system

Abstract

The system according to the embodiment aims to automate in-house data, convert it into various media, and transmit it, thereby enhancing security. [Solution] A system according to an embodiment includes a collection unit, an automation unit, a conversion unit, a transmission unit, and a security unit. The collection unit collects internal company data. The automation unit automates the data collected by the collection unit. The conversion unit converts the data automated by the automation unit to suit each medium. The transmission unit transmits the data converted by the conversion unit. The security unit strengthens the security of the data transmitted by the transmission 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] Conventional technologies do not adequately ensure security when automating in-house data, converting it into various media, and transmitting it, and there is room for improvement.

[0005] The system according to the embodiment aims to automate in-house data, convert it into various media, and transmit it, thereby enhancing security. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an automation unit, a conversion unit, a transmission unit, and a security unit. The collection unit collects internal company data. The automation unit automates the data collected by the collection unit. The conversion unit converts the data automated by the automation unit to suit each medium. The transmission unit transmits the data converted by the conversion unit. The security unit strengthens the security of the data transmitted by the transmission unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate in-house data and convert and transmit it to various media, thereby enhancing security. [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 system according to an embodiment of the present invention is a solution that connects internal systems using iPaaS. This system collects internal data, automates it using iPaaS, makes original proposals tailored to each medium, and strengthens security. This allows the system to efficiently aggregate and automate internal data, reducing the burden on employees. Furthermore, strengthened security reduces the risk of internal information leaks. Furthermore, making original proposals tailored to each medium enables more effective information sharing.

[0029] The system according to the embodiment includes a collection unit, an automation unit, a conversion unit, a transmission unit, and a security unit. The collection unit collects internal company data. For example, the collection unit can collect internal company data using SQL. The collection unit can also collect data manually. The automation unit automates the data collected by the collection unit. For example, the automation unit automatically summarizes the data using iPaaS and transmits it to a medium such as LINE (registered trademark). The conversion unit converts the data automated by the automation unit to suit each medium. For example, the conversion unit can convert the data format to suit each medium. The transmission unit transmits the data converted by the conversion unit. For example, the transmission unit can transmit the converted data to each medium. The security unit enhances the security of the data transmitted by the transmission unit. For example, the security unit performs data encryption and access control. This allows the system according to the embodiment to efficiently collect, automate, convert, transmit, and enhance the security of internal company data.

[0030] The collection unit can collect in-house data using SQL or manually. For example, the collection unit collects in-house data using SQL. For example, the collection unit can extract data from a database using a specific query. The collection unit can also collect data manually. For example, the collection unit can provide a form for employees to manually enter data. This allows for efficient collection of in-house data.

[0031] The automation unit can automatically summarize data using iPaaS and transmit it to at least one medium within LINE. The automation unit can, for example, automatically summarize data using iPaaS. For example, the automation unit can generate a summary of the data using a generation AI. The automation unit can also transmit the summarized data to at least one medium within LINE. For example, the automation unit can transmit the summarized data using LINE's API. This automates and efficiently performs the summarization and transmission of data.

[0032] The security unit can perform data encryption or access control. The security unit, for example, encrypts data. For example, the security unit can encrypt data using AES (Advanced Encryption Standard). The security unit can also perform access control. For example, the security unit can allow only specific users to access data. This strengthens the security of the data.

[0033] The conversion unit can convert the data format to suit each medium. The conversion unit performs data format conversion, for example. For example, the conversion unit can convert data in CSV format to JSON format. The conversion unit can also convert data in XML format to HTML format. This allows for efficient data format conversion.

[0034] The transmitting unit can transmit the converted data to each medium. The transmitting unit transmits, for example, the converted data to each medium. For example, the transmitting unit can transmit the data using email. The transmitting unit can also transmit the data using FTP (File Transfer Protocol). This allows for efficient data transmission.

[0035] The system includes an interface unit that provides a user-friendly interface. The interface unit provides, for example, a user-friendly interface. For example, the interface unit can provide a graphical user interface (GUI) that allows intuitive operation. The interface unit can also support voice input and voice output. This allows the user to easily operate the system.

[0036] The collection unit can analyze the frequency of data collection from each department within the company and set an optimal collection schedule. The collection unit, for example, analyzes the frequency of data collection from each department within the company. For example, the collection unit can analyze the data generation frequency of each department and set the optimal collection timing. The collection unit can also adjust the timing of data collection taking into account the business schedule of each department. This makes it possible to set an optimal collection schedule.

[0037] When collecting data, the collection unit can determine the collection priority based on the importance of the data. The collection unit determines the collection priority based on, for example, the importance of the data. For example, the collection unit can collect data with high importance first and process it quickly. The collection unit can also postpone data with low importance, allowing for efficient use of resources. In this way, the collection priority can be determined based on the importance of the data.

[0038] When collecting data, the collection unit can select the optimal collection means depending on the format of the data. For example, the collection unit can collect text data using natural language processing technology. The collection unit can also collect numerical data using database queries. Furthermore, the collection unit can collect image data using image recognition technology. This makes it possible to select the optimal collection means depending on the format of the data.

[0039] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical distribution within the company. For example, the collection unit can prioritize collecting highly relevant data by taking into account the geographical distribution within the company. For example, the collection unit can prioritize collecting nearby data by taking into account the geographical location of each department. The collection unit can also prioritize collecting data that is highly geographically relevant, thereby efficiently collecting data. This allows highly relevant data to be prioritized by taking into account the geographical distribution.

[0040] The collection unit can analyze internal social media activities and collect related data when collecting data. The collection unit, for example, analyzes internal social media activities and collects related data. For example, the collection unit can analyze the content of internal social media posts and collect related data. The collection unit can also set the optimal collection timing in consideration of the frequency of internal social media activities. This makes it possible to analyze social media activities and collect related data.

[0041] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit customizes the collection method by reflecting, for example, past feedback. For example, the collection unit can analyze past data collection results and optimize the collection method. The collection unit can also improve the collection method by reflecting feedback from users. This allows the collection method to be customized by reflecting past feedback.

[0042] The automation unit can adjust the level of automation detail based on the importance of the data during automation. The automation unit can adjust the level of automation detail based on, for example, the importance of the data. For example, the automation unit can automate highly important data in detail to improve accuracy. The automation unit can also simplify and automate less important data to use resources efficiently. This allows the level of automation detail to be adjusted based on the importance of the data.

[0043] During automation, the automation unit can apply different automation algorithms depending on the category of data. For example, the automation unit can apply different automation algorithms depending on the category of data. For example, the automation unit can apply a natural language processing algorithm to text data. The automation unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the automation unit can apply an image recognition algorithm to image data. In this way, different automation algorithms can be applied depending on the category of data.

[0044] During automation, the automation unit can improve the accuracy of automation by referring to past automation results. For example, the automation unit can improve the accuracy of automation by referring to past automation results. For example, the automation unit can analyze past automation results and improve the accuracy of the algorithm. The automation unit can also reflect feedback obtained from past automation results and improve the process. In this way, the accuracy of automation can be improved by referring to past automation results.

[0045] During automation, the automation unit can determine the priority of automation based on the time of data submission. The automation unit determines the priority of automation based on, for example, the time of data submission. For example, the automation unit can prioritize automation of data with an upcoming submission deadline. The automation unit can also postpone data with a distant submission deadline, allowing for efficient use of resources. This makes it possible to determine the priority of automation based on the time of data submission.

[0046] The automation unit can adjust the order of automation based on the relevance of data during automation. The automation unit adjusts the order of automation based on, for example, the relevance of data. For example, the automation unit can prioritize automation of highly relevant data and process it efficiently. The automation unit can also postpone data with low relevance and use resources efficiently. This makes it possible to adjust the order of automation based on the relevance of data.

[0047] During automation, the automation unit can adjust the automation process according to the user's level of expertise. The automation unit adjusts the automation process according to, for example, the user's level of expertise. For example, the automation unit can provide a detailed automation process to a user with high expertise. The automation unit can also provide a simplified automation process to a user with low expertise. In this way, the automation process can be adjusted according to the user's level of expertise.

[0048] The conversion unit can adjust the level of detail of the conversion based on the importance of the data when converting data. For example, the conversion unit can convert data with high importance in detail to increase accuracy. The conversion unit can also simplify and convert data with low importance to use resources efficiently. This allows the level of detail of the conversion to be adjusted based on the importance of the data.

[0049] During data conversion, the conversion unit can apply different conversion algorithms depending on the category of data. For example, the conversion unit can apply different conversion algorithms depending on the category of data. For example, the conversion unit can apply a natural language processing algorithm to text data. The conversion unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the conversion unit can also apply an image recognition algorithm to image data. This makes it possible to apply different conversion algorithms depending on the category of data.

[0050] When converting data, the conversion unit can improve the accuracy of the conversion by referring to past conversion results. For example, the conversion unit can analyze past conversion results to improve the accuracy of the algorithm. The conversion unit can also reflect feedback obtained from past conversion results and improve the process. In this way, the accuracy of the conversion can be improved by referring to past conversion results.

[0051] When converting data, the conversion unit can determine the priority of conversion based on the time of data submission. The conversion unit determines the priority of conversion based on, for example, the time of data submission. For example, the conversion unit can preferentially convert data with an upcoming submission deadline. The conversion unit can also postpone data with a distant submission deadline, allowing for efficient use of resources. This allows the priority of conversion to be determined based on the time of data submission.

[0052] The conversion unit can adjust the order of conversion based on the relevance of the data when converting data. The conversion unit adjusts the order of conversion based on, for example, the relevance of the data. For example, the conversion unit can convert highly relevant data preferentially and process it efficiently. The conversion unit can also postpone data with low relevance and use resources efficiently. This allows the order of conversion to be adjusted based on the relevance of the data.

[0053] During data conversion, the conversion unit can adjust the conversion process according to the user's level of expertise. The conversion unit adjusts the conversion process according to the user's level of expertise, for example. For example, the conversion unit can provide a detailed conversion process to a user with high expertise. The conversion unit can also provide a simplified conversion process to a user with low expertise. In this way, the conversion process can be adjusted according to the user's level of expertise.

[0054] The transmitting unit can adjust the level of detail of the transmission based on the importance of the data when transmitting data. The transmitting unit adjusts the level of detail of the transmission based on, for example, the importance of the data. For example, the transmitting unit can transmit data of high importance in detail to increase accuracy. The transmitting unit can also transmit data of low importance in a simplified form to efficiently use resources. In this way, the level of detail of the transmission can be adjusted based on the importance of the data.

[0055] The transmitting unit can apply different transmission algorithms depending on the category of data when transmitting data. For example, the transmitting unit can apply different transmission algorithms depending on the category of data. For example, the transmitting unit can apply a natural language processing algorithm to text data. The transmitting unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the transmitting unit can also apply an image recognition algorithm to image data. In this way, different transmission algorithms can be applied depending on the category of data.

[0056] When transmitting data, the transmitting unit can improve the accuracy of transmission by referring to past transmission results. For example, the transmitting unit can improve the accuracy of transmission by referring to past transmission results. For example, the transmitting unit can analyze past transmission results and improve the accuracy of the algorithm. The transmitting unit can also reflect feedback obtained from past transmission results and improve the process. In this way, the accuracy of transmission can be improved by referring to past transmission results.

[0057] When transmitting data, the transmitting unit can determine the priority of transmission based on the time of data submission. The transmitting unit determines the priority of transmission based on, for example, the time of data submission. For example, the transmitting unit can preferentially transmit data with an upcoming submission deadline. The transmitting unit can also postpone data with a distant submission deadline, thereby efficiently using resources. In this way, the priority of transmission can be determined based on the time of data submission.

[0058] The transmitting unit can adjust the order of transmission based on the relevance of the data when transmitting the data. The transmitting unit adjusts the order of transmission based on, for example, the relevance of the data. For example, the transmitting unit can transmit highly relevant data preferentially and process it efficiently. The transmitting unit can also postpone data with low relevance and use resources efficiently. This allows the order of transmission to be adjusted based on the relevance of the data.

[0059] When transmitting data, the transmitting unit can adjust the transmission process according to the user's level of expertise. The transmitting unit, for example, adjusts the transmission process according to the user's level of expertise. For example, the transmitting unit can provide a detailed transmission process to a user with high expertise. The transmitting unit can also provide a simplified transmission process to a user with low expertise. In this way, the transmission process can be adjusted according to the user's level of expertise.

[0060] The security unit can adjust the level of security detail based on the importance of the data when implementing security measures. The security unit adjusts the level of security detail based on, for example, the importance of the data. For example, the security unit can implement detailed security measures for highly important data to increase accuracy. The security unit can also implement simplified security measures for less important data to use resources efficiently. This allows the level of security detail to be adjusted based on the importance of the data.

[0061] When taking security measures, the security unit can apply different security algorithms depending on the category of data. For example, the security unit can apply different security algorithms depending on the category of data. For example, the security unit can apply a natural language processing algorithm to text data. The security unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the security unit can also apply an image recognition algorithm to image data. This makes it possible to apply different security algorithms depending on the category of data.

[0062] When implementing security measures, the security department can improve the accuracy of security by referring to past security incidents. For example, the security department can analyze past security incidents and improve the accuracy of algorithms. The security department can also reflect feedback obtained from past security incidents and improve processes. In this way, the accuracy of security can be improved by referring to past security incidents.

[0063] When taking security measures, the security department can determine the priority of security based on the time of data submission. The security department determines the priority of security based on, for example, the time of data submission. For example, the security department can prioritize security measures for data whose submission deadline is approaching. The security department can also postpone data whose submission deadline is far away, allowing for efficient use of resources. In this way, the priority of security can be determined based on the time of data submission.

[0064] The security unit can adjust the security order based on the relevance of data when taking security measures. The security unit adjusts the security order based on, for example, the relevance of data. For example, the security unit can prioritize security measures on highly relevant data and process it efficiently. The security unit can also postpone less relevant data and use resources efficiently. This allows the security order to be adjusted based on the relevance of data.

[0065] The security department can adjust the security process according to the user's level of expertise when implementing security measures. The security department adjusts the security process according to the user's level of expertise, for example. For example, the security department can provide a detailed security process to a user with a high level of expertise. The security department can also provide a simplified security process to a user with a low level of expertise. In this way, the security process can be adjusted according to the user's level of expertise.

[0066] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. The interface unit, for example, selects the optimal display method by referring to the user's past operation history. For example, the interface unit can prioritize displaying functions that the user has frequently used in the past. The interface unit can also suggest an optimal layout based on the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history.

[0067] The interface unit can customize the display content according to the user's current task when displaying the interface. The interface unit customizes the display content according to, for example, the user's current task. For example, the interface unit can prioritize displaying information related to the task the user is currently performing. The interface unit can also arrange necessary functions so that they can be easily accessed according to the user's current task. This allows the display content to be customized according to the user's current task.

[0068] The interface unit can select the optimal display method in consideration of the user's device information when displaying the interface. The interface unit selects the optimal display method in consideration of, for example, the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the interface unit can provide a display method that is optimized for a large screen. This makes it possible to select the optimal display method in consideration of the user's device information.

[0069] The interface unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. The interface unit can make the display content multilingual in accordance with the user's language setting, for example. For example, the interface unit can automatically set the interface language based on the language setting of the user's device. The interface unit can also provide a language switching function when the user uses multiple languages. This allows the display content to be multilingual in accordance with the user's language setting.

[0070] When displaying the interface, the interface unit can analyze the user's social media activity and provide related information. For example, the interface unit can provide information about places where the user has checked in on social media. The interface unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. This makes it possible to analyze the user's social media activity and provide related information.

[0071] When the interface is displayed, the interface unit can make schedule-based suggestions by referring to the user's calendar information. The interface unit can, for example, make schedule-based suggestions by referring to the user's calendar information. For example, the interface unit can refer to the schedules registered in the user's calendar and display related information. The interface unit can also suggest locations and information related to specific events from the user's calendar information. This makes it possible to make schedule-based suggestions by referring to the user's calendar information.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] In addition to collecting internal data, the collection department can also collect data from external public databases and APIs. For example, the collection department can collect economic indicator data from public government databases. The collection department can also collect trend information using social media APIs. Furthermore, the collection department can collect competitor product information from the company's public APIs. This allows for more comprehensive data analysis by integrating internal data and external data.

[0074] In addition to summarizing data, the automation unit can also detect anomalies in the data. For example, the automation unit can use machine learning algorithms to detect anomalous data patterns. The automation unit can also notify the anomaly detection results in real time. Furthermore, the automation unit can suggest automatic corrections for anomalous data. This allows for a rapid response while maintaining data quality.

[0075] In addition to converting data formats, the converter can also normalize the data. For example, the converter can unify data in different unit systems. The converter can also unify data in different date formats. Furthermore, the converter can also impute missing values ​​in the data. This improves the consistency and accuracy of the data, increasing the reliability of the analysis.

[0076] In addition to transmitting data, the transmitting unit can also manage the data distribution schedule. For example, the transmitting unit can set a schedule for transmitting data during a specific time period. The transmitting unit can also set a trigger for transmitting data when a specific event occurs. Furthermore, the transmitting unit can monitor the reception status of the destination and perform retransmission. This ensures reliable data delivery and improves convenience for recipients.

[0077] In addition to encrypting data and controlling access, the security unit can also detect data tampering. For example, the security unit can check the integrity of data using a hash function. The security unit can also issue an alert if data tampering is detected. Furthermore, the security unit can provide a procedure for recovering tampered data. This ensures the reliability of data and reduces security risks.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The collection unit collects in-house data. For example, the collection unit can collect in-house data using SQL. The collection unit can also collect data manually. Step 2: The automation department automates the data collected by the collection department. For example, the automation department uses iPaaS to automatically summarize the data and send it to a medium such as LINE. Step 3: The conversion unit converts the data automated by the automation unit to suit each medium. For example, the conversion unit can convert the format of the data to suit each medium. Step 4: The transmitting unit transmits the data converted by the converting unit. For example, the transmitting unit can transmit the converted data to each medium. Step 5: The security unit strengthens the security of the data transmitted by the transmission unit. For example, the security unit performs data encryption and access control.

[0080] (Example 2) The system according to an embodiment of the present invention is a solution that connects internal systems using iPaaS. This system collects internal data, automates it using iPaaS, makes original proposals tailored to each medium, and strengthens security. This allows the system to efficiently aggregate and automate internal data, reducing the burden on employees. Furthermore, strengthened security reduces the risk of internal information leaks. Furthermore, making original proposals tailored to each medium enables more effective information sharing.

[0081] The system according to the embodiment includes a collection unit, an automation unit, a conversion unit, a transmission unit, and a security unit. The collection unit collects internal company data. For example, the collection unit can collect internal company data using SQL. The collection unit can also collect data manually. The automation unit automates the data collected by the collection unit. For example, the automation unit automatically summarizes the data using iPaaS and transmits it to a medium such as LINE. The conversion unit converts the data automated by the automation unit to suit each medium. For example, the conversion unit can convert the data format to suit each medium. The transmission unit transmits the data converted by the conversion unit. For example, the transmission unit can transmit the converted data to each medium. The security unit enhances the security of the data transmitted by the transmission unit. For example, the security unit performs data encryption and access control. As a result, the system according to the embodiment can efficiently collect, automate, convert, transmit, and enhance the security of internal company data.

[0082] The collection unit can collect in-house data using SQL or manually. For example, the collection unit collects in-house data using SQL. For example, the collection unit can extract data from a database using a specific query. The collection unit can also collect data manually. For example, the collection unit can provide a form for employees to manually enter data. This allows for efficient collection of in-house data.

[0083] The automation unit can automatically summarize data using iPaaS and transmit it to at least one medium within LINE. The automation unit can, for example, automatically summarize data using iPaaS. For example, the automation unit can generate a summary of the data using a generation AI. The automation unit can also transmit the summarized data to at least one medium within LINE. For example, the automation unit can transmit the summarized data using LINE's API. This automates and efficiently performs the summarization and transmission of data.

[0084] The security unit can perform data encryption or access control. The security unit, for example, encrypts data. For example, the security unit can encrypt data using AES (Advanced Encryption Standard). The security unit can also perform access control. For example, the security unit can allow only specific users to access data. This strengthens the security of the data.

[0085] The conversion unit can convert the data format to suit each medium. The conversion unit performs data format conversion, for example. For example, the conversion unit can convert data in CSV format to JSON format. The conversion unit can also convert data in XML format to HTML format. This allows for efficient data format conversion.

[0086] The transmitting unit can transmit the converted data to each medium. The transmitting unit transmits, for example, the converted data to each medium. For example, the transmitting unit can transmit the data using email. The transmitting unit can also transmit the data using FTP (File Transfer Protocol). This allows for efficient data transmission.

[0087] The system includes an interface unit that provides a user-friendly interface. The interface unit provides, for example, a user-friendly interface. For example, the interface unit can provide a graphical user interface (GUI) that allows intuitive operation. The interface unit can also support voice input and voice output. This allows the user to easily operate the system.

[0088] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using a generation AI. The collection unit can also adjust the timing of data collection based on the estimated user's emotions. For example, the collection unit can reduce the frequency of data collection to reduce the burden when the user is feeling stressed. The collection unit can increase the frequency of data collection when the user is relaxed and collect data efficiently. This makes it possible to adjust the timing of data collection according to the user's emotions.

[0089] The collection unit can analyze the frequency of data collection from each department within the company and set an optimal collection schedule. The collection unit, for example, analyzes the frequency of data collection from each department within the company. For example, the collection unit can analyze the data generation frequency of each department and set the optimal collection timing. The collection unit can also adjust the timing of data collection taking into account the business schedule of each department. This makes it possible to set an optimal collection schedule.

[0090] When collecting data, the collection unit can determine the collection priority based on the importance of the data. The collection unit determines the collection priority based on, for example, the importance of the data. For example, the collection unit can collect data with high importance first and process it quickly. The collection unit can also postpone data with low importance, allowing for efficient use of resources. In this way, the collection priority can be determined based on the importance of the data.

[0091] When collecting data, the collection unit can select the optimal collection means depending on the format of the data. For example, the collection unit can collect text data using natural language processing technology. The collection unit can also collect numerical data using database queries. Furthermore, the collection unit can collect image data using image recognition technology. This makes it possible to select the optimal collection means depending on the format of the data.

[0092] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using a generation AI. The collection unit can also determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting only data with high importance. When the user is relaxed, the collection unit can collect all data equally. This makes it possible to determine the priority of data to be collected based on the user's emotions.

[0093] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the geographical distribution within the company. For example, the collection unit can prioritize collecting highly relevant data by taking into account the geographical distribution within the company. For example, the collection unit can prioritize collecting nearby data by taking into account the geographical location of each department. The collection unit can also prioritize collecting data that is highly geographically relevant, thereby efficiently collecting data. This allows highly relevant data to be prioritized by taking into account the geographical distribution.

[0094] The collection unit can analyze internal social media activities and collect related data when collecting data. The collection unit, for example, analyzes internal social media activities and collects related data. For example, the collection unit can analyze the content of internal social media posts and collect related data. The collection unit can also set the optimal collection timing in consideration of the frequency of internal social media activities. This makes it possible to analyze social media activities and collect related data.

[0095] The collection unit can customize the collection method by reflecting past feedback when collecting data. The collection unit customizes the collection method by reflecting, for example, past feedback. For example, the collection unit can analyze past data collection results and optimize the collection method. The collection unit can also improve the collection method by reflecting feedback from users. This allows the collection method to be customized by reflecting past feedback.

[0096] The automation unit can estimate the user's emotions and adjust the automation process based on the estimated user's emotions. The automation unit, for example, estimates the user's emotions. For example, the automation unit can estimate the user's emotions using generative AI. The automation unit can also adjust the automation process based on the estimated user's emotions. For example, if the user is feeling stressed, the automation unit can simplify the automation process and reduce the burden. Also, if the user is relaxed, the automation unit can provide a detailed automation process. This makes it possible to adjust the automation process based on the user's emotions.

[0097] The automation unit can adjust the level of automation detail based on the importance of the data during automation. The automation unit can adjust the level of automation detail based on, for example, the importance of the data. For example, the automation unit can automate highly important data in detail to improve accuracy. The automation unit can also simplify and automate less important data to use resources efficiently. This allows the level of automation detail to be adjusted based on the importance of the data.

[0098] During automation, the automation unit can apply different automation algorithms depending on the category of data. For example, the automation unit can apply different automation algorithms depending on the category of data. For example, the automation unit can apply a natural language processing algorithm to text data. The automation unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the automation unit can apply an image recognition algorithm to image data. In this way, different automation algorithms can be applied depending on the category of data.

[0099] During automation, the automation unit can improve the accuracy of automation by referring to past automation results. For example, the automation unit can improve the accuracy of automation by referring to past automation results. For example, the automation unit can analyze past automation results and improve the accuracy of the algorithm. The automation unit can also reflect feedback obtained from past automation results and improve the process. In this way, the accuracy of automation can be improved by referring to past automation results.

[0100] The automation unit can estimate the user's emotions and determine the priority of automation based on the estimated user's emotions. The automation unit, for example, estimates the user's emotions. For example, the automation unit can estimate the user's emotions using generative AI. The automation unit can also determine the priority of automation based on the estimated user's emotions. For example, the automation unit can prioritize automation of highly important tasks when the user is feeling stressed. The automation unit can also automate all tasks equally when the user is relaxed. This makes it possible to determine the priority of automation based on the user's emotions.

[0101] During automation, the automation unit can determine the priority of automation based on the time of data submission. The automation unit determines the priority of automation based on, for example, the time of data submission. For example, the automation unit can prioritize automation of data with an upcoming submission deadline. The automation unit can also postpone data with a distant submission deadline, allowing for efficient use of resources. This makes it possible to determine the priority of automation based on the time of data submission.

[0102] The automation unit can adjust the order of automation based on the relevance of data during automation. The automation unit adjusts the order of automation based on, for example, the relevance of data. For example, the automation unit can prioritize automation of highly relevant data and process it efficiently. The automation unit can also postpone data with low relevance and use resources efficiently. This makes it possible to adjust the order of automation based on the relevance of data.

[0103] During automation, the automation unit can adjust the automation process according to the user's level of expertise. The automation unit adjusts the automation process according to, for example, the user's level of expertise. For example, the automation unit can provide a detailed automation process to a user with high expertise. The automation unit can also provide a simplified automation process to a user with low expertise. In this way, the automation process can be adjusted according to the user's level of expertise.

[0104] The conversion unit can estimate the user's emotion and adjust the data conversion method based on the estimated user's emotion. The conversion unit, for example, estimates the user's emotion. For example, the conversion unit can estimate the user's emotion using a generation AI. The conversion unit can also adjust the data conversion method based on the estimated user's emotion. For example, the conversion unit can provide a simplified data conversion method when the user is feeling stressed. The conversion unit can also provide a detailed data conversion method when the user is relaxed. This makes it possible to adjust the data conversion method based on the user's emotion.

[0105] The conversion unit can adjust the level of detail of the conversion based on the importance of the data when converting data. For example, the conversion unit can convert data with high importance in detail to increase accuracy. The conversion unit can also simplify and convert data with low importance to use resources efficiently. This allows the level of detail of the conversion to be adjusted based on the importance of the data.

[0106] During data conversion, the conversion unit can apply different conversion algorithms depending on the category of data. For example, the conversion unit can apply different conversion algorithms depending on the category of data. For example, the conversion unit can apply a natural language processing algorithm to text data. The conversion unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the conversion unit can also apply an image recognition algorithm to image data. This makes it possible to apply different conversion algorithms depending on the category of data.

[0107] When converting data, the conversion unit can improve the accuracy of the conversion by referring to past conversion results. For example, the conversion unit can analyze past conversion results to improve the accuracy of the algorithm. The conversion unit can also reflect feedback obtained from past conversion results and improve the process. In this way, the accuracy of the conversion can be improved by referring to past conversion results.

[0108] The conversion unit can estimate the user's emotions and determine the priority of data to be converted based on the estimated user's emotions. The conversion unit, for example, estimates the user's emotions. For example, the conversion unit can estimate the user's emotions using a generation AI. The conversion unit can also determine the priority of data to be converted based on the estimated user's emotions. For example, if the user is feeling stressed, the conversion unit can prioritize converting data with high importance. Also, if the user is relaxed, the conversion unit can convert all data equally. This makes it possible to determine the priority of data to be converted based on the user's emotions.

[0109] When converting data, the conversion unit can determine the priority of conversion based on the time of data submission. The conversion unit determines the priority of conversion based on, for example, the time of data submission. For example, the conversion unit can preferentially convert data with an upcoming submission deadline. The conversion unit can also postpone data with a distant submission deadline, allowing for efficient use of resources. This allows the priority of conversion to be determined based on the time of data submission.

[0110] The conversion unit can adjust the order of conversion based on the relevance of the data when converting data. The conversion unit adjusts the order of conversion based on, for example, the relevance of the data. For example, the conversion unit can convert highly relevant data preferentially and process it efficiently. The conversion unit can also postpone data with low relevance and use resources efficiently. This allows the order of conversion to be adjusted based on the relevance of the data.

[0111] During data conversion, the conversion unit can adjust the conversion process according to the user's level of expertise. The conversion unit adjusts the conversion process according to the user's level of expertise, for example. For example, the conversion unit can provide a detailed conversion process to a user with high expertise. The conversion unit can also provide a simplified conversion process to a user with low expertise. In this way, the conversion process can be adjusted according to the user's level of expertise.

[0112] The transmitting unit can estimate the user's emotions and adjust the timing of data transmission based on the estimated user's emotions. The transmitting unit, for example, estimates the user's emotions. For example, the transmitting unit can estimate the user's emotions using a generation AI. The transmitting unit can also adjust the timing of data transmission based on the estimated user's emotions. For example, if the user is feeling stressed, the transmitting unit can reduce the transmission frequency to reduce the burden. Also, if the user is relaxed, the transmitting unit can increase the transmission frequency to transmit data efficiently. This makes it possible to adjust the timing of data transmission based on the user's emotions.

[0113] The transmitting unit can adjust the level of detail of the transmission based on the importance of the data when transmitting data. The transmitting unit adjusts the level of detail of the transmission based on, for example, the importance of the data. For example, the transmitting unit can transmit data of high importance in detail to increase accuracy. The transmitting unit can also transmit data of low importance in a simplified form to efficiently use resources. In this way, the level of detail of the transmission can be adjusted based on the importance of the data.

[0114] The transmitting unit can apply different transmission algorithms depending on the category of data when transmitting data. For example, the transmitting unit can apply different transmission algorithms depending on the category of data. For example, the transmitting unit can apply a natural language processing algorithm to text data. The transmitting unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the transmitting unit can also apply an image recognition algorithm to image data. In this way, different transmission algorithms can be applied depending on the category of data.

[0115] When transmitting data, the transmitting unit can improve the accuracy of transmission by referring to past transmission results. For example, the transmitting unit can improve the accuracy of transmission by referring to past transmission results. For example, the transmitting unit can analyze past transmission results and improve the accuracy of the algorithm. The transmitting unit can also reflect feedback obtained from past transmission results and improve the process. In this way, the accuracy of transmission can be improved by referring to past transmission results.

[0116] The transmission unit can estimate the user's emotions and determine the priority of data to be transmitted based on the estimated user's emotions. The transmission unit, for example, estimates the user's emotions. For example, the transmission unit can estimate the user's emotions using a generation AI. The transmission unit can also determine the priority of data to be transmitted based on the estimated user's emotions. For example, if the user is feeling stressed, the transmission unit can prioritize transmitting data with high importance. Also, if the user is relaxed, the transmission unit can transmit all data equally. In this way, the priority of data to be transmitted can be determined based on the user's emotions.

[0117] When transmitting data, the transmitting unit can determine the priority of transmission based on the time of data submission. The transmitting unit determines the priority of transmission based on, for example, the time of data submission. For example, the transmitting unit can preferentially transmit data with an upcoming submission deadline. The transmitting unit can also postpone data with a distant submission deadline, thereby efficiently using resources. In this way, the priority of transmission can be determined based on the time of data submission.

[0118] The transmitting unit can adjust the order of transmission based on the relevance of the data when transmitting the data. The transmitting unit adjusts the order of transmission based on, for example, the relevance of the data. For example, the transmitting unit can transmit highly relevant data preferentially and process it efficiently. The transmitting unit can also postpone data with low relevance and use resources efficiently. This allows the order of transmission to be adjusted based on the relevance of the data.

[0119] When transmitting data, the transmitting unit can adjust the transmission process according to the user's level of expertise. The transmitting unit, for example, adjusts the transmission process according to the user's level of expertise. For example, the transmitting unit can provide a detailed transmission process to a user with high expertise. The transmitting unit can also provide a simplified transmission process to a user with low expertise. In this way, the transmission process can be adjusted according to the user's level of expertise.

[0120] The security unit can estimate the user's emotions and adjust security measures based on the estimated user's emotions. The security unit, for example, estimates the user's emotions. For example, the security unit can estimate the user's emotions using generative AI. The security unit can also adjust security measures based on the estimated user's emotions. For example, the security unit can simplify security measures to reduce the burden when the user is feeling stressed. The security unit can also provide detailed security measures when the user is relaxed. This makes it possible to adjust security measures based on the user's emotions.

[0121] The security unit can adjust the level of security detail based on the importance of the data when implementing security measures. The security unit adjusts the level of security detail based on, for example, the importance of the data. For example, the security unit can implement detailed security measures for highly important data to increase accuracy. The security unit can also implement simplified security measures for less important data to use resources efficiently. This allows the level of security detail to be adjusted based on the importance of the data.

[0122] When taking security measures, the security unit can apply different security algorithms depending on the category of data. For example, the security unit can apply different security algorithms depending on the category of data. For example, the security unit can apply a natural language processing algorithm to text data. The security unit can also apply a statistical analysis algorithm to numerical data. Furthermore, the security unit can also apply an image recognition algorithm to image data. This makes it possible to apply different security algorithms depending on the category of data.

[0123] When implementing security measures, the security department can improve the accuracy of security by referring to past security incidents. For example, the security department can analyze past security incidents and improve the accuracy of algorithms. The security department can also reflect feedback obtained from past security incidents and improve processes. In this way, the accuracy of security can be improved by referring to past security incidents.

[0124] The security unit can estimate the user's emotions and determine the priority of security measures based on the estimated user's emotions. The security unit, for example, estimates the user's emotions. For example, the security unit can estimate the user's emotions using a generation AI. The security unit can also determine the priority of security measures based on the estimated user's emotions. For example, if the user is feeling stressed, the security unit can prioritize security measures with higher importance. Also, if the user is relaxed, the security unit can implement all security measures equally. This makes it possible to determine the priority of security measures based on the user's emotions.

[0125] When taking security measures, the security department can determine the priority of security based on the time of data submission. The security department determines the priority of security based on, for example, the time of data submission. For example, the security department can prioritize security measures for data whose submission deadline is approaching. The security department can also postpone data whose submission deadline is far away, allowing for efficient use of resources. In this way, the priority of security can be determined based on the time of data submission.

[0126] The security unit can adjust the security order based on the relevance of data when taking security measures. The security unit adjusts the security order based on, for example, the relevance of data. For example, the security unit can prioritize security measures on highly relevant data and process it efficiently. The security unit can also postpone less relevant data and use resources efficiently. This allows the security order to be adjusted based on the relevance of data.

[0127] The security department can adjust the security process according to the user's level of expertise when implementing security measures. The security department adjusts the security process according to the user's level of expertise, for example. For example, the security department can provide a detailed security process to a user with a high level of expertise. The security department can also provide a simplified security process to a user with a low level of expertise. In this way, the security process can be adjusted according to the user's level of expertise.

[0128] The interface unit can estimate the user's emotion and adjust the interface display method based on the estimated user's emotion. The interface unit, for example, estimates the user's emotion. For example, the interface unit can estimate the user's emotion using a generation AI. The interface unit can also adjust the interface display method based on the estimated user's emotion. For example, the interface unit can provide a simple, highly visible interface when the user is feeling stressed. The interface unit can also provide an interface including detailed information when the user is relaxed. This makes it possible to adjust the interface display method based on the user's emotion.

[0129] When displaying the interface, the interface unit can select the optimal display method by referring to the user's past operation history. The interface unit, for example, selects the optimal display method by referring to the user's past operation history. For example, the interface unit can prioritize displaying functions that the user has frequently used in the past. The interface unit can also suggest an optimal layout based on the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history.

[0130] The interface unit can customize the display content according to the user's current task when displaying the interface. The interface unit customizes the display content according to, for example, the user's current task. For example, the interface unit can prioritize displaying information related to the task the user is currently performing. The interface unit can also arrange necessary functions so that they can be easily accessed according to the user's current task. This allows the display content to be customized according to the user's current task.

[0131] The interface unit can select the optimal display method in consideration of the user's device information when displaying the interface. The interface unit selects the optimal display method in consideration of, for example, the user's device information. For example, if the user is using a smartphone, the interface unit can provide a display method that matches the screen size. Also, if the user is using a tablet, the interface unit can provide a display method that is optimized for a large screen. This makes it possible to select the optimal display method in consideration of the user's device information.

[0132] The interface unit can estimate the user's emotion and adjust the operation procedure of the interface based on the estimated user's emotion. The interface unit, for example, estimates the user's emotion. For example, the interface unit can estimate the user's emotion using a generation AI. The interface unit can also adjust the operation procedure of the interface based on the estimated user's emotion. For example, the interface unit can simplify the operation procedure and reduce the burden when the user is feeling stressed. The interface unit can also provide detailed operation procedures when the user is relaxed. This makes it possible to adjust the operation procedure of the interface based on the user's emotion.

[0133] The interface unit can make the display content multilingual in accordance with the user's language setting when displaying the interface. The interface unit can make the display content multilingual in accordance with the user's language setting, for example. For example, the interface unit can automatically set the interface language based on the language setting of the user's device. The interface unit can also provide a language switching function when the user uses multiple languages. This allows the display content to be multilingual in accordance with the user's language setting.

[0134] When displaying the interface, the interface unit can analyze the user's social media activity and provide related information. For example, the interface unit can provide information about places where the user has checked in on social media. The interface unit can also analyze the content of the user's social media posts and provide information about related tourist spots and stores. This makes it possible to analyze the user's social media activity and provide related information.

[0135] When the interface is displayed, the interface unit can make schedule-based suggestions by referring to the user's calendar information. The interface unit can, for example, make schedule-based suggestions by referring to the user's calendar information. For example, the interface unit can refer to the schedules registered in the user's calendar and display related information. The interface unit can also suggest locations and information related to specific events from the user's calendar information. This makes it possible to make schedule-based suggestions by referring to the user's calendar information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, automation unit, conversion unit, transmission unit, security unit, and interface unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversion unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The transmission unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The security unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The interface unit is realized, for example, by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, automation unit, conversion unit, transmission unit, security unit, and interface unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The transmission unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The security unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The interface unit is realized, for example, by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, automation unit, conversion unit, transmission unit, security unit, and interface unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversion unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The transmission unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The security unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The interface unit is realized, for example, by the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, automation unit, conversion unit, transmission unit, security unit, and interface unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The automation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The conversion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The transmission unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The security unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The interface unit is realized, for example, by the control unit 46A of the robot 414.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] In addition to collecting internal data, the collection department can also collect data from external public databases and APIs. For example, the collection department can collect economic indicator data from public government databases. The collection department can also collect trend information using social media APIs. Furthermore, the collection department can collect competitor product information from the company's public APIs. This allows for more comprehensive data analysis by integrating internal data and external data.

[0138] In addition to summarizing data, the automation unit can also detect anomalies in the data. For example, the automation unit can use machine learning algorithms to detect anomalous data patterns. The automation unit can also notify the anomaly detection results in real time. Furthermore, the automation unit can suggest automatic corrections for anomalous data. This allows for a rapid response while maintaining data quality.

[0139] In addition to converting data formats, the converter can also normalize the data. For example, the converter can unify data in different unit systems. The converter can also unify data in different date formats. Furthermore, the converter can also impute missing values ​​in the data. This improves the consistency and accuracy of the data, increasing the reliability of the analysis.

[0140] In addition to transmitting data, the transmitting unit can also manage the data distribution schedule. For example, the transmitting unit can set a schedule for transmitting data during a specific time period. The transmitting unit can also set a trigger for transmitting data when a specific event occurs. Furthermore, the transmitting unit can monitor the reception status of the destination and perform retransmission. This ensures reliable data delivery and improves convenience for recipients.

[0141] In addition to encrypting data and controlling access, the security unit can also detect data tampering. For example, the security unit can check the integrity of data using a hash function. The security unit can also issue an alert if data tampering is detected. Furthermore, the security unit can provide a procedure for recovering tampered data. This ensures the reliability of data and reduces security risks.

[0142] The collection unit can estimate the user's emotions and customize the data collection method based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to efficiently collect data. Furthermore, the collection unit can adjust the timing of data collection according to the user's emotions. This makes it possible to collect data that takes the user's emotions into consideration.

[0143] The automation unit can estimate the user's emotions and adjust the automation process based on the estimated user's emotions. For example, if the user is feeling stressed, the automation unit can simplify the automation process and reduce the burden. Also, if the user is relaxed, the automation unit can provide a detailed automation process. Furthermore, the automation unit can determine the priority of automation according to the user's emotions. This enables flexible automation based on the user's emotions.

[0144] The conversion unit can estimate the user's emotion and adjust the data conversion method based on the estimated user's emotion. For example, the conversion unit can provide a simplified data conversion method when the user is stressed. Alternatively, the conversion unit can provide a detailed data conversion method when the user is relaxed. Furthermore, the conversion unit can determine the priority of data to be converted according to the user's emotion. This enables data conversion based on the user's emotion.

[0145] The transmitting unit can estimate the user's emotions and adjust the timing of data transmission based on the estimated user's emotions. For example, if the user is feeling stressed, the transmitting unit can reduce the transmission frequency to reduce the burden on the user. Also, if the user is relaxed, the transmitting unit can increase the transmission frequency to transmit data efficiently. Furthermore, the transmitting unit can determine the priority of data to be transmitted according to the user's emotions. This makes it possible to transmit data based on the user's emotions.

[0146] The security unit can estimate the user's emotions and adjust security measures based on the estimated user's emotions. For example, if the user is feeling stressed, the security unit can simplify security measures to reduce the burden on the user. Alternatively, if the user is relaxed, the security unit can provide more detailed security measures. Furthermore, the security unit can determine the priority of security measures according to the user's emotions. This makes it possible to implement security measures based on the user's emotions.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The collection unit collects in-house data. For example, the collection unit can collect in-house data using SQL. The collection unit can also collect data manually. Step 2: The automation department automates the data collected by the collection department. For example, the automation department uses iPaaS to automatically summarize the data and send it to a medium such as LINE. Step 3: The conversion unit converts the data automated by the automation unit to suit each medium. For example, the conversion unit can convert the format of the data to suit each medium. Step 4: The transmitting unit transmits the data converted by the converting unit. For example, the transmitting unit can transmit the converted data to each medium. Step 5: The security unit strengthens the security of the data transmitted by the transmission unit. For example, the security unit performs data encryption and access control.

[0149] 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.

[0150] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects internal data; an automation unit that automates the data collected by the collection unit; a conversion unit that converts the data automated by the automation unit to fit each medium; a transmitting unit that transmits the data converted by the converting unit; a security unit that enhances security of the data transmitted by the transmission unit; Equipped with A system characterized by:

2. The collecting unit Collect internal data via SQL or manually 2. The system of claim 1.

3. The security unit Encrypting data or controlling access 2. The system of claim 1.

4. The conversion unit Convert data formats to suit each medium 2. The system of claim 1.

5. The transmission unit Transmit the converted data to each medium 2. The system of claim 1.

6. Equipped with an interface section that provides a user-friendly interface 2. The system of claim 1.

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

8. The collecting unit Analyze the frequency of data collection from each department within the company and set the optimal collection schedule 2. The system of claim 1.

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