system

Abstract

The system according to the embodiment aims to facilitate smooth information sharing between the control team and the field activity team when a disaster occurs. [Solution] A system according to an embodiment includes an acquisition unit, a compilation unit, and a response unit. The acquisition unit collects information from field activity teams. The compilation unit compiles the information collected by the acquisition unit. The response unit makes a response based on the information compiled by the compilation unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in that information sharing between the control team and the on-site activity team became complicated when a disaster occurred.

[0005] The system according to the embodiment aims to facilitate smooth information sharing between the control team and the field activity team when a disaster occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a compilation unit, and a response unit. The acquisition unit collects information from the field activity team. The compilation unit compiles the information collected by the acquisition unit. The response unit makes a response based on the information compiled by the compilation unit. [Effects of the Invention]

[0007] The system according to the embodiment can smoothly share information between the control team and the field activity team when a disaster occurs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The information sharing support system according to an embodiment of the present invention supports information sharing between the control team and the field activity team during a disaster. This system listens to the requests of the control team and the field activity team, automatically responds to inquiries from the field activity team, and constantly organizes all information, ensuring that anyone can access it immediately. For example, the hearing function allows the field activity team to quickly collect the information they need. It can automatically collect not only lists of supplies but also the local situation and the assistance needed. Next, the automatic response function not only uses pre-defined information but also uses AI to learn new information in real time and generate appropriate responses. Furthermore, the information organization function prioritizes information and ensures that important information is quickly accessible. The frequency and accuracy of information updates are also important. Furthermore, the information generated by the app should be provided through a user-friendly interface. For example, visually organizing information in a dashboard format for intuitive operation is conceivable. It is also important that the app is multilingual. Because international assistance is often required during disasters, information should be available in multiple languages. Finally, security must also be considered. During a disaster, confidential information may be included, so it is important to encrypt the information and control access properly. This allows the information sharing support system to facilitate smooth information sharing between the control team and the field activity team, improving the efficiency of disaster response.

[0029] An information collaboration support system according to an embodiment includes an acquisition unit, a sorting unit, and a response unit. The acquisition unit collects information from the field activity team. The information from the field activity team includes, but is not limited to, a list of supplies, the current situation on the ground, and the details of necessary support. For example, the acquisition unit automatically collects information entered by the field activity team. The acquisition unit can also collect information about the current situation in real time using a sensor or a camera. For example, the acquisition unit may capture video of the local area with a camera and analyze the video data to understand the current situation. The acquisition unit can also analyze the voice input of the field activity team to extract necessary information. For example, the acquisition unit may convert the statements of the field activity team into text data using voice recognition technology and analyze the text data to extract necessary information. The sorting unit organizes the information collected by the acquisition unit. For example, the sorting unit may classify the collected information by category and set priorities. The sorting unit may also manage the update frequency of the information to ensure that the latest information is always provided. For example, the sorting unit may periodically update the collected information and delete old information. Furthermore, the organizing unit verifies the accuracy of the information to prevent incorrect information from being provided. For example, the organizing unit cross-checks the collected information to check for inconsistencies. The replying unit replies based on the information organized by the organizing unit. For example, the replying unit automatically replies to inquiries from field activity teams. The replying unit also provides a user-friendly interface to enable intuitive operation. For example, the replying unit visually organizes information in a dashboard format to allow users to easily access the information. Furthermore, the replying unit supports multiple languages ​​to ensure smooth international assistance. For example, the replying unit provides information in multiple languages ​​and displays the information in the language selected by the user. This enables the information collaboration support system according to the embodiment to efficiently collect, organize, and reply to information from field activity teams.

[0030] The organizing unit includes a priority setting unit that sets the priority of information. The priority setting unit evaluates the importance of collected information and sets the priority. For example, the priority setting unit uses an algorithm for evaluating the importance of information. The priority setting unit evaluates the importance of information based on evaluation items such as the urgency, impact, and reliability of the information. Furthermore, the priority setting unit sets the priority of information based on the evaluation results. For example, the priority setting unit prioritizes displaying information with a high level of urgency, enabling a quick response. Furthermore, the priority setting unit prioritizes displaying information with a high level of impact, preventing important information from being overlooked. Furthermore, the priority setting unit prioritizes displaying information with a high level of reliability, preventing erroneous information from being provided. In this way, the priority setting unit prioritizes information, thereby enabling quick access to important information.

[0031] The organizer includes an update management unit that manages the update frequency of the information. The update management unit manages the update frequency of the collected information. For example, the update management unit uses an algorithm to set the update timing of the information. The update management unit sets the update timing of the information based on evaluation items such as the importance of the information, the collection frequency, and the need for updates. Furthermore, the update management unit manages the update frequency of the information based on the evaluation results. For example, the update management unit frequently updates information with high importance so that the latest information is always provided. Furthermore, the update management unit frequently updates information with high collection frequency to ensure the accuracy of the information. Furthermore, the update management unit frequently updates information with a high need for updating to maintain the usefulness of the information. In this way, the update management unit manages the update frequency of the information so that the latest information is always provided.

[0032] The response unit includes an interface unit that provides a user-friendly interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit visually organizes information in a dashboard format, allowing the user to easily access the information. For example, the interface unit provides tabs for each information category, allowing the user to quickly access the information they need. The interface unit also displays important information in a prominent position based on the priority of the information, preventing the user from missing important information. Furthermore, the interface unit displays the update status of the information in real time, allowing the user to always be aware of the latest information. For example, the interface unit displays the update date and time of the information, allowing the user to check the freshness of the information. The interface unit also provides an information search function, allowing the user to quickly search for the information they need. For example, the interface unit provides keyword search and filtering functions, allowing the user to easily find specific information. As a result, the interface unit provides a user-friendly interface, allowing intuitive operation.

[0033] The response unit includes a multilingual unit that provides multilingual support. The multilingual unit provides information in multiple languages. For example, the multilingual unit displays information in a language selected by the user. The multilingual unit supports multiple languages, such as English, Japanese, and French. The multilingual unit also uses an algorithm to improve the accuracy of information translation. For example, the multilingual unit automatically translates information using machine translation technology. The multilingual unit also has a function to check the translation result and correct it as necessary. For example, the multilingual unit displays the translation result so that the user can check the translation content. The multilingual unit also provides a feedback function to evaluate and improve the accuracy of the translation. For example, the multilingual unit collects feedback from users and improves the translation algorithm. As a result, the multilingual unit provides multilingual support, thereby enabling smooth international support.

[0034] The response unit includes a security unit that ensures security. The security unit encrypts information and controls access to protect confidential information. For example, the security unit uses information encryption technology to safely transmit and receive information. The security unit uses encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The security unit also controls access to information to ensure that only authorized users can access the information. For example, the security unit uses user authentication technology to verify the identity of users. The security unit also has a function to record access logs of information and detect unauthorized access. For example, the security unit analyzes access logs and detects abnormal access patterns. The security unit also regularly backs up information to prevent data loss. For example, the security unit backs up information using cloud storage. In this way, the security unit ensures security and enables the protection of confidential information.

[0035] The acquisition unit can analyze the past activity history of the field activity team and select the optimal information collection method. The acquisition unit uses an algorithm to analyze the past activity history of the field activity team. For example, the acquisition unit proposes an optimal method based on information collection methods used by the field activity team in the past. The acquisition unit, for example, analyzes past activity history data and identifies effective information collection means. The acquisition unit can also predict effective information collection timing from the past activity history of the field activity team. For example, the acquisition unit proposes the optimal timing for information collection based on past activity history data. Furthermore, the acquisition unit analyzes the past activity history of the field activity team and selects the most efficient information collection means. For example, the acquisition unit identifies a means that maximizes the efficiency of information collection based on the past activity history data. As a result, the acquisition unit selects the optimal information collection method based on the past activity history, thereby enabling efficient information collection.

[0036] The acquisition unit can filter information based on local conditions and environments when collecting information. The acquisition unit uses an algorithm for filtering information taking into account local conditions and environments. For example, the acquisition unit selects an appropriate means of collecting information based on local weather information. The acquisition unit can, for example, analyze weather data and propose an optimal means of collecting information. The acquisition unit can also limit the scope of information collection taking into account local geographical conditions. For example, the acquisition unit can identify the scope of information collection using a geographic information system (GIS). Furthermore, the acquisition unit can filter unnecessary information and collect only important information based on local environmental conditions. For example, the acquisition unit can monitor local conditions using an environmental sensor and extract important information. As a result, the acquisition unit can collect only important information by filtering based on local conditions and environments.

[0037] When collecting information, the acquisition unit can prioritize collecting highly relevant information by taking into account local geographical location information. The acquisition unit uses an algorithm for collecting information by taking into account the local geographical location information. For example, the acquisition unit prioritizes collecting information about areas that will be heavily affected by a disaster based on the local geographical location information. The acquisition unit, for example, analyzes GPS data to identify areas that will be heavily affected. The acquisition unit can also prioritize collecting information about evacuation routes by taking into account the local geographical location information. For example, the acquisition unit identifies evacuation routes using a geographic information system (GIS). Furthermore, the acquisition unit prioritizes collecting information about areas that need assistance based on the local geographical location information. For example, the acquisition unit identifies areas that need assistance using a geographical information system (GIS). In this way, the acquisition unit can prioritize collecting highly relevant information by taking into account the geographical location information.

[0038] When collecting information, the acquisition unit can analyze the social media activities of the field activity team and collect related information. The acquisition unit uses an algorithm to analyze the social media activities of the field activity team. For example, the acquisition unit analyzes the social media posts of the field activity team and collects information about the local situation. The acquisition unit, for example, analyzes social media posting data to understand the local situation. The acquisition unit can also identify the necessary support content and collect information from the social media activities of the field activity team. For example, the acquisition unit analyzes social media posting data to identify the necessary support content. Furthermore, the acquisition unit collects information according to local needs based on the social media activities of the field activity team. For example, the acquisition unit analyzes social media posting data to understand the local needs. In this way, the acquisition unit can efficiently collect information about the local situation by analyzing social media activity.

[0039] When organizing information, the organizing unit can adjust the level of detail of the organizing based on the importance of the information. The organizing unit uses an algorithm for evaluating the importance of the information. For example, the organizing unit sets evaluation criteria for evaluating the importance of the information. The organizing unit evaluates the importance of the information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the organizing unit adjusts the level of detail of the information organizing based on the evaluation results. For example, the organizing unit organizes information with high importance in detail to enable quick access. The organizing unit can also organize information with low importance in a simplified manner to increase the visibility of the information. Furthermore, the organizing unit sets priorities for organizing according to the importance of the information and provides information efficiently. As a result, the organizing unit adjusts the level of detail of the organizing based on the importance of the information, thereby enabling quick access to important information.

[0040] When organizing information, the organizing unit can apply different organizing algorithms depending on the category of information. The organizing unit uses an algorithm for applying the organizing algorithm depending on the category of information. For example, the organizing unit sets criteria for classifying information categories. The organizing unit sets categories such as supply information, local situation information, and support content information. Furthermore, the organizing unit applies different organizing algorithms depending on each category. For example, the organizing unit organizes supply information based on inventory status and required quantity. The organizing unit can also organize local situation information based on geographical location information. Furthermore, the organizing unit organizes support content information based on priority and urgency. In this way, the organizing unit can efficiently organize information by applying the organizing algorithm depending on the category of information.

[0041] When organizing information, the organizing unit can determine the priority of organizing based on the time of submission of the information. The organizing unit uses an algorithm for evaluating the time of submission of the information. For example, the organizing unit sets criteria for recording the time of submission of the information. The organizing unit, for example, records the date and time of submission of the information and evaluates the time of submission. Furthermore, the organizing unit determines the priority of organizing the information based on the time of submission. For example, the organizing unit prioritizes organizing the most recent information, enabling a quick response. The organizing unit can also simply organize information that was submitted earlier, increasing visibility. Furthermore, the organizing unit sets the priority of information based on the time of submission and provides information efficiently. As a result, the organizing unit determines the priority of organizing based on the time of submission of the information, making it possible to quickly access the most recent information.

[0042] When organizing information, the organizing unit can adjust the order of organizing based on the relevance of the information. The organizing unit uses an algorithm for evaluating the relevance of the information. For example, the organizing unit sets evaluation criteria for evaluating the relevance of the information. The organizing unit evaluates the relevance of the information based on evaluation criteria such as the content of the information, the source of the information, and the impact of the information. Furthermore, the organizing unit adjusts the order of organizing the information based on the evaluation results. For example, the organizing unit prioritizes organizing highly relevant information to enable quick access. The organizing unit can also simplify organizing information with low relevance to increase the visibility of the information. Furthermore, the organizing unit sets the order of organizing based on the relevance of the information, providing efficient information provision. As a result, the organizing unit adjusts the order of organizing based on the relevance of the information, thereby enabling quick access to important information.

[0043] When responding, the response unit can adjust the level of detail of the response based on the importance of the information. The response unit uses an algorithm for evaluating the importance of the information. For example, the response unit sets evaluation criteria for evaluating the importance of the information. The response unit evaluates the importance of the information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the response unit adjusts the level of detail of the response based on the evaluation results. For example, the response unit provides a detailed response for information with high importance. The response unit can also provide a brief response for information with low importance. Furthermore, the response unit sets priority of responses according to the importance of the information, and provides information efficiently. As a result, the response unit adjusts the level of detail of the response based on the importance of the information, allowing important information to be provided quickly.

[0044] When responding, the response unit can apply different response algorithms depending on the category of information. The response unit uses an algorithm for applying a response algorithm depending on the category of information. For example, the response unit sets criteria for classifying information categories. The response unit sets categories such as supply information, local situation information, and support content information. Furthermore, the response unit applies different response algorithms depending on each category. For example, the response unit provides a response based on stock status and required quantity for supply information. The response unit can also provide a response based on geographical location information for local situation information. Furthermore, the response unit provides a response based on priority and urgency for support content information. In this way, the response unit can apply a response algorithm depending on the category of information, enabling efficient information provision.

[0045] When responding, the response unit can determine the priority of the response based on the time of submission of the information. The response unit uses an algorithm for evaluating the time of submission of the information. For example, the response unit sets criteria for recording the time of submission of the information. The response unit records, for example, the date and time of submission of the information and evaluates the time of submission. Furthermore, the response unit determines the priority of the response based on the time of submission. For example, the response unit provides a quick response for the latest information. The response unit can also provide a brief response for information that was submitted earlier. Furthermore, the response unit sets the priority of the response based on the time of submission and provides information efficiently. As a result, the response unit determines the priority of the response based on the time of submission of the information, so that the latest information is provided quickly.

[0046] When responding, the response unit can adjust the order of responses based on the relevance of the information. The response unit uses an algorithm for evaluating the relevance of the information. For example, the response unit sets evaluation criteria for evaluating the relevance of the information. The response unit evaluates the relevance of the information based on evaluation criteria such as the content of the information, the source of the information, and the impact of the information. Furthermore, the response unit adjusts the order of responses based on the evaluation results. For example, the response unit provides a quick response to highly relevant information. The response unit can also provide a brief response to less relevant information. Furthermore, the response unit sets the order of responses according to the relevance of the information, and provides information efficiently. As a result, the response unit adjusts the order of responses based on the relevance of the information, thereby allowing important information to be provided quickly.

[0047] The priority setting unit can adjust the priority based on the importance of information when setting the priority. The priority setting unit uses an algorithm for evaluating the importance of information. For example, the priority setting unit sets evaluation items for evaluating the importance of information. The priority setting unit evaluates the importance of information based on evaluation items such as the urgency, impact, and reliability of the information. Furthermore, the priority setting unit adjusts the priority based on the evaluation results. For example, the priority setting unit prioritizes information with high importance, enabling a quick response. The priority setting unit can also simplify information with low importance, increasing visibility. Furthermore, the priority setting unit sets the priority according to the importance of information, providing efficient information provision. As a result, the priority setting unit adjusts the priority based on the importance of information, enabling quick access to important information.

[0048] When setting priorities, the priority setting unit can determine the priorities based on the time of submission of information. The priority setting unit uses an algorithm for evaluating the time of submission of information. For example, the priority setting unit sets criteria for recording the time of submission of information. The priority setting unit records, for example, the date and time of submission of information and evaluates the time of submission. Furthermore, the priority setting unit determines the priorities based on the time of submission. For example, the priority setting unit prioritizes the latest information, enabling a quick response. The priority setting unit can also simplify information submitted earlier, increasing visibility. Furthermore, the priority setting unit sets priorities based on the time of submission, providing efficient information provision. As a result, the priority setting unit determines priorities based on the time of submission of information, enabling the latest information to be accessed quickly.

[0049] During update management, the update management unit can set update priorities based on the importance of information. The update management unit uses an algorithm for evaluating the importance of information. For example, the update management unit sets evaluation criteria for evaluating the importance of information. The update management unit evaluates the importance of information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the update management unit sets update priorities based on the evaluation results. For example, the update management unit prioritizes updates of information with high importance, enabling a prompt response. The update management unit can also simply update information with low importance, increasing visibility. Furthermore, the update management unit sets update priorities according to the importance of the information, providing information efficiently. As a result, the update management unit sets update priorities based on the importance of the information, allowing important information to be provided promptly.

[0050] During update management, the update management unit can determine the priority of updates based on the time when the information was submitted. The update management unit uses an algorithm for evaluating the time when the information was submitted. For example, the update management unit sets criteria for recording the time when the information was submitted. The update management unit records, for example, the date and time when the information was submitted and evaluates the time when the information was submitted. Furthermore, the update management unit determines the priority of updates based on the time when the information was submitted. For example, the update management unit prioritizes updating the latest information, enabling a quick response. The update management unit can also simply update information that was submitted earlier, increasing visibility. Furthermore, the update management unit sets the priority of updates based on the time when the information was submitted, and provides information efficiently. As a result, the update management unit determines the priority of updates based on the time when the information was submitted, allowing the latest information to be provided quickly.

[0051] When displaying the interface, the interface unit can select the optimal display method by referring to the past operation history of the field activity team. The interface unit uses an algorithm for analyzing the past operation history of the field activity team. For example, the interface unit proposes the optimal display method based on display methods used by the field activity team in the past. The interface unit, for example, analyzes past operation history data and identifies effective display means. The interface unit can also predict effective display timing from the past operation history of the field activity team. For example, the interface unit proposes the optimal timing for display based on past operation history data. Furthermore, the interface unit analyzes the past operation history of the field activity team and selects the most efficient display means. For example, the interface unit identifies a means that maximizes display efficiency based on past operation history data. In this way, the interface unit selects the optimal display method based on the past operation history, enabling efficient operation.

[0052] When displaying the interface, the interface unit can select the optimal display method by taking into account device information of the field activity team. The interface unit collects device information of the field activity team and uses an algorithm to select the optimal display method. For example, the interface unit identifies the type of device used by the field activity team. The interface unit collects information such as the device's screen size, resolution, and operation method. Furthermore, the interface unit selects the optimal display method based on the collected device information. For example, if the field activity team is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, if the field activity team is using a tablet, the interface unit can also provide a display method optimized for a large screen. Furthermore, if the field activity team is using a smartwatch, the interface unit provides a simple and highly visible display method. As a result, the interface unit provides the optimal display method by taking into account the device information, enabling efficient operation.

[0053] When providing multilingual support, the multilingual department can select the optimal language by referring to the field team's past language use history. The multilingual department uses an algorithm to analyze the field team's past language use history. For example, the multilingual department suggests the optimal language based on the languages ​​used by the field team in the past. For example, the multilingual department analyzes past language use history data to identify effective language support measures. The multilingual department can also predict effective timing for language support based on the field team's past language use history. For example, the multilingual department suggests the optimal timing for language support based on past language use history data. Furthermore, the multilingual department analyzes the field team's past language use history and selects the most efficient language support measures. For example, the multilingual department identifies measures that maximize the efficiency of language support based on past language use history data. In this way, the multilingual department can select the optimal language based on the past language use history, enabling efficient multilingual support.

[0054] The multilingual department can select the optimal language when providing multilingual support, taking into account the geographic location information of the field team. The multilingual department uses an algorithm to select a language taking into account the geographic location information of the field team. For example, the multilingual department proposes the optimal language based on the geographic location information of the field team. The multilingual department, for example, analyzes GPS data to identify the primary language in the area. The multilingual department can also predict effective language support taking into account the geographic location information of the field team. For example, the multilingual department uses a geographic information system (GIS) to identify the optimal means for language support. Furthermore, the multilingual department analyzes the geographic location information of the field team and selects the most efficient means for language support. For example, the multilingual department uses a geographic information system (GIS) to identify the means that maximizes the efficiency of language support. In this way, the multilingual department selects the optimal language by taking into account the geographic location information, enabling efficient multilingual support.

[0055] When implementing security measures, the security unit can set a security level based on the importance of information. The security unit uses an algorithm for evaluating the importance of information. For example, the security unit sets evaluation criteria for evaluating the importance of information. The security unit evaluates the importance of information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the security unit sets a security level based on the evaluation results. For example, the security unit provides enhanced security measures for information of high importance. The security unit can also provide standard security measures for information of low importance. Furthermore, the security unit sets a security level according to the importance of information to efficiently protect the information. In this way, the security unit sets a security level based on the importance of information, thereby quickly protecting important information.

[0056] When implementing security measures, the security unit can adjust the security level based on the time when the information was submitted. The security unit uses an algorithm to evaluate the time when the information was submitted. For example, the security unit sets criteria for recording the time when the information was submitted. The security unit records, for example, the date and time when the information was submitted and evaluates the time when the information was submitted. Furthermore, the security unit adjusts the security level based on the time when the information was submitted. For example, the security unit provides enhanced security measures for the most recent information. The security unit can also provide standard security measures for information that was submitted earlier. Furthermore, the security unit sets the security level based on the time when the information was submitted, thereby providing efficient information protection. As a result, the security unit adjusts the security level based on the time when the information was submitted, thereby quickly protecting the most recent information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The organizing unit includes a priority setting unit that sets the priority of information. The priority setting unit evaluates the importance of collected information and sets the priority. For example, the priority setting unit evaluates the importance of information based on evaluation items such as the urgency, impact, and reliability of the information. Furthermore, the priority setting unit sets the priority of information based on the evaluation results. For example, information with a high level of urgency is displayed preferentially to enable a quick response. Information with a high level of impact is displayed preferentially to prevent important information from being overlooked. Furthermore, information with a high level of reliability is displayed preferentially to prevent incorrect information from being provided. In this way, the priority setting unit sets the priority of information, thereby enabling quick access to important information.

[0059] The organizer includes an update management unit that manages the update frequency of the information. The update management unit manages the update frequency of the collected information. For example, the update management unit uses an algorithm to set the update timing of the information. The update management unit sets the update timing of the information based on evaluation items such as the importance of the information, the collection frequency, and the need for updates. Furthermore, the update management unit manages the update frequency of the information based on the evaluation results. For example, information with a high level of importance is frequently updated to ensure that the latest information is always provided. Furthermore, information with a high collection frequency is frequently updated to ensure the accuracy of the information. Furthermore, information with a high need for updating is frequently updated to maintain the usefulness of the information. In this way, the update management unit manages the update frequency of the information to always provide the latest information.

[0060] The response unit includes an interface unit that provides a user-friendly interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit visually organizes information in a dashboard format, allowing the user to easily access the information. The interface unit provides tabs for each information category, allowing the user to quickly access the information they need. The interface unit also displays important information in a prominent position based on the priority of the information, preventing the user from missing important information. Furthermore, the interface unit displays the update status of the information in real time, allowing the user to always be aware of the latest information. For example, the update date and time of the information is displayed, allowing the user to check the freshness of the information. The interface unit also provides an information search function, allowing the user to quickly search for the information they need. For example, the interface unit provides keyword search and filtering functions, allowing the user to easily find specific information. As a result, the interface unit provides a user-friendly interface, allowing intuitive operation.

[0061] The response unit includes a multilingual unit that provides multilingual support. The multilingual unit provides information in multiple languages. For example, the multilingual unit displays information in a language selected by the user. The multilingual unit supports multiple languages, such as English, Japanese, and French. The multilingual unit also uses an algorithm to improve the accuracy of information translation. For example, the multilingual unit automatically translates information using machine translation technology. The multilingual unit also has a function to check the translation results and correct them as necessary. For example, the multilingual unit displays the translation results so that the user can check the translation content. The multilingual unit also provides a feedback function to evaluate and improve the accuracy of the translation. For example, the multilingual unit collects feedback from users and improves the translation algorithm. As a result, the multilingual unit provides multilingual support, enabling smooth international support.

[0062] The acquisition unit can analyze the past activity history of the field activity team and select the optimal information collection method. The acquisition unit uses an algorithm to analyze the past activity history of the field activity team. For example, the acquisition unit proposes the optimal method based on the information collection methods used by the field activity team in the past. The acquisition unit analyzes past activity history data and identifies effective information collection means. The acquisition unit can also predict effective information collection timing from the past activity history of the field activity team. For example, the acquisition unit proposes the optimal timing for information collection based on past activity history data. Furthermore, the acquisition unit analyzes the past activity history of the field activity team and selects the most efficient information collection means. For example, the acquisition unit identifies a means that maximizes the efficiency of information collection based on past activity history data. As a result, the acquisition unit can select the optimal information collection method based on the past activity history, enabling efficient information collection.

[0063] The acquisition unit can filter information based on the local situation and environment when collecting information. The acquisition unit uses an algorithm to filter information taking into account the local situation and environment. For example, the acquisition unit selects an appropriate means of collecting information based on local weather information. The acquisition unit analyzes weather data and proposes the optimal means of collecting information. The acquisition unit can also limit the scope of information collection taking into account the local geographical conditions. For example, a geographic information system (GIS) is used to identify the scope of information collection. Furthermore, unnecessary information is filtered based on the local environmental conditions, and only important information is collected. For example, an environmental sensor is used to monitor the local situation and extract important information. As a result, the acquisition unit can collect only important information by filtering based on the local situation and environment.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The acquisition unit collects information from the field activity team. Information from the field activity team includes a list of supplies, the local situation, and the details of the support required. The acquisition unit automatically collects information entered by the field activity team. It can also collect local conditions in real time using sensors and cameras. For example, the acquisition unit can capture video of the local area with a camera and analyze the video data to understand the local situation. Furthermore, the acquisition unit can analyze the voice input of the field activity team and extract the necessary information. For example, it can use voice recognition technology to convert the utterances of the field activity team into text data, and then analyze the text data to extract the necessary information. Step 2: The organizer organizes the information collected by the acquirer. The organizer classifies the collected information by category and sets priorities. It also manages the frequency of information updates to ensure that the latest information is always provided. For example, it periodically updates the collected information and deletes old information. It also checks the accuracy of the information to prevent incorrect information from being provided. For example, it cross-checks the collected information to ensure there are no inconsistencies. Step 3: The Response Unit responds based on the information organized by the Organizer. The Response Unit automatically responds to inquiries from field teams. It also provides a user-friendly interface to make operation intuitive. For example, it visually organizes information in a dashboard format, allowing users to easily access the information. It also supports multiple languages ​​to ensure smooth international assistance. For example, it provides information in multiple languages ​​and displays it in the language selected by the user.

[0066] (Example 2) The information sharing support system according to an embodiment of the present invention supports information sharing between the control team and the field activity team during a disaster. This system listens to the requests of the control team and the field activity team, automatically responds to inquiries from the field activity team, and constantly organizes all information, ensuring that anyone can access it immediately. For example, the hearing function allows the field activity team to quickly collect the information they need. It can automatically collect not only lists of supplies but also the local situation and the assistance needed. Next, the automatic response function not only uses pre-defined information but also uses AI to learn new information in real time and generate appropriate responses. Furthermore, the information organization function prioritizes information and ensures that important information is quickly accessible. The frequency and accuracy of information updates are also important. Furthermore, the information generated by the app should be provided through a user-friendly interface. For example, visually organizing information in a dashboard format for intuitive operation is conceivable. It is also important that the app is multilingual. Because international assistance is often required during disasters, information should be available in multiple languages. Finally, security must also be considered. During a disaster, confidential information may be included, so it is important to encrypt the information and control access properly. This allows the information sharing support system to facilitate smooth information sharing between the control team and the field activity team, improving the efficiency of disaster response.

[0067] An information collaboration support system according to an embodiment includes an acquisition unit, a sorting unit, and a response unit. The acquisition unit collects information from the field activity team. The information from the field activity team includes, but is not limited to, a list of supplies, the current situation on the ground, and the details of necessary support. For example, the acquisition unit automatically collects information entered by the field activity team. The acquisition unit can also collect information about the current situation in real time using a sensor or a camera. For example, the acquisition unit may capture video of the local area with a camera and analyze the video data to understand the current situation. The acquisition unit can also analyze the voice input of the field activity team to extract necessary information. For example, the acquisition unit may convert the statements of the field activity team into text data using voice recognition technology and analyze the text data to extract necessary information. The sorting unit organizes the information collected by the acquisition unit. For example, the sorting unit may classify the collected information by category and set priorities. The sorting unit may also manage the update frequency of the information to ensure that the latest information is always provided. For example, the sorting unit may periodically update the collected information and delete old information. Furthermore, the organizing unit verifies the accuracy of the information to prevent incorrect information from being provided. For example, the organizing unit cross-checks the collected information to check for inconsistencies. The replying unit replies based on the information organized by the organizing unit. For example, the replying unit automatically replies to inquiries from field activity teams. The replying unit also provides a user-friendly interface to enable intuitive operation. For example, the replying unit visually organizes information in a dashboard format to allow users to easily access the information. Furthermore, the replying unit supports multiple languages ​​to ensure smooth international assistance. For example, the replying unit provides information in multiple languages ​​and displays the information in the language selected by the user. This enables the information collaboration support system according to the embodiment to efficiently collect, organize, and reply to information from field activity teams.

[0068] The organizing unit includes a priority setting unit that sets the priority of information. The priority setting unit evaluates the importance of collected information and sets the priority. For example, the priority setting unit uses an algorithm for evaluating the importance of information. The priority setting unit evaluates the importance of information based on evaluation items such as the urgency, impact, and reliability of the information. Furthermore, the priority setting unit sets the priority of information based on the evaluation results. For example, the priority setting unit prioritizes displaying information with a high level of urgency, enabling a quick response. Furthermore, the priority setting unit prioritizes displaying information with a high level of impact, preventing important information from being overlooked. Furthermore, the priority setting unit prioritizes displaying information with a high level of reliability, preventing erroneous information from being provided. In this way, the priority setting unit prioritizes information, thereby enabling quick access to important information.

[0069] The organizer includes an update management unit that manages the update frequency of the information. The update management unit manages the update frequency of the collected information. For example, the update management unit uses an algorithm to set the update timing of the information. The update management unit sets the update timing of the information based on evaluation items such as the importance of the information, the collection frequency, and the need for updates. Furthermore, the update management unit manages the update frequency of the information based on the evaluation results. For example, the update management unit frequently updates information with high importance so that the latest information is always provided. Furthermore, the update management unit frequently updates information with high collection frequency to ensure the accuracy of the information. Furthermore, the update management unit frequently updates information with a high need for updating to maintain the usefulness of the information. In this way, the update management unit manages the update frequency of the information so that the latest information is always provided.

[0070] The response unit includes an interface unit that provides a user-friendly interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit visually organizes information in a dashboard format, allowing the user to easily access the information. For example, the interface unit provides tabs for each information category, allowing the user to quickly access the information they need. The interface unit also displays important information in a prominent position based on the priority of the information, preventing the user from missing important information. Furthermore, the interface unit displays the update status of the information in real time, allowing the user to always be aware of the latest information. For example, the interface unit displays the update date and time of the information, allowing the user to check the freshness of the information. The interface unit also provides an information search function, allowing the user to quickly search for the information they need. For example, the interface unit provides keyword search and filtering functions, allowing the user to easily find specific information. As a result, the interface unit provides a user-friendly interface, allowing intuitive operation.

[0071] The response unit includes a multilingual unit that provides multilingual support. The multilingual unit provides information in multiple languages. For example, the multilingual unit displays information in a language selected by the user. The multilingual unit supports multiple languages, such as English, Japanese, and French. The multilingual unit also uses an algorithm to improve the accuracy of information translation. For example, the multilingual unit automatically translates information using machine translation technology. The multilingual unit also has a function to check the translation result and correct it as necessary. For example, the multilingual unit displays the translation result so that the user can check the translation content. The multilingual unit also provides a feedback function to evaluate and improve the accuracy of the translation. For example, the multilingual unit collects feedback from users and improves the translation algorithm. As a result, the multilingual unit provides multilingual support, thereby enabling smooth international support.

[0072] The response unit includes a security unit that ensures security. The security unit encrypts information and controls access to protect confidential information. For example, the security unit uses information encryption technology to safely transmit and receive information. The security unit uses encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman). The security unit also controls access to information to ensure that only authorized users can access the information. For example, the security unit uses user authentication technology to verify the identity of users. The security unit also has a function to record access logs of information and detect unauthorized access. For example, the security unit analyzes access logs and detects abnormal access patterns. The security unit also regularly backs up information to prevent data loss. For example, the security unit backs up information using cloud storage. In this way, the security unit ensures security and enables the protection of confidential information.

[0073] The acquisition unit can estimate the emotions of the field team and adjust the timing of information collection based on the estimated emotions. The acquisition unit uses an algorithm to estimate the emotions of the field team. For example, the acquisition unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the acquisition unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The acquisition unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the acquisition unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the acquisition unit adjusts the timing of information collection based on the estimated emotions. For example, if the field team is feeling stressed, the acquisition unit reduces the frequency of information collection to reduce the burden. Furthermore, if the field team is relaxed, the acquisition unit can increase the frequency of information collection to collect more detailed information. Furthermore, if the field team is facing an emergency, the acquisition unit immediately collects information, enabling a rapid response. As a result, the acquisition unit can reduce the burden on the field team by adjusting the timing of information collection based on the emotions.

[0074] The acquisition unit can analyze the past activity history of the field activity team and select the optimal information collection method. The acquisition unit uses an algorithm to analyze the past activity history of the field activity team. For example, the acquisition unit proposes an optimal method based on information collection methods used by the field activity team in the past. The acquisition unit, for example, analyzes past activity history data and identifies effective information collection means. The acquisition unit can also predict effective information collection timing from the past activity history of the field activity team. For example, the acquisition unit proposes the optimal timing for information collection based on past activity history data. Furthermore, the acquisition unit analyzes the past activity history of the field activity team and selects the most efficient information collection means. For example, the acquisition unit identifies a means that maximizes the efficiency of information collection based on the past activity history data. As a result, the acquisition unit selects the optimal information collection method based on the past activity history, thereby enabling efficient information collection.

[0075] The acquisition unit can filter information based on local conditions and environments when collecting information. The acquisition unit uses an algorithm for filtering information taking into account local conditions and environments. For example, the acquisition unit selects an appropriate means of collecting information based on local weather information. The acquisition unit can, for example, analyze weather data and propose an optimal means of collecting information. The acquisition unit can also limit the scope of information collection taking into account local geographical conditions. For example, the acquisition unit can identify the scope of information collection using a geographic information system (GIS). Furthermore, the acquisition unit can filter unnecessary information and collect only important information based on local environmental conditions. For example, the acquisition unit can monitor local conditions using an environmental sensor and extract important information. As a result, the acquisition unit can collect only important information by filtering based on local conditions and environments.

[0076] The acquisition unit can estimate the emotions of the field team and determine the priority of information to be collected based on the estimated emotions. The acquisition unit uses an algorithm for estimating the emotions of the field team. For example, the acquisition unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the acquisition unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The acquisition unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the acquisition unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the acquisition unit determines the priority of information to be collected based on the estimated emotions. For example, if the field team is nervous, the acquisition unit prioritizes collecting important information. Furthermore, if the field team is relaxed, the acquisition unit can collect detailed information. Furthermore, if the field team is tired, the acquisition unit collects the minimum necessary information. In this way, the acquisition unit can prioritize collecting important information by determining the priority of information based on emotions.

[0077] When collecting information, the acquisition unit can prioritize collecting highly relevant information by taking into account local geographical location information. The acquisition unit uses an algorithm for collecting information by taking into account the local geographical location information. For example, the acquisition unit prioritizes collecting information about areas that will be heavily affected by a disaster based on the local geographical location information. The acquisition unit, for example, analyzes GPS data to identify areas that will be heavily affected. The acquisition unit can also prioritize collecting information about evacuation routes by taking into account the local geographical location information. For example, the acquisition unit identifies evacuation routes using a geographic information system (GIS). Furthermore, the acquisition unit prioritizes collecting information about areas that need assistance based on the local geographical location information. For example, the acquisition unit identifies areas that need assistance using a geographical information system (GIS). In this way, the acquisition unit can prioritize collecting highly relevant information by taking into account the geographical location information.

[0078] When collecting information, the acquisition unit can analyze the social media activities of the field activity team and collect related information. The acquisition unit uses an algorithm to analyze the social media activities of the field activity team. For example, the acquisition unit analyzes the social media posts of the field activity team and collects information about the local situation. The acquisition unit, for example, analyzes social media posting data to understand the local situation. The acquisition unit can also identify the necessary support content and collect information from the social media activities of the field activity team. For example, the acquisition unit analyzes social media posting data to identify the necessary support content. Furthermore, the acquisition unit collects information according to local needs based on the social media activities of the field activity team. For example, the acquisition unit analyzes social media posting data to understand the local needs. In this way, the acquisition unit can efficiently collect information about the local situation by analyzing social media activity.

[0079] The organization unit can estimate the emotions of the field team and adjust the information organization method based on the estimated emotions. The organization unit uses an algorithm to estimate the emotions of the field team. For example, the organization unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the organization unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The organization unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the organization unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the organization unit adjusts the information organization method based on the estimated emotions. For example, if the field team is feeling stressed, the organization unit adopts a simple organization method to increase the visibility of the information. Furthermore, if the field team is relaxed, the organization unit can organize detailed information and provide comprehensive information. Furthermore, if the field team is facing an emergency, the organization unit prioritizes important information to enable a rapid response. In this way, the organization unit adjusts the information organization method based on emotions, increasing visibility and enabling a rapid response.

[0080] When organizing information, the organizing unit can adjust the level of detail of the organizing based on the importance of the information. The organizing unit uses an algorithm for evaluating the importance of the information. For example, the organizing unit sets evaluation criteria for evaluating the importance of the information. The organizing unit evaluates the importance of the information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the organizing unit adjusts the level of detail of the information organizing based on the evaluation results. For example, the organizing unit organizes information with high importance in detail to enable quick access. The organizing unit can also organize information with low importance in a simplified manner to increase the visibility of the information. Furthermore, the organizing unit sets priorities for organizing according to the importance of the information and provides information efficiently. As a result, the organizing unit adjusts the level of detail of the organizing based on the importance of the information, thereby enabling quick access to important information.

[0081] When organizing information, the organizing unit can apply different organizing algorithms depending on the category of information. The organizing unit uses an algorithm for applying the organizing algorithm depending on the category of information. For example, the organizing unit sets criteria for classifying information categories. The organizing unit sets categories such as supply information, local situation information, and support content information. Furthermore, the organizing unit applies different organizing algorithms depending on each category. For example, the organizing unit organizes supply information based on inventory status and required quantity. The organizing unit can also organize local situation information based on geographical location information. Furthermore, the organizing unit organizes support content information based on priority and urgency. In this way, the organizing unit can efficiently organize information by applying the organizing algorithm depending on the category of information.

[0082] The sorting unit can estimate the emotions of the field team and adjust the priority of information based on the estimated emotions. The sorting unit uses an algorithm to estimate the emotions of the field team. For example, the sorting unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the sorting unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The sorting unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the sorting unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the sorting unit adjusts the priority of information based on the estimated emotions. For example, if the field team is nervous, the sorting unit prioritizes important information. Also, if the field team is relaxed, the sorting unit can prioritize detailed information. Furthermore, if the field team is tired, the sorting unit prioritizes the minimum necessary information. In this way, the sorting unit adjusts the priority of information based on emotions, making important information quickly accessible.

[0083] When organizing information, the organizing unit can determine the priority of organizing based on the time of submission of the information. The organizing unit uses an algorithm for evaluating the time of submission of the information. For example, the organizing unit sets criteria for recording the time of submission of the information. The organizing unit, for example, records the date and time of submission of the information and evaluates the time of submission. Furthermore, the organizing unit determines the priority of organizing the information based on the time of submission. For example, the organizing unit prioritizes organizing the most recent information, enabling a quick response. The organizing unit can also simply organize information that was submitted earlier, increasing visibility. Furthermore, the organizing unit sets the priority of information based on the time of submission and provides information efficiently. As a result, the organizing unit determines the priority of organizing based on the time of submission of the information, making it possible to quickly access the most recent information.

[0084] When organizing information, the organizing unit can adjust the order of organizing based on the relevance of the information. The organizing unit uses an algorithm for evaluating the relevance of the information. For example, the organizing unit sets evaluation criteria for evaluating the relevance of the information. The organizing unit evaluates the relevance of the information based on evaluation criteria such as the content of the information, the source of the information, and the impact of the information. Furthermore, the organizing unit adjusts the order of organizing the information based on the evaluation results. For example, the organizing unit prioritizes organizing highly relevant information to enable quick access. The organizing unit can also simplify organizing information with low relevance to increase the visibility of the information. Furthermore, the organizing unit sets the order of organizing based on the relevance of the information, providing efficient information provision. As a result, the organizing unit adjusts the order of organizing based on the relevance of the information, thereby enabling quick access to important information.

[0085] The response unit can estimate the emotion of the field team and adjust the way a response is expressed based on the estimated emotion. The response unit uses an algorithm for estimating the emotion of the field team. For example, the response unit analyzes the facial expressions and voice of the field team to estimate the emotion. For example, the response unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotion. The response unit can also analyze the voice of the field team using voice analysis technology to estimate the emotion. For example, the response unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the response unit adjusts the way a response is expressed based on the estimated emotion. For example, if the field team is nervous, the response unit provides a simple, highly visible response. Furthermore, if the field team is relaxed, the response unit can provide a response that includes detailed information. Furthermore, if the field team is in a hurry, the response unit provides a response that gets to the point. In this way, the response unit can reduce the burden on the field team by adjusting the way a response is expressed based on the emotion.

[0086] When responding, the response unit can adjust the level of detail of the response based on the importance of the information. The response unit uses an algorithm for evaluating the importance of the information. For example, the response unit sets evaluation criteria for evaluating the importance of the information. The response unit evaluates the importance of the information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the response unit adjusts the level of detail of the response based on the evaluation results. For example, the response unit provides a detailed response for information with high importance. The response unit can also provide a brief response for information with low importance. Furthermore, the response unit sets priority of responses according to the importance of the information, and provides information efficiently. As a result, the response unit adjusts the level of detail of the response based on the importance of the information, allowing important information to be provided quickly.

[0087] When responding, the response unit can apply different response algorithms depending on the category of information. The response unit uses an algorithm for applying a response algorithm depending on the category of information. For example, the response unit sets criteria for classifying information categories. The response unit sets categories such as supply information, local situation information, and support content information. Furthermore, the response unit applies different response algorithms depending on each category. For example, the response unit provides a response based on stock status and required quantity for supply information. The response unit can also provide a response based on geographical location information for local situation information. Furthermore, the response unit provides a response based on priority and urgency for support content information. In this way, the response unit can apply a response algorithm depending on the category of information, enabling efficient information provision.

[0088] The response unit can estimate the emotion of the field team and adjust the length of the response based on the estimated emotion. The response unit uses an algorithm to estimate the emotion of the field team. For example, the response unit analyzes the facial expressions and voice of the field team to estimate the emotion. For example, the response unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotion. The response unit can also analyze the voice of the field team using voice analysis technology to estimate the emotion. For example, the response unit analyzes the tone and speed of the voice to estimate the emotion. Furthermore, the response unit adjusts the length of the response based on the estimated emotion. For example, if the field team is nervous, the response unit provides a short and to-the-point response. Also, if the field team is relaxed, the response unit can provide a longer response including detailed explanations. Furthermore, if the field team is in a hurry, the response unit provides a quick and concise response. In this way, the response unit can reduce the burden on the field team by adjusting the length of the response based on the emotion.

[0089] When responding, the response unit can determine the priority of the response based on the time of submission of the information. The response unit uses an algorithm for evaluating the time of submission of the information. For example, the response unit sets criteria for recording the time of submission of the information. The response unit records, for example, the date and time of submission of the information and evaluates the time of submission. Furthermore, the response unit determines the priority of the response based on the time of submission. For example, the response unit provides a quick response for the latest information. The response unit can also provide a brief response for information that was submitted earlier. Furthermore, the response unit sets the priority of the response based on the time of submission and provides information efficiently. As a result, the response unit determines the priority of the response based on the time of submission of the information, so that the latest information is provided quickly.

[0090] When responding, the response unit can adjust the order of responses based on the relevance of the information. The response unit uses an algorithm for evaluating the relevance of the information. For example, the response unit sets evaluation criteria for evaluating the relevance of the information. The response unit evaluates the relevance of the information based on evaluation criteria such as the content of the information, the source of the information, and the impact of the information. Furthermore, the response unit adjusts the order of responses based on the evaluation results. For example, the response unit provides a quick response to highly relevant information. The response unit can also provide a brief response to less relevant information. Furthermore, the response unit sets the order of responses according to the relevance of the information, and provides information efficiently. As a result, the response unit adjusts the order of responses based on the relevance of the information, thereby allowing important information to be provided quickly.

[0091] The priority setting unit can estimate the emotions of the field team and set a priority order for information based on the estimated emotions. The priority setting unit uses an algorithm for estimating the emotions of the field team. For example, the priority setting unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the priority setting unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The priority setting unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the priority setting unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the priority setting unit sets a priority order for information based on the estimated emotions. For example, if the field team is nervous, the priority setting unit can prioritize important information. Furthermore, if the field team is relaxed, the priority setting unit can prioritize detailed information. Furthermore, if the field team is tired, the priority setting unit can prioritize the minimum necessary information. In this way, the priority setting unit sets the priority order of information based on emotions, making it possible to quickly access important information.

[0092] The priority setting unit can adjust the priority based on the importance of information when setting the priority. The priority setting unit uses an algorithm for evaluating the importance of information. For example, the priority setting unit sets evaluation items for evaluating the importance of information. The priority setting unit evaluates the importance of information based on evaluation items such as the urgency, impact, and reliability of the information. Furthermore, the priority setting unit adjusts the priority based on the evaluation results. For example, the priority setting unit prioritizes information with high importance, enabling a quick response. The priority setting unit can also simplify information with low importance, increasing visibility. Furthermore, the priority setting unit sets the priority according to the importance of information, providing efficient information provision. As a result, the priority setting unit adjusts the priority based on the importance of information, enabling quick access to important information.

[0093] The priority setting unit can estimate the emotions of the field team and adjust the display method of the priorities based on the estimated emotions. The priority setting unit uses an algorithm for estimating the emotions of the field team. For example, the priority setting unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the priority setting unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The priority setting unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the priority setting unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the priority setting unit adjusts the display method of the priorities based on the estimated emotions. For example, the priority setting unit provides a simple, highly visible display method when the field team is tense. Furthermore, the priority setting unit can provide a display method including detailed information when the field team is relaxed. Furthermore, the priority setting unit displays the minimum necessary information when the field team is tired. In this way, the priority setting unit adjusts the display method of the priorities based on the emotions, thereby improving visibility and enabling a quick response.

[0094] When setting priorities, the priority setting unit can determine the priorities based on the time of submission of information. The priority setting unit uses an algorithm for evaluating the time of submission of information. For example, the priority setting unit sets criteria for recording the time of submission of information. The priority setting unit records, for example, the date and time of submission of information and evaluates the time of submission. Furthermore, the priority setting unit determines the priorities based on the time of submission. For example, the priority setting unit prioritizes the latest information, enabling a quick response. The priority setting unit can also simplify information submitted earlier, increasing visibility. Furthermore, the priority setting unit sets priorities based on the time of submission, providing efficient information provision. As a result, the priority setting unit determines priorities based on the time of submission of information, enabling the latest information to be accessed quickly.

[0095] The update management unit can estimate the emotions of the field team and adjust the information update frequency based on the estimated emotions. The update management unit uses an algorithm to estimate the emotions of the field team. For example, the update management unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the update management unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The update management unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the update management unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the update management unit adjusts the information update frequency based on the estimated emotions. For example, if the field team is feeling stressed, the update management unit reduces the update frequency to reduce the burden. Furthermore, if the field team is relaxed, the update management unit can increase the update frequency and provide more detailed information. Furthermore, if the field team is facing an emergency, the update management unit immediately updates information to enable a rapid response. This allows the update management department to adjust the frequency of information updates based on emotions, thereby reducing the burden on field teams.

[0096] During update management, the update management unit can set update priorities based on the importance of information. The update management unit uses an algorithm for evaluating the importance of information. For example, the update management unit sets evaluation criteria for evaluating the importance of information. The update management unit evaluates the importance of information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the update management unit sets update priorities based on the evaluation results. For example, the update management unit prioritizes updates of information with high importance, enabling a prompt response. The update management unit can also simply update information with low importance, increasing visibility. Furthermore, the update management unit sets update priorities according to the importance of the information, providing information efficiently. As a result, the update management unit sets update priorities based on the importance of the information, allowing important information to be provided promptly.

[0097] The update management unit can estimate the emotions of the field team and adjust the display method of the update information based on the estimated emotions. The update management unit uses an algorithm to estimate the emotions of the field team. For example, the update management unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the update management unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The update management unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the update management unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the update management unit adjusts the display method of the update information based on the estimated emotions. For example, if the field team is tense, the update management unit provides a simple, highly visible display method. Furthermore, if the field team is relaxed, the update management unit can provide a display method including detailed information. Furthermore, if the field team is tired, the update management unit displays the minimum necessary information. In this way, the update management unit adjusts the display method of the update information based on the emotions, thereby improving visibility and enabling a quick response.

[0098] During update management, the update management unit can determine the priority of updates based on the time when the information was submitted. The update management unit uses an algorithm for evaluating the time when the information was submitted. For example, the update management unit sets criteria for recording the time when the information was submitted. The update management unit records, for example, the date and time when the information was submitted and evaluates the time when the information was submitted. Furthermore, the update management unit determines the priority of updates based on the time when the information was submitted. For example, the update management unit prioritizes updating the latest information, enabling a quick response. The update management unit can also simply update information that was submitted earlier, increasing visibility. Furthermore, the update management unit sets the priority of updates based on the time when the information was submitted, and provides information efficiently. As a result, the update management unit determines the priority of updates based on the time when the information was submitted, allowing the latest information to be provided quickly.

[0099] The interface unit can estimate the emotions of the field team and adjust the display method of the interface based on the estimated emotions. The interface unit uses an algorithm to estimate the emotions of the field team. For example, the interface unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the interface unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The interface unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the interface unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the interface unit adjusts the display method of the interface based on the estimated emotions. For example, if the field team is nervous, the interface unit provides an interface with calm colors to reduce visual stress. Furthermore, if the field team is having fun, the interface unit can provide an interface with bright colors to make operation more enjoyable. Furthermore, if the field team is tired, the interface unit provides a simple, highly visible interface to make operation easier. In this way, the interface unit adjusts the display method of the interface based on the emotions, improving visibility and enabling quick responses.

[0100] When displaying the interface, the interface unit can select the optimal display method by referring to the past operation history of the field activity team. The interface unit uses an algorithm for analyzing the past operation history of the field activity team. For example, the interface unit proposes the optimal display method based on display methods used by the field activity team in the past. The interface unit, for example, analyzes past operation history data and identifies effective display means. The interface unit can also predict effective display timing from the past operation history of the field activity team. For example, the interface unit proposes the optimal timing for display based on past operation history data. Furthermore, the interface unit analyzes the past operation history of the field activity team and selects the most efficient display means. For example, the interface unit identifies a means that maximizes display efficiency based on past operation history data. In this way, the interface unit selects the optimal display method based on the past operation history, enabling efficient operation.

[0101] The interface unit can estimate the emotions of the field team and adjust the interface operation procedures based on the estimated emotions. The interface unit uses an algorithm for estimating the emotions of the field team. For example, the interface unit analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the interface unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The interface unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the interface unit analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the interface unit adjusts the interface operation procedures based on the estimated emotions. For example, the interface unit provides simple, highly visible operation procedures when the field team is tense. Furthermore, the interface unit can provide detailed operation procedures when the field team is relaxed. Furthermore, the interface unit provides the minimum necessary operation procedures when the field team is tired. In this way, the interface unit adjusts the interface operation procedures based on the emotions, thereby improving visibility and enabling a quick response.

[0102] When displaying the interface, the interface unit can select the optimal display method by taking into account device information of the field activity team. The interface unit collects device information of the field activity team and uses an algorithm to select the optimal display method. For example, the interface unit identifies the type of device used by the field activity team. The interface unit collects information such as the device's screen size, resolution, and operation method. Furthermore, the interface unit selects the optimal display method based on the collected device information. For example, if the field activity team is using a smartphone, the interface unit provides a display method that matches the screen size. Furthermore, if the field activity team is using a tablet, the interface unit can also provide a display method optimized for a large screen. Furthermore, if the field activity team is using a smartwatch, the interface unit provides a simple and highly visible display method. As a result, the interface unit provides the optimal display method by taking into account the device information, enabling efficient operation.

[0103] The multilingual unit can estimate the emotions of the field team and adjust its multilingual support methods based on the estimated emotions. The multilingual unit uses an algorithm to estimate the emotions of the field team. For example, the multilingual unit analyzes the facial expressions and voice of the field team to estimate their emotions. For example, the multilingual unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate their emotions. The multilingual unit can also analyze the voice of the field team using voice analysis technology to estimate their emotions. For example, the multilingual unit analyzes the tone and speed of the voice to estimate their emotions. Furthermore, the multilingual unit adjusts its multilingual support methods based on the estimated emotions. For example, if the field team is nervous, the multilingual unit provides simple, highly visible multilingual support. If the field team is relaxed, the multilingual unit can provide detailed multilingual support. Furthermore, if the field team is tired, the multilingual unit provides minimal multilingual support. This allows the multilingual department to adjust its multilingual support methods based on emotions, improving visibility and enabling quicker responses.

[0104] When providing multilingual support, the multilingual department can select the optimal language by referring to the field team's past language use history. The multilingual department uses an algorithm to analyze the field team's past language use history. For example, the multilingual department suggests the optimal language based on the languages ​​used by the field team in the past. For example, the multilingual department analyzes past language use history data to identify effective language support measures. The multilingual department can also predict effective timing for language support based on the field team's past language use history. For example, the multilingual department suggests the optimal timing for language support based on past language use history data. Furthermore, the multilingual department analyzes the field team's past language use history and selects the most efficient language support measures. For example, the multilingual department identifies measures that maximize the efficiency of language support based on past language use history data. In this way, the multilingual department can select the optimal language based on the past language use history, enabling efficient multilingual support.

[0105] The multilingual department can estimate the emotions of the field team and determine the priority of multilingual support based on the estimated emotions. The multilingual department uses an algorithm to estimate the emotions of the field team. For example, the multilingual department analyzes the facial expressions and voice of the field team to estimate the emotions. For example, the multilingual department uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The multilingual department can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the multilingual department analyzes the tone and speed of the voice to estimate the emotions. Furthermore, the multilingual department determines the priority of multilingual support based on the estimated emotions. For example, if the field team is nervous, the multilingual department can prioritize providing important information in multilingual form. Furthermore, if the field team is relaxed, the multilingual department can provide detailed information in multilingual form. Furthermore, if the field team is tired, the multilingual department can provide the minimum necessary information in multilingual form. In this way, the multilingual department determines the priority of multilingual support based on emotions, thereby ensuring that important information is provided quickly.

[0106] The multilingual department can select the optimal language when providing multilingual support, taking into account the geographic location information of the field team. The multilingual department uses an algorithm to select a language taking into account the geographic location information of the field team. For example, the multilingual department proposes the optimal language based on the geographic location information of the field team. The multilingual department, for example, analyzes GPS data to identify the primary language in the area. The multilingual department can also predict effective language support taking into account the geographic location information of the field team. For example, the multilingual department uses a geographic information system (GIS) to identify the optimal means for language support. Furthermore, the multilingual department analyzes the geographic location information of the field team and selects the most efficient means for language support. For example, the multilingual department uses a geographic information system (GIS) to identify the means that maximizes the efficiency of language support. In this way, the multilingual department selects the optimal language by taking into account the geographic location information, enabling efficient multilingual support.

[0107] The security department can estimate the emotions of the field team and adjust security measures based on the estimated emotions. The security department uses an algorithm to estimate the emotions of the field team. For example, the security department analyzes the facial expressions and voice of the field team to estimate emotions. For example, the security department uses facial recognition technology to analyze the facial expressions of the field team and estimate emotions. The security department can also use voice analysis technology to analyze the voice of the field team and estimate emotions. For example, the security department analyzes the tone and speed of the voice to estimate emotions. Furthermore, the security department adjusts security measures based on the estimated emotions. For example, the security department can provide enhanced security measures if the field team is tense. Furthermore, the security department can provide standard security measures if the field team is relaxed. Furthermore, the security department can provide simplified security measures if the field team is tired. In this way, the security department can adjust security measures based on emotions, thereby increasing visibility and enabling faster response.

[0108] When implementing security measures, the security unit can set a security level based on the importance of information. The security unit uses an algorithm for evaluating the importance of information. For example, the security unit sets evaluation criteria for evaluating the importance of information. The security unit evaluates the importance of information based on evaluation criteria such as the urgency, impact, and reliability of the information. Furthermore, the security unit sets a security level based on the evaluation results. For example, the security unit provides enhanced security measures for information of high importance. The security unit can also provide standard security measures for information of low importance. Furthermore, the security unit sets a security level according to the importance of information to efficiently protect the information. In this way, the security unit sets a security level based on the importance of information, thereby quickly protecting important information.

[0109] The security department can estimate the emotions of the field team and prioritize security measures based on the estimated emotions. The security department uses an algorithm to estimate the emotions of the field team. For example, the security department analyzes the facial expressions and voice of the field team to estimate emotions. For example, the security department uses facial expression recognition technology to analyze the facial expressions of the field team and estimate emotions. The security department can also analyze the voice of the field team using voice analysis technology to estimate emotions. For example, the security department analyzes the tone and speed of the voice to estimate emotions. Furthermore, the security department prioritizes security measures based on the estimated emotions. For example, if the field team is nervous, the security department prioritizes protecting important information. Also, if the field team is relaxed, the security department can protect detailed information. Furthermore, if the field team is tired, the security department protects the minimum necessary information. In this way, the security department prioritizes security measures based on emotions, thereby quickly protecting important information.

[0110] When implementing security measures, the security unit can adjust the security level based on the time when the information was submitted. The security unit uses an algorithm to evaluate the time when the information was submitted. For example, the security unit sets criteria for recording the time when the information was submitted. The security unit records, for example, the date and time when the information was submitted and evaluates the time when the information was submitted. Furthermore, the security unit adjusts the security level based on the time when the information was submitted. For example, the security unit provides enhanced security measures for the most recent information. The security unit can also provide standard security measures for information that was submitted earlier. Furthermore, the security unit sets the security level based on the time when the information was submitted, thereby providing efficient information protection. As a result, the security unit adjusts the security level based on the time when the information was submitted, thereby quickly protecting the most recent information. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, organization unit, and response unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit collects information about the local situation using the camera 42 and microphone 38B of the smart device 14, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The organization unit organizes and prioritizes the information collected by the specific processing unit 290 of the data processing device 12. The response unit provides a user-friendly interface via the control unit 46A of the smart device 14, and visually organizes and displays information. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, organization unit, and response unit described above is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit collects information about the local situation using the camera 42 and microphone 238 of the smart glasses 214, which is analyzed by the specific processing unit 290 of the data processing device 12. The organization unit organizes and prioritizes the information collected by the specific processing unit 290 of the data processing device 12. The response unit provides a user-friendly interface via the control unit 46A of the smart glasses 214, and visually organizes and displays information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, organization unit, and response unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit collects information about the local situation using the camera 42 and microphone 238 of the headset type terminal 314, and the collected information is analyzed by the specific processing unit 290 of the data processing device 12. The organization unit organizes and prioritizes the information collected by the specific processing unit 290 of the data processing device 12. The response unit provides a user-friendly interface via the control unit 46A of the headset type terminal 314, and visually organizes and displays information. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, organization unit, and response unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit collects information about the local situation using the camera 42 and microphone 238 of the robot 414, which is analyzed by the specific processing unit 290 of the data processing device 12. The organization unit organizes and prioritizes the information collected by the specific processing unit 290 of the data processing device 12. The response unit provides a user-friendly interface via the control unit 46A of the robot 414, and visually organizes and displays information.

[0111] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0112] The acquisition unit can estimate the emotions of the field team and adjust the timing of information collection based on the estimated emotions. For example, the acquisition unit analyzes the facial expressions and voice of the field team to estimate the emotions. The acquisition unit can analyze the facial expressions of the field team using facial expression recognition technology to estimate the emotions. The acquisition unit can also analyze the voice of the field team using voice analysis technology to estimate the emotions. For example, the tone and speed of the voice can be analyzed to estimate the emotions. Furthermore, the acquisition unit adjusts the timing of information collection based on the estimated emotions. For example, if the field team is feeling stressed, the frequency of information collection can be reduced to reduce the burden. Furthermore, if the field team is relaxed, the frequency of information collection can be increased to collect more detailed information. Furthermore, if the field team is facing an emergency, information can be collected immediately, enabling a rapid response. In this way, the acquisition unit can reduce the burden on the field team by adjusting the timing of information collection based on the emotions.

[0113] The organizing unit includes a priority setting unit that sets the priority of information. The priority setting unit evaluates the importance of collected information and sets the priority. For example, the priority setting unit evaluates the importance of information based on evaluation items such as the urgency, impact, and reliability of the information. Furthermore, the priority setting unit sets the priority of information based on the evaluation results. For example, information with a high level of urgency is displayed preferentially to enable a quick response. Information with a high level of impact is displayed preferentially to prevent important information from being overlooked. Furthermore, information with a high level of reliability is displayed preferentially to prevent incorrect information from being provided. In this way, the priority setting unit sets the priority of information, thereby enabling quick access to important information.

[0114] The organizer includes an update management unit that manages the update frequency of the information. The update management unit manages the update frequency of the collected information. For example, the update management unit uses an algorithm to set the update timing of the information. The update management unit sets the update timing of the information based on evaluation items such as the importance of the information, the collection frequency, and the need for updates. Furthermore, the update management unit manages the update frequency of the information based on the evaluation results. For example, information with a high level of importance is frequently updated to ensure that the latest information is always provided. Furthermore, information with a high collection frequency is frequently updated to ensure the accuracy of the information. Furthermore, information with a high need for updating is frequently updated to maintain the usefulness of the information. In this way, the update management unit manages the update frequency of the information to always provide the latest information.

[0115] The response unit includes an interface unit that provides a user-friendly interface. The interface unit provides an interface that can be intuitively operated by the user. For example, the interface unit visually organizes information in a dashboard format, allowing the user to easily access the information. The interface unit provides tabs for each information category, allowing the user to quickly access the information they need. The interface unit also displays important information in a prominent position based on the priority of the information, preventing the user from missing important information. Furthermore, the interface unit displays the update status of the information in real time, allowing the user to always be aware of the latest information. For example, the update date and time of the information is displayed, allowing the user to check the freshness of the information. The interface unit also provides an information search function, allowing the user to quickly search for the information they need. For example, the interface unit provides keyword search and filtering functions, allowing the user to easily find specific information. As a result, the interface unit provides a user-friendly interface, allowing intuitive operation.

[0116] The response unit includes a multilingual unit that provides multilingual support. The multilingual unit provides information in multiple languages. For example, the multilingual unit displays information in a language selected by the user. The multilingual unit supports multiple languages, such as English, Japanese, and French. The multilingual unit also uses an algorithm to improve the accuracy of information translation. For example, the multilingual unit automatically translates information using machine translation technology. The multilingual unit also has a function to check the translation results and correct them as necessary. For example, the multilingual unit displays the translation results so that the user can check the translation content. The multilingual unit also provides a feedback function to evaluate and improve the accuracy of the translation. For example, the multilingual unit collects feedback from users and improves the translation algorithm. As a result, the multilingual unit provides multilingual support, enabling smooth international support.

[0117] The acquisition unit can analyze the past activity history of the field activity team and select the optimal information collection method. The acquisition unit uses an algorithm to analyze the past activity history of the field activity team. For example, the acquisition unit proposes the optimal method based on the information collection methods used by the field activity team in the past. The acquisition unit analyzes past activity history data and identifies effective information collection means. The acquisition unit can also predict effective information collection timing from the past activity history of the field activity team. For example, the acquisition unit proposes the optimal timing for information collection based on past activity history data. Furthermore, the acquisition unit analyzes the past activity history of the field activity team and selects the most efficient information collection means. For example, the acquisition unit identifies a means that maximizes the efficiency of information collection based on past activity history data. As a result, the acquisition unit can select the optimal information collection method based on the past activity history, enabling efficient information collection.

[0118] The acquisition unit can filter information based on the local situation and environment when collecting information. The acquisition unit uses an algorithm to filter information taking into account the local situation and environment. For example, the acquisition unit selects an appropriate means of collecting information based on local weather information. The acquisition unit analyzes weather data and proposes the optimal means of collecting information. The acquisition unit can also limit the scope of information collection taking into account the local geographical conditions. For example, a geographic information system (GIS) is used to identify the scope of information collection. Furthermore, unnecessary information is filtered based on the local environmental conditions, and only important information is collected. For example, an environmental sensor is used to monitor the local situation and extract important information. As a result, the acquisition unit can collect only important information by filtering based on the local situation and environment.

[0119] The acquisition unit can estimate the emotions of the field team and determine the priority of information to be collected based on the estimated emotions. The acquisition unit uses an algorithm for estimating the emotions of the field team. For example, the acquisition unit analyzes the facial expressions and voice of the field team to estimate the emotions. The acquisition unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate the emotions. The acquisition unit can also use voice analysis technology to analyze the voice of the field team and estimate the emotions. For example, the tone and speed of the voice can be analyzed to estimate the emotions. Furthermore, the acquisition unit determines the priority of information to be collected based on the estimated emotions. For example, if the field team is nervous, important information can be collected with priority. Also, if the field team is relaxed, detailed information can be collected. Furthermore, if the field team is tired, the minimum necessary information can be collected. In this way, the acquisition unit can prioritize information collection based on emotions, thereby allowing important information to be collected with priority.

[0120] The sorting unit can estimate the emotions of the field team and adjust the information sorting method based on the estimated emotions. The sorting unit uses an algorithm to estimate the emotions of the field team. For example, the sorting unit analyzes the facial expressions and voice of the field team to estimate their emotions. The sorting unit can also use facial expression recognition technology to analyze the facial expressions of the field team and estimate their emotions. The sorting unit can also use voice analysis technology to analyze the voice of the field team and estimate their emotions. For example, the tone and speed of the voice can be analyzed to estimate emotions. Furthermore, the sorting unit adjusts the information sorting method based on the estimated emotions. For example, if the field team is feeling stressed, a simple sorting method can be used to increase the visibility of the information. Alternatively, if the field team is relaxed, detailed information can be sorted to provide comprehensive information. Furthermore, if the field team is facing an emergency, important information can be prioritized and sorted to enable a rapid response. In this way, the sorting unit can adjust the information sorting method based on emotions, thereby increasing visibility and enabling a rapid response.

[0121] The response unit can estimate the emotions of the field team and adjust the way a response is expressed based on the estimated emotions. The response unit uses an algorithm for estimating the emotions of the field team. For example, the response unit analyzes the facial expressions and voice of the field team to estimate emotions. The response unit uses facial expression recognition technology to analyze the facial expressions of the field team and estimate emotions. The response unit can also use voice analysis technology to analyze the voice of the field team and estimate emotions. For example, the response unit analyzes the tone and speed of the voice to estimate emotions. Furthermore, the response unit adjusts the way a response is expressed based on the estimated emotions. For example, if the field team is nervous, the response unit provides a simple, highly visible response. If the field team is relaxed, the response unit can provide a response that includes detailed information. Furthermore, if the field team is in a hurry, the response unit provides a response that gets to the point. In this way, the response unit can reduce the burden on the field team by adjusting the way a response is expressed based on emotions.

[0122] The processing flow of the second embodiment will be briefly explained below.

[0123] Step 1: The acquisition unit collects information from the field activity team. Information from the field activity team includes a list of supplies, the local situation, and the details of the support required. The acquisition unit automatically collects information entered by the field activity team. It can also collect local conditions in real time using sensors and cameras. For example, the acquisition unit can capture video of the local area with a camera and analyze the video data to understand the local situation. Furthermore, the acquisition unit can analyze the voice input of the field activity team and extract the necessary information. For example, it can use voice recognition technology to convert the utterances of the field activity team into text data, and then analyze the text data to extract the necessary information. Step 2: The organizer organizes the information collected by the acquirer. The organizer classifies the collected information by category and sets priorities. It also manages the frequency of information updates to ensure that the latest information is always provided. For example, it periodically updates the collected information and deletes old information. It also checks the accuracy of the information to prevent incorrect information from being provided. For example, it cross-checks the collected information to ensure there are no inconsistencies. Step 3: The Response Unit responds based on the information organized by the Organizer. The Response Unit automatically responds to inquiries from field teams. It also provides a user-friendly interface to make operation intuitive. For example, it visually organizes information in a dashboard format, allowing users to easily access the information. It also supports multiple languages ​​to ensure smooth international assistance. For example, it provides information in multiple languages ​​and displays it in the language selected by the user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0126] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0129] 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.

[0130] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0134] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0135] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0145] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0152] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0153] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0154] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0155] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0161] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0164] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0166] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0167] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0168] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0169] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0170] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0171] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0172] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0174] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0176] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0177] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0179] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0180] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0181] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0185] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0186] 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.

[0187] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0188] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0189] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0190] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0192] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0193] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0194] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Acquisition Department collects information from field teams, and an organizing unit that organizes the information collected by the acquiring unit; a replying unit that makes a reply based on the information organized by the organizing unit; Equipped with A system characterized by:

2. The organizing unit Equipped with a priority setting unit that sets the priority of information 2. The system of claim 1.

3. The organizing unit Equipped with an update management unit that manages the frequency of information updates 2. The system of claim 1.

4. The reply unit: Equipped with an interface section that provides a user-friendly interface 2. The system of claim 1.

5. The reply unit: Equipped with a multilingual department that handles multiple languages 2. The system of claim 1.

6. The reply unit: Equipped with a security department to ensure security 2. The system of claim 1.

7. The acquisition unit Estimate the emotions of field teams and adjust the timing of information gathering based on those emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the past activity history of the field team and select the most appropriate information gathering method 2. The system of claim 1.

Images