvements to the accuracy of creation.

[0054] The creation unit can improve the accuracy of data creation by referring to relevant literature during the creation process. For example, the creation unit can improve the accuracy of data creation by referring to relevant literature. The creation unit can optimize the creation algorithm based on relevant literature. The creation unit can also improve the reliability of the creation results by referring to relevant literature. For example, the creation unit can improve the accuracy of data creation by referring to relevant literature. The creation unit can optimize the creation algorithm based on relevant literature. The creation unit can also improve the reliability of the creation results by referring to relevant literature. As a result, the accuracy of data creation is improved by referring to relevant literature. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input relevant literature into AI and have AI perform the task of improving the accuracy of data creation.

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

[0056] The data infrastructure system can also include a forecasting unit. Based on data obtained from the collection, storage, and analysis units, the forecasting unit can predict future trends and demand. For example, the forecasting unit can analyze consumer purchasing data to predict popular products for the next season. It can also analyze medical data to predict the timing of the next influenza outbreak. Furthermore, it can analyze traffic data to predict the timing of the next congestion. This allows the data infrastructure system to provide more appropriate services to businesses and individuals by predicting future trends and demand.

[0057] The data infrastructure system can also include a feedback unit. The feedback unit collects user feedback on the value and services provided by the creation unit and provides this feedback to the analysis unit. For example, the feedback unit collects user reviews and ratings and provides them to the analysis unit. It can also collect user usage data and provide it to the analysis unit. Furthermore, it can collect user behavior data and provide it to the analysis unit. This allows the data infrastructure system to improve its value and services based on user feedback, enabling it to provide better services.

[0058] The data infrastructure system can also include a notification unit. This notification unit notifies users of information obtained from the analysis and creation units. For example, the notification unit can notify users of health management advice based on the analysis results of medical data. It can also notify users of recommended products based on the analysis results of consumer purchase data. Furthermore, it can notify users of traffic congestion information based on the analysis results of traffic data. This allows the data infrastructure system to provide users with appropriate information in a timely manner.

[0059] The data infrastructure system can also include a customization section. This customization section customizes the value and services provided based on user preferences and behavioral patterns. For example, the customization section can provide a personalized product list based on the user's past purchase history. It can also provide a personalized health management plan based on the user's health data. Furthermore, it can provide personalized route guidance based on the user's travel data. This allows the data infrastructure system to provide users with more personalized services.

[0060] The data infrastructure system can also include a security unit. The security unit ensures the security of data obtained from the collection, storage, analysis, and creation units. For example, the security unit can encrypt data to protect it from unauthorized access. It can also manage data access permissions, ensuring that only authorized users can access the data. Furthermore, the security unit can back up data to prevent data loss. This allows the data infrastructure system to ensure data security and provide highly reliable services.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data from various companies within the SoftBank Group. The data collection unit can collect data such as communication data, payment data, search data, and medical data. The data collection unit obtains call history and message content from communication infrastructure, credit card transaction and electronic money transaction data from payment systems, and search keywords and search history from search engines. Step 2: The storage unit stores the data collected by the collection unit. The storage unit can store text data, numerical data, image data, etc., in a database and can specify the data storage format. For example, it can specify a specific file format for storing text data, a specific data format for storing numerical data, and a specific image format for storing image data. Step 3: The analysis department analyzes the data stored in the storage department. The analysis department uses machine learning algorithms to analyze medical data to discover new treatments and analyzes consumer purchasing data to propose new marketing strategies. For example, they analyze medical records and test results to analyze purchase history and purchasing patterns. Step 4: The Creation Department creates new value and services based on the analysis results obtained by the Analysis Department. The Creation Department proposes new treatments based on data analyzed by AI and develops new financial products based on consumer purchasing data. For example, they propose drug therapies and new surgical techniques, and develop new investment and insurance products.

[0063] (Example of form 2) The data infrastructure system according to an embodiment of the present invention is a system that brings together all of SoftBank's assets, such as its communication infrastructure, data centers, and AI processing platform, as well as the assets of its group companies, to create an integrated data infrastructure called "SB DataBank." SB DataBank stores diverse data from the SoftBank Group, and this data is analyzed by artificial superintelligence (ASI). ASI extracts insights far exceeding human knowledge and uses them to create new value and services. For example, it integrates SoftBank's assets, such as its communication infrastructure, data centers, and AI processing platform, to construct the data infrastructure "SB DataBank." Next, it collects data from each company in the SoftBank Group and stores it in SB DataBank. This data includes, for example, communication data, payment data, search data, and medical data. Then, the stored data is analyzed by artificial superintelligence (ASI). ASI analyzes vast amounts of data and extracts insights that surpass human knowledge. For example, it can analyze medical data to discover new treatments or analyze consumer purchasing data to propose new marketing strategies. Furthermore, it creates new value and services based on the insights obtained by ASI. For example, in the medical field, AI could propose new treatments based on analyzed data, and in the financial field, it could develop new financial products based on consumer purchasing data. This would allow the data infrastructure system to maximize the use of SoftBank Group's assets and generate new value and services through artificial superintelligence (ASI). This would enable the provision of new value to various sectors, both for businesses (B2B) and consumers (B2C). For example, applications are expected in fields such as healthcare, real estate, logistics, finance, automotive, and retail. This would allow the data infrastructure system to maximize the use of SoftBank Group's assets and generate new value and services through artificial superintelligence (ASI).

[0064] The data infrastructure system according to the embodiment comprises a collection unit, a storage unit, an analysis unit, and a creation unit. The collection unit collects data from each company of the SoftBank Group. The collection unit can collect data such as communication data, payment data, search data, and medical data. For example, to collect communication data, the collection unit can acquire data from the communication infrastructure. The collection unit can also acquire data from the payment system to collect payment data. Furthermore, the collection unit can acquire data from the search engine to collect search data. For example, the collection unit can acquire call history and message content from the communication infrastructure. It can acquire credit card transaction and electronic money transaction data from the payment system. It can acquire search keywords and search history from the search engine. The storage unit stores the data collected by the collection unit. For example, the storage unit can store data in a database. For example, the storage unit can store text data, numerical data, image data, etc., in the database. The storage unit can also specify the data storage format. For example, the storage unit can specify a specific file format to store text data in the database. It can specify a specific data format to store numerical data. To save image data, a specific image format is specified. The analysis unit analyzes the data stored in the storage unit. For example, the analysis unit can analyze medical data to discover new treatments. For example, the analysis unit uses machine learning algorithms to analyze medical data. The analysis unit can also analyze consumer purchase data to propose new marketing strategies. For example, the analysis unit analyzes medical records and test results to discover new treatments by analyzing medical data. It analyzes purchase history and purchase patterns to propose new marketing strategies by analyzing consumer purchase data. The creation unit creates new value and services based on the analysis results obtained by the analysis unit. For example, the creation unit can propose new treatments based on data analyzed by AI. For example, the creation unit proposes drug therapies or new surgical techniques to propose new treatments based on data analyzed by AI.Furthermore, the creation unit can also develop new financial products based on consumer purchasing data. For example, the creation unit can develop new investment products or insurance products based on consumer purchasing data in order to develop new financial products. In this way, the data infrastructure system according to the embodiment can collect, store, analyze, and create data from each company in the SoftBank Group, thereby generating new value and services.

[0065] The Data Collection Unit collects data from various companies within the SoftBank Group. For example, it can collect data such as communication data, payment data, search data, and medical data. Specifically, to collect communication data, it obtains data from communication infrastructure. This infrastructure includes base stations and communication servers, from which it obtains detailed data such as call history, message content, and data traffic. To collect payment data, it obtains data from payment systems. These include credit card payment systems and electronic money payment systems, from which it obtains detailed data such as transaction date and time, transaction amount, and transaction location. To collect search data, it obtains data from search engines. Search engines contain detailed data such as search keywords entered by users, search history, and clicked links. To collect medical data, it obtains data from electronic medical record systems and testing equipment at medical institutions. This includes medical records, test results, and prescription information. The Data Collection Unit collects this data in real time and transmits it to a central database. Security and privacy protection are crucial for data collection, and data encryption and access control are applied. Furthermore, the Data Collection Unit performs data integrity checks and error checks to ensure data quality. This allows the data collection unit to efficiently collect high-quality data from diverse data sources, thereby strengthening the data infrastructure of the entire system.

[0066] The storage unit stores the data collected by the collection unit. The storage unit can, for example, store data in a database. Specifically, it uses relational databases or NoSQL databases to store text data, numerical data, image data, etc. Relational databases define relationships between tables and manipulate data using SQL queries to maintain data integrity and consistency. NoSQL databases, on the other hand, offer scalability and flexibility, and can efficiently store large amounts of data. The storage unit can also specify the data storage format. For example, it can specify JSON or XML format for text data, CSV or Parquet format for numerical data, and JPEG or PNG format for image data. Furthermore, the storage unit has data backup and recovery functions to protect against data loss or corruption. Access control and encryption are applied to data storage to ensure data security. The storage unit can speed up data retrieval and searching by utilizing data indexing and caching functions. This allows the storage unit to efficiently store large amounts of data and access it quickly when needed.

[0067] The analysis department analyzes data stored in the data storage department. For example, the analysis department can analyze medical data to discover new treatments. Specifically, it uses machine learning algorithms to analyze medical records and test results to predict patient symptoms and treatment effectiveness. This involves techniques such as regression analysis, clustering, and deep learning. To propose new marketing strategies by analyzing consumer purchase data, the department analyzes purchase history and patterns. For example, it uses collaborative filtering and association rule mining to identify customer preferences and purchasing trends and provide personalized product recommendations. As part of data preprocessing, the analysis department imputes missing values, removes outliers, and normalizes the data. Furthermore, to visualize the analysis results, it generates graphs and charts to allow for an intuitive understanding of data trends and patterns. The analysis department also performs streaming analysis of real-time data to support immediate decision-making. In this way, the analysis department can analyze stored data from multiple perspectives and provide valuable insights.

[0068] The Creation Department creates new value and services based on the analysis results obtained by the Analysis Department. Specifically, it can propose new treatment methods based on data analyzed by AI. For example, it can propose drug therapies or new surgical techniques based on medical records and test results analyzed by AI. This includes a process in which AI predicts the patient's symptoms and treatment effectiveness and selects the optimal treatment method. To develop new financial products based on consumer purchasing data, it analyzes purchase history and purchasing patterns to develop new investment and insurance products. For example, AI analyzes the consumer's risk profile and proposes customized financial products that meet individual needs. To realize these new values ​​and services, the Creation Department develops prototypes and conducts demonstration experiments to verify their practical application. Furthermore, the Creation Department collects feedback from users and continuously improves and optimizes its services. As a result, the Creation Department can quickly create and put into practical use new value and services based on analysis results.

[0069] The data collection unit can collect data such as communication data, payment data, search data, and medical data. For example, to collect communication data, the data collection unit can acquire data from communication infrastructure. For example, the data collection unit can acquire call history and message content. The data collection unit can also acquire data from payment systems to collect payment data. For example, the data collection unit can acquire data on credit card transactions and electronic money transactions. The data collection unit can also acquire data from search engines to collect search data. For example, the data collection unit can acquire search keywords and search history. The data collection unit can also acquire data from medical institutions to collect medical data. For example, the data collection unit can acquire medical records and test results. By collecting diverse data in this way, analysis in a wide range of fields becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input call history and message content acquired from communication infrastructure into AI and have the AI ​​perform data collection.

[0070] The analysis department can analyze medical data to discover new treatments. For example, the analysis department uses machine learning algorithms to analyze medical data. For example, the analysis department analyzes medical records and test results. The analysis department can also use data mining techniques to analyze medical data and discover new treatments. For example, the analysis department analyzes medical records and test results using data mining techniques. The analysis department can also use natural language processing techniques to analyze medical data and discover new treatments. For example, the analysis department analyzes medical records and test results using natural language processing techniques. This makes it possible to discover new treatments through the analysis of medical data. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input medical records and test results into AI and have the AI ​​discover new treatments.

[0071] The analytics department can analyze consumer purchase data and propose new marketing strategies. For example, the analytics department may use machine learning algorithms to analyze consumer purchase data. For instance, it might analyze purchase history and patterns. The analytics department can also use data mining techniques to analyze consumer purchase data and propose new marketing strategies. For example, it might analyze purchase history and patterns using data mining techniques. Furthermore, the analytics department can use natural language processing techniques to analyze consumer purchase data and propose new marketing strategies. For example, it might analyze purchase history and patterns using natural language processing techniques. This enables the proposal of new marketing strategies through the analysis of consumer purchase data. Some or all of the above processes in the analytics department may be performed using AI, or not. For example, the analytics department could input purchase history and patterns into an AI and have the AI ​​propose new marketing strategies.

[0072] The development unit can propose new treatments based on data analyzed by AI. For example, the development unit proposes drug therapies or new surgical techniques based on data analyzed by AI. For example, the development unit uses machine learning algorithms to propose new treatments based on data analyzed by AI. The development unit can also use data mining techniques to propose new treatments based on data analyzed by AI. For example, the development unit analyzes the data analyzed by AI using data mining techniques and proposes new treatments. The development unit can also use natural language processing techniques to propose new treatments based on data analyzed by AI. For example, the development unit analyzes the data analyzed by AI using natural language processing techniques and proposes new treatments. This makes it possible to propose new treatments based on data analyzed by AI. Some or all of the above processes in the development unit may be performed using AI, or not using AI. For example, the development unit can input the data analyzed by AI into the AI ​​and have the AI ​​propose new treatments.

[0073] The product development unit can develop new financial products based on consumer purchase data. For example, the product development unit can develop new investment products or insurance products based on consumer purchase data. For example, the product development unit can use machine learning algorithms to develop new financial products based on consumer purchase data. The product development unit can also use data mining techniques to develop new financial products based on consumer purchase data. For example, the product development unit can analyze consumer purchase data using data mining techniques and develop new financial products. The product development unit can also use natural language processing techniques to develop new financial products based on consumer purchase data. For example, the product development unit can analyze consumer purchase data using natural language processing techniques and develop new financial products. This makes it possible to develop new financial products based on consumer purchase data. Some or all of the above processes in the product development unit may be performed using AI, for example, or without AI. For example, the product development unit can input consumer purchase data into AI and have the AI ​​develop new financial products.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. The data collection unit can also temporarily stop data collection if the user is in a hurry and resume it later. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on changes in facial expressions. The data collection unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the voice and calculate an emotion score. The data collection unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the data collection unit can calculate an emotion score based on fluctuations in heart rate. This allows the data collection timing to be adjusted according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0075] The data collection unit can analyze each company's data collection history and select the optimal collection method. For example, the data collection unit can analyze each company's past data collection history to identify the most efficient collection method. For example, the data collection unit can evaluate the time and cost of collection from each company's data collection history and select the optimal method. The data collection unit can also propose improvements to the collection method based on each company's data collection history and optimize it. For example, the data collection unit can analyze each company's past data collection history to identify the most efficient collection method. For example, the data collection unit can evaluate the time and cost of collection from each company's data collection history and select the optimal method. The data collection unit can also propose improvements to the collection method based on each company's data collection history and optimize it. In this way, the optimal collection method can be selected by analyzing each company's data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each company's data collection history into AI and have the AI ​​select the optimal collection method.

[0076] The data collection unit can filter data based on each company's current business status and areas of interest during data collection. For example, the data collection unit can understand each company's current business status and collect only relevant data. For example, the data collection unit can prioritize the collection of necessary data based on each company's areas of interest. The data collection unit can also adjust the scope of data collection according to each company's business status and areas of interest. For example, the data collection unit can understand each company's current business status and collect only relevant data. For example, the data collection unit can prioritize the collection of necessary data based on each company's areas of interest. The data collection unit can also adjust the scope of data collection according to each company's business status and areas of interest. This allows for the collection of highly relevant data by filtering the data based on each company's business status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input each company's business status and areas of interest into the AI ​​and have the AI ​​perform the data filtering.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit will postpone collecting less important data. If the user is relaxed, the unit can prioritize collecting detailed data. If the user is in a hurry, the unit can prioritize collecting only high-priority data. For example, the unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The unit can calculate an emotion score based on changes in facial expressions. The unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the unit can analyze the tone and speed of the voice and calculate an emotion score. The unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the unit can calculate an emotion score based on fluctuations in heart rate. This allows the system to prioritize data collection based on the user's emotions, ensuring that important data is collected first. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not using AI. For example, the collection unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each company during data collection. For example, the data collection unit can prioritize the collection of region-related data based on the location of each company. For example, the data collection unit can prioritize the collection of nearby data by considering the geographical location information of each company. Furthermore, the data collection unit can also collect region-specific data based on the geographical location information of each company. For example, the data collection unit can prioritize the collection of region-related data based on the location of each company. For example, the data collection unit can prioritize the collection of nearby data by considering the geographical location information of each company. Furthermore, the data collection unit can also collect region-specific data based on the geographical location information of each company. This allows for the priority collection of highly relevant data by considering the geographical location information of each company. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of each company into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0079] The data collection unit can analyze each company's social media activities and collect relevant data during data collection. For example, the data collection unit can analyze each company's social media activities and collect relevant data. For example, the data collection unit can identify trends on each company's social media and prioritize the collection of relevant data. Furthermore, the data collection unit can adjust the scope of data collected based on each company's social media activities. This allows for the collection of relevant data by analyzing each company's social media activities. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input each company's social media activities into an AI and have the AI ​​collect the relevant data.

[0080] The data storage unit can estimate the user's emotions and adjust the data storage method based on the estimated emotions. For example, if the user is stressed, the storage unit can reduce the frequency of data storage to alleviate the burden. For example, if the user is relaxed, the storage unit can store detailed data. Also, if the user is in a hurry, the storage unit can prioritize storing only important data. For example, the storage unit can capture the user's facial expressions with a camera and estimate emotions using an emotion estimation algorithm. For example, the storage unit can calculate an emotion score based on changes in facial expressions. The storage unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the storage unit can analyze the tone and speed of the voice and calculate an emotion score. The storage unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate emotions using an emotion estimation algorithm. For example, the storage unit can calculate an emotion score based on fluctuations in heart rate. In this way, the burden on the user can be reduced by adjusting the data storage method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, or not using AI. For example, the storage unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0081] The storage unit can adjust the level of detail of data storage based on its importance. For example, the storage unit can store highly important data in detail and less important data in a simplified manner. The storage unit can also adjust the frequency of storage according to the importance of the data. Furthermore, the storage unit can take multiple backups of highly important data and limit less important data to a single backup. For example, the storage unit can store highly important data in detail and less important data in a simplified manner. The storage unit can also adjust the frequency of storage according to the importance of the data. Furthermore, the storage unit can take multiple backups of highly important data and limit less important data to a single backup. This enables efficient data management by adjusting the level of detail of storage based on the importance of the data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the importance of the data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of storage.

[0082] The storage unit can apply different storage algorithms depending on the data category during storage. For example, the storage unit can apply a high-precision storage algorithm to medical data and a high-speed storage algorithm to communication data. For example, the storage unit can select the optimal compression algorithm depending on the data category. The storage unit can also apply different security measures based on the data category. For example, the storage unit can apply a high-precision storage algorithm to medical data and a high-speed storage algorithm to communication data. For example, the storage unit can select the optimal compression algorithm depending on the data category. The storage unit can also apply different security measures based on the data category. This enables efficient data management by applying different storage algorithms depending on the data category. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the data category into the AI ​​and have the AI ​​execute the application of the storage algorithm.

[0083] The data storage unit can estimate the user's emotions and adjust the order in which data is stored based on the estimated emotions. For example, if the user is stressed, the storage unit will postpone the storage of less important data. For example, if the user is relaxed, the storage unit can prioritize the storage of detailed data. Also, if the user is in a hurry, the storage unit can prioritize the storage of only high-priority data. For example, the storage unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the storage unit can calculate an emotion score based on changes in facial expressions. The storage unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the storage unit can analyze the tone and speed of the voice and calculate an emotion score. The storage unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the storage unit can calculate an emotion score based on fluctuations in heart rate. In this way, by adjusting the order in which data is stored according to the user's emotions, important data can be stored preferentially. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, or not using AI. For example, the storage unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0084] The storage unit can determine the priority of data storage based on the data submission date. For example, the storage unit can prioritize storing the latest data and postpone older data. The storage unit can adjust the frequency of storage based on the data submission date. The storage unit can also prioritize storing data with a recent submission date and postpone data with a distant submission date. For example, the storage unit can prioritize storing the latest data and postpone older data. The storage unit can adjust the frequency of storage based on the data submission date. The storage unit can also prioritize storing data with a recent submission date and postpone data with a distant submission date. This allows the storage unit to prioritize storing the latest data by determining the priority of storage based on the data submission date. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the data submission date into the AI ​​and have the AI ​​determine the priority of storage.

[0085] The storage unit can adjust the storage order based on the relevance of the data during storage. For example, the storage unit can prioritize storing highly relevant data and postpone storing less relevant data. The storage unit can also adjust the storage frequency based on the relevance of the data. Furthermore, the storage unit can efficiently manage data by grouping highly relevant data together. For example, the storage unit can prioritize storing highly relevant data and postpone storing less relevant data. For example, the storage unit can adjust the storage frequency based on the relevance of the data. Furthermore, the storage unit can efficiently manage data by grouping highly relevant data together. This enables efficient data management by adjusting the storage order based on the relevance of the data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the relevance of the data into the AI ​​and have the AI ​​perform the adjustment of the storage order.

[0086] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simplified analysis result. If the user is relaxed, for example, the analysis unit can provide a detailed analysis result. The analysis unit can also provide a concise analysis result if the user is in a hurry. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can calculate an emotion score based on changes in facial expressions, for example. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows the analysis unit to provide analysis results that are appropriate for the user by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0087] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between data. For example, the analysis unit can analyze the interrelationships between data and perform analysis based on highly relevant data. For example, the analysis unit can optimize its analysis algorithm by considering the interrelationships between data. Furthermore, the analysis unit can improve the reliability of its analysis results based on the interrelationships between data. For example, the analysis unit can analyze the interrelationships between data and perform analysis based on highly relevant data. For example, the analysis unit can optimize its analysis algorithm by considering the interrelationships between data. Furthermore, the analysis unit can improve the reliability of its analysis results based on the interrelationships between data. As a result, the accuracy of the analysis is improved by considering the interrelationships between data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the interrelationships between data into AI and have AI perform the task of improving the accuracy of the analysis.

[0088] The analysis unit can perform analysis while considering the attribute information of the data submitter. For example, the analysis unit can improve the accuracy of the analysis based on the attribute information of the data submitter. For example, the analysis unit can optimize the analysis algorithm by considering the attribute information of the data submitter. Furthermore, the analysis unit can also improve the reliability of the analysis results based on the attribute information of the data submitter. For example, the analysis unit can improve the accuracy of the analysis based on the attribute information of the data submitter. For example, the analysis unit can optimize the analysis algorithm by considering the attribute information of the data submitter. Furthermore, the analysis unit can improve the reliability of the analysis results by considering the attribute information of the data submitter. As a result, the accuracy of the analysis is improved by considering the attribute information of the data submitter. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the attribute information of the data submitter into AI and have AI perform the improvement of the accuracy of the analysis.

[0089] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will display important analysis results first. If the user is relaxed, the analysis unit can display detailed analysis results sequentially. Also, if the user is in a hurry, the analysis unit can display concise analysis results first. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on changes in facial expressions. The analysis unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of information tailored to the user by adjusting the display order of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform emotion estimation of the user.

[0090] The analysis unit can perform analysis while considering the geographical distribution of the data. For example, the analysis unit can analyze the geographical distribution of the data and perform analysis while considering the characteristics of each region. For example, the analysis unit can analyze region-specific trends based on the geographical distribution of the data. Furthermore, the analysis unit can improve the reliability of the analysis results by considering the geographical distribution of the data. For example, the analysis unit can analyze the geographical distribution of the data and perform analysis while considering the characteristics of each region. For example, the analysis unit can analyze region-specific trends based on the geographical distribution of the data. Furthermore, the analysis unit can improve the reliability of the analysis results by considering the geographical distribution of the data. This makes it possible to analyze region-specific trends by considering the geographical distribution of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical distribution of the data into AI and have AI perform improvements to the accuracy of the analysis.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data. For example, the analysis unit can optimize its analysis algorithm based on relevant literature on the data. The analysis unit can also improve the reliability of its analysis results by referring to relevant literature on the data. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the data. For example, the analysis unit can optimize its analysis algorithm based on relevant literature on the data. The analysis unit can also improve the reliability of its analysis results by referring to relevant literature on the data. As a result, the accuracy of the analysis is improved by referring to relevant literature on the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature on the data into AI and have AI perform the analysis accuracy improvement.

[0092] The creation unit can estimate the user's emotions and determine the priority of the value and services to create based on those estimated emotions. For example, if the user is stressed, the creation unit will prioritize providing relaxing services. If the user is relaxed, the creation unit can provide detailed services. Also, if the user is in a hurry, the creation unit can prioritize providing services that can be used quickly. For example, the creation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the creation unit can calculate an emotion score based on changes in facial expressions. The creation unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the creation unit can analyze the tone and speed of the voice and calculate an emotion score. The creation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the creation unit can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of services tailored to the user by determining the priority of value and services according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, or not using AI. For example, the creation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The creation unit can improve the accuracy of creation by considering the interrelationships of data during the creation process. For example, the creation unit can analyze the interrelationships of data and create value or services based on highly relevant data. For example, the creation unit can optimize the creation algorithm by considering the interrelationships of data. Furthermore, the creation unit can improve the reliability of the creation results based on the interrelationships of data. For example, the creation unit can analyze the interrelationships of data and create value or services based on highly relevant data. For example, the creation unit can optimize the creation algorithm by considering the interrelationships of data. Furthermore, the creation unit can improve the reliability of the creation results based on the interrelationships of data. As a result, the accuracy of creation is improved by considering the interrelationships of data. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the interrelationships of data into AI and have AI perform the task of improving the accuracy of creation.

[0094] The creation unit can perform data creation while considering the attribute information of the data submitter. The creation unit can, for example, improve the accuracy of creation based on the attribute information of the data submitter. The creation unit can, for example, optimize the creation algorithm while considering the attribute information of the data submitter. Furthermore, the creation unit can also improve the reliability of the creation results based on the attribute information of the data submitter. For example, the creation unit can improve the accuracy of creation based on the attribute information of the data submitter. The creation unit can, for example, optimize the creation algorithm while considering the attribute information of the data submitter. Furthermore, the creation unit can also improve the reliability of the creation results based on the attribute information of the data submitter. As a result, the accuracy of creation is improved by considering the attribute information of the data submitter. Some or all of the above processing in the creation unit may be performed using AI, for example, or without using AI. For example, the creation unit can input the attribute information of the data submitter into AI and have AI perform the task of improving the accuracy of creation.

[0095] The creation unit can estimate the user's emotions and adjust the display method of the value and services it creates based on the estimated user emotions. For example, if the user is stressed, the creation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the creation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the creation unit can provide a concise display method. For example, the creation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the creation unit can calculate an emotion score based on changes in facial expressions. The creation unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the creation unit can analyze the tone and speed of the voice and calculate an emotion score. The creation unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the creation unit can calculate an emotion score based on fluctuations in heart rate. This allows for the provision of information tailored to the user by adjusting the display method of value and services according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, or not using AI. For example, the creation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0096] The creation unit can perform creation while considering the geographical distribution of data. For example, the creation unit can analyze the geographical distribution of data and create value and services while considering the characteristics of each region. For example, the creation unit can create region-specific value and services based on the geographical distribution of data. Furthermore, the creation unit can improve the reliability of the creation results by considering the geographical distribution of data. For example, the creation unit can analyze the geographical distribution of data and create value and services while considering the characteristics of each region. For example, the creation unit can create region-specific value and services based on the geographical distribution of data. Furthermore, the creation unit can improve the reliability of the creation results by considering the geographical distribution of data. In this way, region-specific value and services can be created by considering the geographical distribution of data. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input the geographical distribution of data into AI and have AI perform improvements to the accuracy of creation.

[0097] The creation unit can improve the accuracy of data creation by referring to relevant literature during the creation process. For example, the creation unit can improve the accuracy of data creation by referring to relevant literature. The creation unit can optimize the creation algorithm based on relevant literature. The creation unit can also improve the reliability of the creation results by referring to relevant literature. For example, the creation unit can improve the accuracy of data creation by referring to relevant literature. The creation unit can optimize the creation algorithm based on relevant literature. The creation unit can also improve the reliability of the creation results by referring to relevant literature. As a result, the accuracy of data creation is improved by referring to relevant literature. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can input relevant literature into AI and have AI perform the task of improving the accuracy of data creation.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data infrastructure system can also include a forecasting unit. Based on data obtained from the collection, storage, and analysis units, the forecasting unit can predict future trends and demand. For example, the forecasting unit can analyze consumer purchasing data to predict popular products for the next season. It can also analyze medical data to predict the timing of the next influenza outbreak. Furthermore, it can analyze traffic data to predict the timing of the next congestion. This allows the data infrastructure system to provide more appropriate services to businesses and individuals by predicting future trends and demand.

[0100] The data infrastructure system can also include a feedback unit. The feedback unit collects user feedback on the value and services provided by the creation unit and provides this feedback to the analysis unit. For example, the feedback unit collects user reviews and ratings and provides them to the analysis unit. It can also collect user usage data and provide it to the analysis unit. Furthermore, it can collect user behavior data and provide it to the analysis unit. This allows the data infrastructure system to improve its value and services based on user feedback, enabling it to provide better services.

[0101] The data infrastructure system can also include a notification unit. This notification unit notifies users of information obtained from the analysis and creation units. For example, the notification unit can notify users of health management advice based on the analysis results of medical data. It can also notify users of recommended products based on the analysis results of consumer purchase data. Furthermore, it can notify users of traffic congestion information based on the analysis results of traffic data. This allows the data infrastructure system to provide users with appropriate information in a timely manner.

[0102] The data infrastructure system can also include a customization section. This customization section customizes the value and services provided based on user preferences and behavioral patterns. For example, the customization section can provide a personalized product list based on the user's past purchase history. It can also provide a personalized health management plan based on the user's health data. Furthermore, it can provide personalized route guidance based on the user's travel data. This allows the data infrastructure system to provide users with more personalized services.

[0103] The data infrastructure system can also include a security unit. The security unit ensures the security of data obtained from the collection, storage, analysis, and creation units. For example, the security unit can encrypt data to protect it from unauthorized access. It can also manage data access permissions, ensuring that only authorized users can access the data. Furthermore, the security unit can back up data to prevent data loss. This allows the data infrastructure system to ensure data security and provide highly reliable services.

[0104] The data infrastructure system can also include an emotion estimation unit. This unit estimates the user's emotions and collects, stores, analyzes, and creates data based on the estimated emotions. For example, the emotion estimation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also record the user's voice and estimate their emotions using voice analysis technology. Furthermore, the emotion estimation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the data infrastructure system to provide appropriate services according to the user's emotions.

[0105] The data infrastructure system can also include an emotional feedback unit. This unit collects user emotions regarding the value and services provided by the creation unit and feeds them back to the analysis unit. For example, the emotional feedback unit can collect user facial expressions and voice data and estimate emotions using an emotion estimation algorithm. It can also collect user biometric data and estimate emotions using an emotion estimation algorithm. Furthermore, the emotional feedback unit can provide user emotional data to the analysis unit for use in service improvement. This allows the data infrastructure system to improve value and services based on user emotions, enabling it to provide better services.

[0106] The data infrastructure system can also include an emotion notification unit. This unit notifies the user of information obtained from the analysis and creation units according to their emotions. For example, if the user is feeling stressed, the emotion notification unit can notify them of information that helps them relax. If the user is relaxed, the emotion notification unit can also notify them of detailed information. Furthermore, if the user is in a hurry, the emotion notification unit can notify them of concise information. This allows the data infrastructure system to provide appropriate information according to the user's emotions.

[0107] The data infrastructure system can also include an emotion customization unit. This unit customizes the value and services provided based on the user's emotions. For example, if the user is stressed, the emotion customization unit can provide relaxing services. If the user is relaxed, it can provide more detailed services. Furthermore, if the user is in a hurry, it can provide quickly accessible services. This allows the data infrastructure system to provide more personalized services in response to the user's emotions.

[0108] The data infrastructure system can also include an emotional security unit. This emotional security unit adjusts data security based on the user's emotions. For example, if the user is stressed, the emotional security unit can strictly manage data access permissions. Conversely, if the user is relaxed, the emotional security unit can flexibly manage data access permissions. Furthermore, if the user is in a hurry, the emotional security unit can allow for rapid data access. This enables the data infrastructure system to adjust data security according to the user's emotions and provide appropriate services.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects data from various companies within the SoftBank Group. The data collection unit can collect data such as communication data, payment data, search data, and medical data. The data collection unit obtains call history and message content from communication infrastructure, credit card transaction and electronic money transaction data from payment systems, and search keywords and search history from search engines. Step 2: The storage unit stores the data collected by the collection unit. The storage unit can store text data, numerical data, image data, etc., in a database and can specify the data storage format. For example, it can specify a specific file format for storing text data, a specific data format for storing numerical data, and a specific image format for storing image data. Step 3: The analysis department analyzes the data stored in the storage department. The analysis department uses machine learning algorithms to analyze medical data to discover new treatments and analyzes consumer purchasing data to propose new marketing strategies. For example, they analyze medical records and test results to analyze purchase history and purchasing patterns. Step 4: The Creation Department creates new value and services based on the analysis results obtained by the Analysis Department. The Creation Department proposes new treatments based on data analyzed by AI and develops new financial products based on consumer purchasing data. For example, they propose drug therapies and new surgical techniques, and develop new investment and insurance products.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, storage unit, analysis unit, and creation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires communication data using the communication I / F 44 of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The storage unit stores the data in the database 24 of the data processing unit 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12, and the creation unit generates new value or services based on the analysis results obtained by the specific processing unit 290. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, storage unit, analysis unit, and creation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires communication data using the communication I / F 44 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The storage unit stores the data in the database 24 of the data processing unit 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12, and the creation unit generates new value or services based on the analysis results obtained by the specific processing unit 290. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, storage unit, analysis unit, and creation unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the collection unit acquires communication data using the communication I / F 44 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The storage unit stores the data in the database 24 of the data processing unit 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12, and the creation unit generates new value or services based on the analysis results obtained by the specific processing unit 290. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, storage unit, analysis unit, and creation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit acquires communication data using the robot 414's communication I / F 44 and transmits it to the data processing unit 12 via the control unit 46A. The storage unit stores the data in the database 24 of the data processing unit 12. The analysis unit analyzes the data using the specific processing unit 290 of the data processing unit 12, and the creation unit generates new value or services based on the analysis results obtained by the specific processing unit 290. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The data collection department collects data from various companies within the SoftBank Group, A storage unit that stores the data collected by the aforementioned collection unit, An analysis unit analyzes the data stored in the storage unit, The system includes a creation unit that creates new value and services based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as communication data, payment data, search data, and medical data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyzing medical data to discover new treatment methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is We analyze consumer purchasing data to propose new marketing strategies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The creation unit is, We propose new treatment methods based on data analyzed by AI. The system described in Appendix 1, characterized by the features described herein. (Note 6) The creation unit is, Develop new financial products based on consumer purchasing data. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze each company's data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on each company's current business situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the geographical location information of each company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, we analyze each company's social media activities and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The storage unit is We estimate the user's emotions and adjust the data collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The storage unit is During storage, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The storage unit is During storage, different storage algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The storage unit is It estimates the user's emotions and adjusts the data storage order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The storage unit is During data storage, the storage priority is determined based on the data submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The storage unit is During storage, the storage order is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When performing analysis, consider the interrelationships between data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is When performing the analysis, the attribute information of the data submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When performing analysis, consider the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The creation unit is, We estimate user emotions and determine the priority of value creation and services based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The creation unit is, When creating data, consider the interrelationships between the data to improve the accuracy of the creation process. The system described in Appendix 1, characterized by the features described herein. (Note 27) The creation unit is, When creating data, the attribute information of the data submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 28) The creation unit is, It estimates user emotions and adjusts how value and services are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The creation unit is, When creating data, the geographical distribution of the data should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 30) The creation unit is, During data generation, we improve the accuracy of the generation by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects data from various companies within the SoftBank Group, A storage unit that stores the data collected by the aforementioned collection unit, An analysis unit analyzes the data stored in the storage unit, The system includes a creation unit that creates new value and services based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as communication data, payment data, search data, and medical data. The system according to feature 1.

3. The aforementioned analysis unit is Analyzing medical data to discover new treatment methods. The system according to feature 1.

4. The aforementioned analysis unit is We analyze consumer purchasing data to propose new marketing strategies. The system according to feature 1.

5. The creation unit is, We propose new treatment methods based on data analyzed by AI. The system according to feature 1.

6. The creation unit is, Develop new financial products based on consumer purchasing data. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is We analyze each company's data collection history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on each company's current business situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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