system

Abstract

The system according to the embodiment aims to make in-house information available in multiple languages, enabling foreign employees to carry out their work smoothly. [Solution] A system according to an embodiment includes a collection unit, a translation unit, and a provision unit. The collection unit collects information related to in-house business. The translation unit translates the information collected by the collection unit into multiple languages. The provision unit provides the information translated by the translation unit to each employee.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that internal company information is not multilingual, making it difficult for foreign employees to carry out their work smoothly.

[0005] The system according to the embodiment aims to make in-house information available in multiple languages, enabling foreign employees to carry out their work smoothly. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a translation unit, and a provision unit. The collection unit collects information related to in-house business. The translation unit translates the information collected by the collection unit into multiple languages. The provision unit provides the information translated by the translation unit to each employee. [Effects of the Invention]

[0007] The system according to the embodiment can make in-house information available in multiple languages, enabling foreign employees to carry out their work smoothly. [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) A system according to an embodiment of the present invention provides multilingual support for internal operations, enabling foreign employees to smoothly execute their work. This system collects information about internal operations, translates it into multiple languages ​​using a generation AI, and provides the translated information to each employee, thereby enabling smooth operations. For example, when collecting information about internal operations, detailed information necessary for the operations, such as work procedures, manuals, and important notices, is collected. The collected information is then translated into multiple languages ​​using a generation AI. The generation AI analyzes the collected information and performs translations appropriate for each language. For example, a Japanese work procedure manual may be translated into English, Chinese, Spanish, or other languages. The generation AI is trained to handle technical terms and expressions specific to the operation. The translated information is then provided to each employee. For example, when a foreign employee references a work procedure manual, they can view the manual in their native language. This allows them to smoothly execute their work without experiencing language barriers. This system enables foreign employees to perform their work efficiently. For example, providing multilingual work procedures and manuals makes it easier for foreign employees to understand the work, reducing errors and problems. Furthermore, important information can be provided in multiple languages, which makes the communication of information smoother and improves work efficiency. This allows the system to enable foreign employees to carry out their work efficiently without feeling a language barrier.

[0029] The business support system according to the embodiment includes a collection unit, a translation unit, and a provision unit. The collection unit collects information related to internal business operations. The collection unit collects information such as business procedure manuals, manuals, and internal notices. For example, the collection unit can collect information in formats such as PDF files, Word documents, and emails. The collection unit can also automatically collect business-related information using AI. For example, the collection unit extracts and collects information from an internal database or document management system. The translation unit translates the information collected by the collection unit into multiple languages. The translation unit uses a generation AI to analyze the collected information and provide translations appropriate for each language. For example, the translation unit translates Japanese business procedure manuals into English, Chinese, Spanish, and other languages. The generation AI is trained to handle technical terms and business-specific expressions. For example, the generation AI can understand the contents of the business procedure manuals and provide appropriate translations. The provision unit provides the information translated by the translation unit to each employee. For example, the provision unit provides the translated information via email, an internal portal site, or the like. For example, the providing unit may send the translated work procedure manual by email so that employees can view the procedure manual in their native language. The providing unit may also post the translated information on an in-house portal site so that employees can refer to the necessary information at any time. As a result, the work support system according to the embodiment can enable foreign employees to perform their work efficiently without experiencing a language barrier.

[0030] The collection unit can collect information such as business procedure manuals, manuals, and internal notices. For example, the collection unit can collect information such as business procedure manuals, manuals, and internal notices. For example, the collection unit can collect information in formats such as PDF files, Word documents, and emails. The collection unit can also automatically collect information related to business operations using AI. For example, the collection unit extracts and collects information from internal databases and document management systems. This improves the accuracy of translation by collecting detailed information necessary for business operations. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can extract information from internal databases and automatically collect it using AI.

[0031] The translation unit may include a learning unit that learns technical terminology and expressions specific to a business. The translation unit may include a learning unit that learns technical terminology and expressions specific to a business. The learning unit learns, for example, technical terminology and expressions specific to an industry. For example, the learning unit can learn technical terminology contained in business procedure manuals and manuals and perform appropriate translations. The learning unit may also use a generation AI to learn technical terminology and expressions specific to a business. For example, the learning unit can input a dataset of technical terminology into the generation AI and have it learn, thereby producing translations that correspond to the technical terminology. This enables translations that can also correspond to the technical terminology and expressions specific to a business. Some or all of the above-mentioned processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit can learn technical terminology using the generation AI to improve the accuracy of translations.

[0032] The translation department can translate the collected information into English, Chinese, and Spanish. The translation department translates the collected information into English, Chinese, Spanish, etc. The translation department uses generation AI to analyze the collected information and provide translations appropriate for each language. For example, the translation department translates Japanese work procedure manuals into English, Chinese, Spanish, etc. The generation AI is trained to handle technical terms and expressions specific to the work. For example, the generation AI can understand the contents of the work procedure manuals and provide appropriate translations. This enables translations that support multiple languages. Some or all of the above-mentioned processing in the translation department may be performed using generation AI, or may be performed without using generation AI. For example, the translation department can translate collected information into multiple languages ​​using generation AI and provide it to each employee.

[0033] The provision unit can provide the translated information via email or an internal portal site. The provision unit provides the translated information via email or an internal portal site. For example, the provision unit can send translated work procedure manuals via email so that employees can view the procedures in their native language. The provision unit can also post the translated information on an internal portal site so that employees can refer to the necessary information at any time. For example, the provision unit can provide the translated information via an intranet or a chat tool. This allows the translated information to be provided to each employee efficiently. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can automatically distribute translated information using AI so that employees can quickly obtain the necessary information.

[0034] The translation unit may include a verification unit that checks the quality of the translation. The translation unit may include a verification unit that checks the quality of the translation. The verification unit checks, for example, the accuracy and naturalness of the translation, the appropriateness of technical terminology, etc. For example, the verification unit checks the translated business procedure manual to check for mistranslations and unnatural expressions. The verification unit may also automatically verify the quality of the translation using a generation AI. For example, the verification unit inputs the translation results into the generation AI and has it evaluate the quality, thereby enabling the provision of accurate information. In this way, by checking the quality of the translation, accurate information can be provided. Some or all of the above-mentioned processing in the verification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the verification unit may use the generation AI to verify the quality of the translation, thereby enabling the provision of accurate information.

[0035] The collection unit can analyze past information collection history and select the most appropriate collection method. The collection unit analyzes past information collection history and selects the most appropriate collection method. For example, the collection unit selects the most efficient collection method from the past information collection history. For example, the collection unit determines the priority of information to be collected based on the past information collection history. The collection unit can also analyze the past information collection history and identify areas for improvement in the collection method. For example, the collection unit analyzes the past information collection history and finds areas for improvement to improve the efficiency of the collection method. This enables efficient information collection by selecting the optimal collection method based on the past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the past information collection history using AI and select the optimal collection method.

[0036] The collection unit can adjust the level of detail of the information to be collected based on the priority of the work when collecting information. The collection unit adjusts the level of detail of the information to be collected based on the priority of the work when collecting information. For example, when the priority of the work is high, the collection unit collects detailed information. For example, the collection unit prioritizes collecting information related to important work and provides detailed data. The collection unit can also collect only basic information when the priority of the work is low. For example, the collection unit briefly collects information related to low-priority work and provides the minimum necessary data. The collection unit can also adjust the scope of information to be collected based on the priority of the work. For example, the collection unit adjusts the level of detail of the information to be collected based on the importance and urgency of the work. This enables efficient information collection by adjusting the level of detail of the information based on the priority of the work. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can evaluate the priority of the work using AI and adjust the level of detail of the information collection.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. For example, when the user is in a specific city, the collection unit collects business information related to that city. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the user's destination. For example, when the user is on a business trip, the collection unit collects business information related to the business trip destination. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. For example, when the user is in an office, the collection unit collects business information related to the office. In this way, by taking into account the user's geographical location information, highly relevant information can be efficiently collected. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the user's geographical location information using AI and prioritize collecting highly relevant information.

[0038] The collection unit can analyze the user's social media activities and collect related information when collecting information. The collection unit can analyze the user's social media activities and collect related information when collecting information. The collection unit can collect related information, for example, based on information shared by the user on social media. For example, the collection unit can analyze content posted by the user on social media and collect related business information. The collection unit can also analyze the activities of the user's social media followers and friends and collect related information. For example, the collection unit can collect related business information based on information shared by the user's followers. The collection unit can also collect information related to topics in which the user showed interest on social media. For example, if the user showed interest in "marketing," the collection unit can collect business information related to marketing. This allows for efficient collection of highly relevant information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using AI, or can be performed without using AI. For example, the collection unit can analyze the user's social media activities and collect related information using AI.

[0039] The translation unit can adjust the level of detail of the translation based on the importance of the information during translation. The translation unit can adjust the level of detail of the translation based on the importance of the information during translation. For example, the translation unit performs a detailed translation for important information. For example, the translation unit translates important procedures and manuals in detail for the business. The translation unit can also perform only a basic translation for less important information. For example, the translation unit translates general notices briefly. The translation unit can also adjust the scope of the translation based on the importance of the information. For example, the translation unit adjusts the level of detail of the translation based on the impact and urgency of the business. This enables efficient translation by adjusting the level of detail of the translation based on the importance of the information. Some or all of the above-mentioned processing in the translation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation unit can evaluate the importance of the information using a generation AI and adjust the level of detail of the translation.

[0040] The translation unit can apply different translation algorithms depending on the category of information during translation. The translation unit can apply different translation algorithms depending on the category of information during translation. For example, for technical information, the translation unit uses a translation algorithm that corresponds to technical terminology. For example, when translating technical documents or technical manuals, the translation unit applies an algorithm that corresponds to technical terminology. The translation unit can also use a concise and easy-to-understand translation algorithm for general information. For example, the translation unit applies a concise algorithm when translating general notices or news articles. The translation unit can also use a translation algorithm specialized for the business for business-specific information. For example, the translation unit applies a business-specific algorithm when translating business procedure manuals or business manuals. This allows for more appropriate translation by applying a translation algorithm depending on the category of information. Some or all of the above-mentioned processing in the translation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the translation unit can identify the category of information using a generation AI and apply an appropriate translation algorithm.

[0041] The translation department can determine the priority of translation based on the time of submission of information during translation. The translation department determines the priority of translation based on the time of submission of information during translation. For example, the translation department gives the highest priority to urgent information. For example, the translation department prioritizes the translation of business procedure manuals whose submission deadline is approaching. The translation department can also prioritize the translation of information whose submission deadline is approaching. For example, the translation department quickly translates notices whose submission deadline is approaching. The translation department can also postpone the translation of information whose submission deadline is more flexible. For example, the translation department postpones the translation of business manuals whose submission deadline is further away. In this way, determining the priority of translation based on the time of submission of information enables efficient translation. Some or all of the above-mentioned processes in the translation department may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation department can evaluate the time of submission of information using a generation AI and determine the priority of translation.

[0042] The translation unit can adjust the order of translation based on the relevance of information during translation. The translation unit adjusts the order of translation based on the relevance of information during translation. For example, the translation unit prioritizes translating highly relevant information. For example, the translation unit prioritizes translating procedure manuals and manuals that are highly relevant to the work. The translation unit can also postpone translating less relevant information. For example, the translation unit postpones translating general notices. The translation unit can also adjust the order of translation based on the relevance of information. For example, the translation unit adjusts the order of translation based on the content of the work and the progress of the project. This enables efficient translation by adjusting the order of translation based on the relevance of information. Some or all of the above-mentioned processing in the translation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation unit can evaluate the relevance of information using a generation AI and adjust the order of translation.

[0043] The providing unit can select the most appropriate information provision method by referring to the user's past information browsing history when providing information. The providing unit can select the most appropriate information provision method by referring to the user's past information browsing history when providing information. For example, the providing unit can prioritize providing information that the user has frequently browsed in the past. For example, the providing unit can prioritize providing business procedures and manuals that the user has frequently browsed in the past. The providing unit can also select the optimal information provision method from the user's past browsing history. For example, the providing unit can analyze the format and provision method of information that the user has previously browsed and select the optimal method. The providing unit can also adjust the information provision order based on the user's past browsing history. For example, the providing unit can adjust the provision order based on the importance and relevance of information that the user has previously browsed. This makes it possible to provide optimal information by referring to the user's past information browsing history. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can analyze the user's past information browsing history using AI to select the optimal information provision method.

[0044] The provision unit can adjust the level of detail to be provided based on the importance of the information when providing the information. The provision unit can adjust the level of detail to be provided based on the importance of the information when providing the information. For example, in the case of important information, the provision unit provides the information in a manner including detailed explanations. For example, the provision unit can provide detailed procedures or manuals that are important for the business. In addition, the provision unit can provide only basic information for information of low importance. For example, the provision unit can provide general notices in a concise manner. In addition, the provision unit can adjust the scope of the information to be provided based on the importance of the information. For example, the provision unit adjusts the level of detail to be provided based on the impact and urgency of the business. This enables efficient information provision by adjusting the level of detail to be provided based on the importance of the information. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can evaluate the importance of the information using AI and adjust the level of detail to be provided.

[0045] The providing unit can select the optimal information providing method by taking into consideration the user's device information when providing information. The providing unit selects the optimal information providing method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit selects a information providing method that matches the screen size. For example, the providing unit selects an information display method optimized for the small screen of a smartphone. Furthermore, if the user is using a tablet, the providing unit can also select a information providing method optimized for a large screen. For example, the providing unit selects an information display method that matches the large screen of a tablet. Furthermore, if the user is using a desktop, the providing unit can also select a method for providing detailed information. For example, the providing unit selects an information display method optimized for the wide screen of a desktop. This enables optimal information provision by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can analyze the user's device information using AI and select the optimal information providing method.

[0046] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. When providing information, the providing unit prioritizes providing highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the providing unit prioritizes providing information related to that area. For example, when the user is in a specific city, the providing unit provides business information related to that city. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the user's destination. For example, when the user is on a business trip, the providing unit provides business information related to the business trip destination. Furthermore, when the user is in a specific location, the providing unit can prioritize providing information related to that location. For example, when the user is in an office, the providing unit provides business information related to the office. In this way, by taking into account the user's geographical location information, highly relevant information can be efficiently provided. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can analyze the user's geographical location information using AI and prioritize providing highly relevant information.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on past learning data. For example, the learning unit selects an optimal learning algorithm based on past translation results and user feedback. The learning unit can also analyze past learning data to identify improvements to the learning algorithm. For example, the learning unit analyzes past learning data to find improvements to improve the efficiency of the learning algorithm. The learning unit can also adjust parameters of the learning algorithm by referring to past learning data. For example, the learning unit optimizes parameters of the learning algorithm based on past learning data. In this way, the optimal learning algorithm can be applied by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can analyze past learning data using a generation AI to optimize the learning algorithm.

[0048] The learning unit can weight the learning data based on the time of submission of the information during learning. The learning unit weights the learning data based on the time of submission of the information during learning. For example, the learning unit weights the learning data based on the time of submission of the information when the submission deadline is approaching. For example, the learning unit prioritizes learning of business procedure manuals with an approaching submission deadline. The learning unit can also weight the learning data based on the time of submission of information with more time to spare. For example, the learning unit postpones learning of business manuals with a more distant submission deadline. The learning unit can also adjust the weighting of the learning data depending on the time of submission. For example, the learning unit adjusts the weighting of the learning data based on the progress and urgency of the work. As a result, weighting the learning data based on the time of submission of the information enables efficient learning. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can evaluate the time of submission of the information using a generation AI and weight the learning data.

[0049] The verification unit can optimize the verification algorithm by referring to past verification data during verification. The verification unit optimizes the verification algorithm by referring to past verification data during verification. The verification unit, for example, selects an optimal verification algorithm based on past verification data. For example, the verification unit selects an optimal verification algorithm based on past translation results and user feedback. The verification unit can also analyze past verification data to identify improvements to the verification algorithm. For example, the verification unit analyzes past verification data to find improvements to improve the efficiency of the verification algorithm. The verification unit can also adjust parameters of the verification algorithm by referring to past verification data. For example, the verification unit optimizes parameters of the verification algorithm based on past verification data. In this way, the optimal verification algorithm can be applied by referring to the past verification data. Some or all of the above-mentioned processing in the verification unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the verification unit can analyze past verification data using a generation AI to optimize the verification algorithm.

[0050] The verification unit can weight the verification data based on the time of submission of the information during verification. The verification unit weights the verification data based on the time of submission of the information during verification. For example, the verification unit weights the verification data based on the time of submission of the information when the submission deadline is approaching. For example, the verification unit prioritizes verification of business procedure manuals whose submission deadline is approaching. The verification unit can also weight the information with less time to submit by lowering the weight. For example, the verification unit postpones verification of business manuals whose submission deadline is far away. The verification unit can also adjust the weighting of the verification data depending on the time of submission. For example, the verification unit adjusts the weighting of the verification data based on the progress and urgency of the work. As a result, weighting the verification data based on the time of submission of the information enables efficient verification. Some or all of the above-mentioned processing in the verification unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the verification unit can evaluate the time of submission of the information using a generation AI and weight the verification data.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's past behavioral history and select the most appropriate information collection method. For example, it can prioritize collection of information sources that the user has frequently accessed in the past. It can also avoid information sources that the user has ignored in the past. Furthermore, it can analyze the user's behavioral patterns and determine the optimal collection timing. This enables efficient information collection based on the user's behavioral history.

[0053] When providing information, the providing unit can select the optimal providing method taking into consideration the remaining battery level of the user's device. For example, when the remaining battery level is low, the providing unit can provide information in a lightweight data format. When the remaining battery level is sufficient, the providing unit can provide detailed information. Furthermore, when the remaining battery level is medium, the providing unit can provide information with a medium level of detail. This makes it possible to provide optimal information according to the battery status of the device.

[0054] During translation, the translation unit can adjust the level of detail of the translation based on the reliability of the information. For example, if the information is highly reliable, a detailed translation can be performed. If the information is less reliable, a concise translation can be performed. Furthermore, if the information is medium reliable, a translation with a medium level of detail can be performed. This allows for appropriate translation according to the reliability of the information.

[0055] When collecting information, the collection unit can select the optimal collection method taking into account the user's internet connection status. For example, if the internet connection is unstable, the collection unit can collect information in a lightweight data format. If the internet connection is stable, the collection unit can collect detailed information. Furthermore, if the internet connection is medium, the collection unit can collect information with a medium level of detail. This makes it possible to collect optimal information according to the internet connection status.

[0056] When providing information, the providing unit can select the optimal delivery method by referring to the user's past feedback. For example, it can give priority to delivery methods that the user has previously rated highly. It can also avoid delivery methods that the user has previously rated poorly. Furthermore, it can analyze the user's feedback and identify areas for improvement in the delivery method. This makes it possible to provide optimal information based on the user's past feedback.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection department collects information related to internal operations. The collection department collects information such as work procedures, manuals, and internal communications. For example, the collection department can collect information in the form of PDF files, Word documents, emails, etc. The collection department can also use AI to automatically collect information related to operations. For example, the collection department extracts and collects information from internal databases and document management systems. Step 2: The translation department translates the information collected by the collection department into multiple languages. Using generation AI, the translation department analyzes the collected information and provides translations appropriate for each language. For example, the translation department translates a Japanese work procedure manual into English, Chinese, Spanish, etc. The generation AI is trained to handle technical terms and expressions specific to the work. For example, the generation AI can understand the contents of the work procedure manual and provide appropriate translations. Step 3: The provision department provides the information translated by the translation department to each employee. The provision department provides the translated information, for example, by email or on an internal portal site. For example, the provision department sends the translated work procedure manual by email so that employees can view the procedure manual in their own language. The provision department also posts the translated information on the internal portal site so that employees can refer to the information they need at any time.

[0059] (Example 2) A system according to an embodiment of the present invention provides multilingual support for internal operations, enabling foreign employees to smoothly execute their work. This system collects information about internal operations, translates it into multiple languages ​​using a generation AI, and provides the translated information to each employee, thereby enabling smooth operations. For example, when collecting information about internal operations, detailed information necessary for the operations, such as work procedures, manuals, and important notices, is collected. The collected information is then translated into multiple languages ​​using a generation AI. The generation AI analyzes the collected information and performs translations appropriate for each language. For example, a Japanese work procedure manual may be translated into English, Chinese, Spanish, or other languages. The generation AI is trained to handle technical terms and expressions specific to the operation. The translated information is then provided to each employee. For example, when a foreign employee references a work procedure manual, they can view the manual in their native language. This allows them to smoothly execute their work without experiencing language barriers. This system enables foreign employees to perform their work efficiently. For example, providing multilingual work procedures and manuals makes it easier for foreign employees to understand the work, reducing errors and problems. Furthermore, important information can be provided in multiple languages, which makes the communication of information smoother and improves work efficiency. This allows the system to enable foreign employees to carry out their work efficiently without feeling a language barrier.

[0060] The business support system according to the embodiment includes a collection unit, a translation unit, and a provision unit. The collection unit collects information related to internal business operations. The collection unit collects information such as business procedure manuals, manuals, and internal notices. For example, the collection unit can collect information in formats such as PDF files, Word documents, and emails. The collection unit can also automatically collect business-related information using AI. For example, the collection unit extracts and collects information from an internal database or document management system. The translation unit translates the information collected by the collection unit into multiple languages. The translation unit uses a generation AI to analyze the collected information and provide translations appropriate for each language. For example, the translation unit translates Japanese business procedure manuals into English, Chinese, Spanish, and other languages. The generation AI is trained to handle technical terms and business-specific expressions. For example, the generation AI can understand the contents of the business procedure manuals and provide appropriate translations. The provision unit provides the information translated by the translation unit to each employee. For example, the provision unit provides the translated information via email, an internal portal site, or the like. For example, the providing unit may send the translated work procedure manual by email so that employees can view the procedure manual in their native language. The providing unit may also post the translated information on an in-house portal site so that employees can refer to the necessary information at any time. As a result, the work support system according to the embodiment can enable foreign employees to perform their work efficiently without experiencing a language barrier.

[0061] The collection unit can collect information such as business procedure manuals, manuals, and internal notices. For example, the collection unit can collect information such as business procedure manuals, manuals, and internal notices. For example, the collection unit can collect information in formats such as PDF files, Word documents, and emails. The collection unit can also automatically collect information related to business operations using AI. For example, the collection unit extracts and collects information from internal databases and document management systems. This improves the accuracy of translation by collecting detailed information necessary for business operations. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can extract information from internal databases and automatically collect it using AI.

[0062] The translation unit may include a learning unit that learns technical terminology and expressions specific to a business. The translation unit may include a learning unit that learns technical terminology and expressions specific to a business. The learning unit learns, for example, technical terminology and expressions specific to an industry. For example, the learning unit can learn technical terminology contained in business procedure manuals and manuals and perform appropriate translations. The learning unit may also use a generation AI to learn technical terminology and expressions specific to a business. For example, the learning unit can input a dataset of technical terminology into the generation AI and have it learn, thereby producing translations that correspond to the technical terminology. This enables translations that can also correspond to the technical terminology and expressions specific to a business. Some or all of the above-mentioned processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit can learn technical terminology using the generation AI to improve the accuracy of translations.

[0063] The translation department can translate the collected information into English, Chinese, and Spanish. The translation department translates the collected information into English, Chinese, Spanish, etc. The translation department uses generation AI to analyze the collected information and provide translations appropriate for each language. For example, the translation department translates Japanese work procedure manuals into English, Chinese, Spanish, etc. The generation AI is trained to handle technical terms and expressions specific to the work. For example, the generation AI can understand the contents of the work procedure manuals and provide appropriate translations. This enables translations that support multiple languages. Some or all of the above-mentioned processing in the translation department may be performed using generation AI, or may be performed without using generation AI. For example, the translation department can translate collected information into multiple languages ​​using generation AI and provide it to each employee.

[0064] The provision unit can provide the translated information via email or an internal portal site. The provision unit provides the translated information via email or an internal portal site. For example, the provision unit can send translated work procedure manuals via email so that employees can view the procedures in their native language. The provision unit can also post the translated information on an internal portal site so that employees can refer to the necessary information at any time. For example, the provision unit can provide the translated information via an intranet or a chat tool. This allows the translated information to be provided to each employee efficiently. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can automatically distribute translated information using AI so that employees can quickly obtain the necessary information.

[0065] The translation unit may include a verification unit that checks the quality of the translation. The translation unit may include a verification unit that checks the quality of the translation. The verification unit checks, for example, the accuracy and naturalness of the translation, the appropriateness of technical terminology, etc. For example, the verification unit checks the translated business procedure manual to check for mistranslations and unnatural expressions. The verification unit may also automatically verify the quality of the translation using a generation AI. For example, the verification unit inputs the translation results into the generation AI and has it evaluate the quality, thereby enabling the provision of accurate information. In this way, by checking the quality of the translation, accurate information can be provided. Some or all of the above-mentioned processing in the verification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the verification unit may use the generation AI to verify the quality of the translation, thereby enabling the provision of accurate information.

[0066] The collection unit can estimate a user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit can estimate a user's emotions and adjust the timing of information collection based on the estimated user emotions. The collection unit estimates a user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit can capture a user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice and calculate an emotion score. The collection unit can also analyze the user's text data and estimate the emotions. For example, the collection unit can analyze the user's written sentences and calculate an emotion score. This enables efficient information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may use AI to estimate the user's emotions and adjust the timing of information collection.

[0067] The collection unit can analyze past information collection history and select the most appropriate collection method. The collection unit analyzes past information collection history and selects the most appropriate collection method. For example, the collection unit selects the most efficient collection method from the past information collection history. For example, the collection unit determines the priority of information to be collected based on the past information collection history. The collection unit can also analyze the past information collection history and identify areas for improvement in the collection method. For example, the collection unit analyzes the past information collection history and finds areas for improvement to improve the efficiency of the collection method. This enables efficient information collection by selecting the optimal collection method based on the past information collection history. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the past information collection history using AI and select the optimal collection method.

[0068] The collection unit can adjust the level of detail of the information to be collected based on the priority of the work when collecting information. The collection unit adjusts the level of detail of the information to be collected based on the priority of the work when collecting information. For example, when the priority of the work is high, the collection unit collects detailed information. For example, the collection unit prioritizes collecting information related to important work and provides detailed data. The collection unit can also collect only basic information when the priority of the work is low. For example, the collection unit briefly collects information related to low-priority work and provides the minimum necessary data. The collection unit can also adjust the scope of information to be collected based on the priority of the work. For example, the collection unit adjusts the level of detail of the information to be collected based on the importance and urgency of the work. This enables efficient information collection by adjusting the level of detail of the information based on the priority of the work. Some or all of the above-mentioned processing in the collection unit may be performed using AI or without AI. For example, the collection unit can evaluate the priority of the work using AI and adjust the level of detail of the information collection.

[0069] The collection unit can estimate a user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit can estimate a user's emotions and determine the priority of information to be collected based on the estimated user emotions. The collection unit estimates a user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the collection unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice and calculate an emotion score. The collection unit can also analyze the user's text data and estimate the emotion. For example, the collection unit can analyze the user's written text and calculate an emotion score. This enables efficient information collection by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may use AI to estimate the user's emotions and determine the priority of information.

[0070] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. When collecting information, the collection unit prioritizes collecting highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. For example, when the user is in a specific city, the collection unit collects business information related to that city. Furthermore, when the user is traveling, the collection unit can prioritize collecting information related to the user's destination. For example, when the user is on a business trip, the collection unit collects business information related to the business trip destination. Furthermore, when the user is in a specific location, the collection unit can prioritize collecting information related to that location. For example, when the user is in an office, the collection unit collects business information related to the office. In this way, by taking into account the user's geographical location information, highly relevant information can be efficiently collected. Some or all of the above-described processing by the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit can analyze the user's geographical location information using AI and prioritize collecting highly relevant information.

[0071] The collection unit can analyze the user's social media activities and collect related information when collecting information. The collection unit can analyze the user's social media activities and collect related information when collecting information. The collection unit can collect related information, for example, based on information shared by the user on social media. For example, the collection unit can analyze content posted by the user on social media and collect related business information. The collection unit can also analyze the activities of the user's social media followers and friends and collect related information. For example, the collection unit can collect related business information based on information shared by the user's followers. The collection unit can also collect information related to topics in which the user showed interest on social media. For example, if the user showed interest in "marketing," the collection unit can collect business information related to marketing. This allows for efficient collection of highly relevant information by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using AI, or can be performed without using AI. For example, the collection unit can analyze the user's social media activities and collect related information using AI.

[0072] The translation unit can estimate the user's emotion and adjust the translation expression based on the estimated user emotion. The translation unit can estimate the user's emotion and adjust the translation expression based on the estimated user emotion. The translation unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the translation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The translation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the translation unit can analyze the tone and speed of the voice and calculate an emotion score. The translation unit can also analyze the user's text data and estimate the emotion. For example, the translation unit can analyze the user's written text and calculate an emotion score. This enables more appropriate translation by adjusting the translation expression based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the translation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation unit may use a generation AI to estimate the user's emotions and adjust the way the translation is expressed.

[0073] The translation unit can adjust the level of detail of the translation based on the importance of the information during translation. The translation unit can adjust the level of detail of the translation based on the importance of the information during translation. For example, the translation unit performs a detailed translation for important information. For example, the translation unit translates important procedures and manuals in detail for the business. The translation unit can also perform only a basic translation for less important information. For example, the translation unit translates general notices briefly. The translation unit can also adjust the scope of the translation based on the importance of the information. For example, the translation unit adjusts the level of detail of the translation based on the impact and urgency of the business. This enables efficient translation by adjusting the level of detail of the translation based on the importance of the information. Some or all of the above-mentioned processing in the translation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation unit can evaluate the importance of the information using a generation AI and adjust the level of detail of the translation.

[0074] The translation unit can apply different translation algorithms depending on the category of information during translation. The translation unit can apply different translation algorithms depending on the category of information during translation. For example, for technical information, the translation unit uses a translation algorithm that corresponds to technical terminology. For example, when translating technical documents or technical manuals, the translation unit applies an algorithm that corresponds to technical terminology. The translation unit can also use a concise and easy-to-understand translation algorithm for general information. For example, the translation unit applies a concise algorithm when translating general notices or news articles. The translation unit can also use a translation algorithm specialized for the business for business-specific information. For example, the translation unit applies a business-specific algorithm when translating business procedure manuals or business manuals. This allows for more appropriate translation by applying a translation algorithm depending on the category of information. Some or all of the above-mentioned processing in the translation unit can be performed using a generation AI, or can be performed without using a generation AI. For example, the translation unit can identify the category of information using a generation AI and apply an appropriate translation algorithm.

[0075] The translation unit can estimate the user's emotion and adjust the length of the translation based on the estimated user's emotion. The translation unit can estimate the user's emotion and adjust the length of the translation based on the estimated user's emotion. The translation unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the translation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The translation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the translation unit can analyze the tone and speed of the voice and calculate an emotion score. The translation unit can also analyze the user's text data and estimate the emotion. For example, the translation unit can analyze the user's written text and calculate an emotion score. This enables more appropriate translation by adjusting the length of the translation based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the translation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation unit may use a generation AI to estimate the user's emotions and adjust the length of the translation.

[0076] The translation department can determine the priority of translation based on the time of submission of information during translation. The translation department determines the priority of translation based on the time of submission of information during translation. For example, the translation department gives the highest priority to urgent information. For example, the translation department prioritizes the translation of business procedure manuals whose submission deadline is approaching. The translation department can also prioritize the translation of information whose submission deadline is approaching. For example, the translation department quickly translates notices whose submission deadline is approaching. The translation department can also postpone the translation of information whose submission deadline is more flexible. For example, the translation department postpones the translation of business manuals whose submission deadline is further away. In this way, determining the priority of translation based on the time of submission of information enables efficient translation. Some or all of the above-mentioned processes in the translation department may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation department can evaluate the time of submission of information using a generation AI and determine the priority of translation.

[0077] The translation unit can adjust the order of translation based on the relevance of information during translation. The translation unit adjusts the order of translation based on the relevance of information during translation. For example, the translation unit prioritizes translating highly relevant information. For example, the translation unit prioritizes translating procedure manuals and manuals that are highly relevant to the work. The translation unit can also postpone translating less relevant information. For example, the translation unit postpones translating general notices. The translation unit can also adjust the order of translation based on the relevance of information. For example, the translation unit adjusts the order of translation based on the content of the work and the progress of the project. This enables efficient translation by adjusting the order of translation based on the relevance of information. Some or all of the above-mentioned processing in the translation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the translation unit can evaluate the relevance of information using a generation AI and adjust the order of translation.

[0078] The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the method of providing information based on the estimated user's emotion. The providing unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice and calculate an emotion score. The providing unit can also analyze the user's text data and estimate the emotion. For example, the providing unit can analyze the user's written text and calculate an emotion score. This enables more appropriate information to be provided by adjusting the method of providing information according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may use AI to estimate the user's emotions and adjust the method of providing information.

[0079] The providing unit can select the most appropriate information provision method by referring to the user's past information browsing history when providing information. The providing unit can select the most appropriate information provision method by referring to the user's past information browsing history when providing information. For example, the providing unit can prioritize providing information that the user has frequently browsed in the past. For example, the providing unit can prioritize providing business procedures and manuals that the user has frequently browsed in the past. The providing unit can also select the optimal information provision method from the user's past browsing history. For example, the providing unit can analyze the format and provision method of information that the user has previously browsed and select the optimal method. The providing unit can also adjust the information provision order based on the user's past browsing history. For example, the providing unit can adjust the provision order based on the importance and relevance of information that the user has previously browsed. This makes it possible to provide optimal information by referring to the user's past information browsing history. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can analyze the user's past information browsing history using AI to select the optimal information provision method.

[0080] The provision unit can adjust the level of detail to be provided based on the importance of the information when providing the information. The provision unit can adjust the level of detail to be provided based on the importance of the information when providing the information. For example, in the case of important information, the provision unit provides the information in a manner including detailed explanations. For example, the provision unit can provide detailed procedures or manuals that are important for the business. In addition, the provision unit can provide only basic information for information of low importance. For example, the provision unit can provide general notices in a concise manner. In addition, the provision unit can adjust the scope of the information to be provided based on the importance of the information. For example, the provision unit adjusts the level of detail to be provided based on the impact and urgency of the business. This enables efficient information provision by adjusting the level of detail to be provided based on the importance of the information. Some or all of the above-mentioned processing in the provision unit may be performed using AI, or may be performed without using AI. For example, the provision unit can evaluate the importance of the information using AI and adjust the level of detail to be provided.

[0081] The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. The providing unit can estimate the user's emotions and determine the priority of information provision based on the estimated user emotions. The providing unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the providing unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit can analyze the tone and speed of the voice and calculate an emotion score. The providing unit can also analyze the user's text data and estimate the emotions. For example, the providing unit can analyze the user's written text and calculate an emotion score. This enables more appropriate information provision by determining the priority of information provision based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may use AI to estimate the user's emotions and determine the priority of information provision.

[0082] The providing unit can select the optimal information providing method by taking into consideration the user's device information when providing information. The providing unit selects the optimal information providing method by taking into consideration the user's device information when providing information. For example, if the user is using a smartphone, the providing unit selects a information providing method that matches the screen size. For example, the providing unit selects an information display method optimized for the small screen of a smartphone. Furthermore, if the user is using a tablet, the providing unit can also select a information providing method optimized for a large screen. For example, the providing unit selects an information display method that matches the large screen of a tablet. Furthermore, if the user is using a desktop, the providing unit can also select a method for providing detailed information. For example, the providing unit selects an information display method optimized for the wide screen of a desktop. This enables optimal information provision by taking into consideration the user's device information. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can analyze the user's device information using AI and select the optimal information providing method.

[0083] When providing information, the providing unit can prioritize providing highly relevant information by taking into account the user's geographical location information. When providing information, the providing unit prioritizes providing highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the providing unit prioritizes providing information related to that area. For example, when the user is in a specific city, the providing unit provides business information related to that city. Furthermore, when the user is traveling, the providing unit can prioritize providing information related to the user's destination. For example, when the user is on a business trip, the providing unit provides business information related to the business trip destination. Furthermore, when the user is in a specific location, the providing unit can prioritize providing information related to that location. For example, when the user is in an office, the providing unit provides business information related to the office. In this way, by taking into account the user's geographical location information, highly relevant information can be efficiently provided. Some or all of the above-described processing by the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can analyze the user's geographical location information using AI and prioritize providing highly relevant information.

[0084] The learning unit can estimate a user's emotions and select training data based on the estimated user emotions. The learning unit can estimate a user's emotions and select training data based on the estimated user emotions. The learning unit estimates a user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the learning unit can capture a user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The learning unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the learning unit can analyze the tone and speed of the voice and calculate an emotion score. The learning unit can also analyze the user's text data and estimate the emotion. For example, the learning unit can analyze the user's written sentences and calculate an emotion score. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit may use a generation AI to estimate a user's emotions and select learning data.

[0085] The learning unit can optimize the learning algorithm by referring to past learning data during learning. The learning unit optimizes the learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on past learning data. For example, the learning unit selects an optimal learning algorithm based on past translation results and user feedback. The learning unit can also analyze past learning data to identify improvements to the learning algorithm. For example, the learning unit analyzes past learning data to find improvements to improve the efficiency of the learning algorithm. The learning unit can also adjust parameters of the learning algorithm by referring to past learning data. For example, the learning unit optimizes parameters of the learning algorithm based on past learning data. In this way, the optimal learning algorithm can be applied by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can analyze past learning data using a generation AI to optimize the learning algorithm.

[0086] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. The learning unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the learning unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The learning unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the learning unit can analyze the tone and speed of the voice and calculate an emotion score. The learning unit can also analyze the user's text data and estimate the emotions. For example, the learning unit can analyze the user's written sentences and calculate an emotion score. This enables efficient learning by adjusting the frequency of learning according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the learning unit may use the generation AI to estimate the user's emotions and adjust the frequency of learning.

[0087] The learning unit can weight the learning data based on the time of submission of the information during learning. The learning unit weights the learning data based on the time of submission of the information during learning. For example, the learning unit weights the learning data based on the time of submission of the information when the submission deadline is approaching. For example, the learning unit prioritizes learning of business procedure manuals with an approaching submission deadline. The learning unit can also weight the learning data based on the time of submission of information with more time to spare. For example, the learning unit postpones learning of business manuals with a more distant submission deadline. The learning unit can also adjust the weighting of the learning data depending on the time of submission. For example, the learning unit adjusts the weighting of the learning data based on the progress and urgency of the work. As a result, weighting the learning data based on the time of submission of the information enables efficient learning. Some or all of the above-described processing in the learning unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the learning unit can evaluate the time of submission of the information using a generation AI and weight the learning data.

[0088] The verification unit can estimate the user's emotions and adjust the method for verifying the translation quality based on the estimated user emotions. The verification unit can estimate the user's emotions and adjust the method for verifying the translation quality based on the estimated user emotions. The verification unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the verification unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The verification unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the verification unit can analyze the tone and speed of the voice and calculate an emotion score. The verification unit can also analyze the user's text data and estimate the emotions. For example, the verification unit can analyze the user's written text and calculate an emotion score. This enables more appropriate quality verification by adjusting the method for verifying the translation quality according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the verification unit may be performed using or without the use of a generation AI. For example, the verification unit may use a generation AI to estimate the user's emotions and adjust the method for verifying the quality of the translation.

[0089] The verification unit can optimize the verification algorithm by referring to past verification data during verification. The verification unit optimizes the verification algorithm by referring to past verification data during verification. The verification unit, for example, selects an optimal verification algorithm based on past verification data. For example, the verification unit selects an optimal verification algorithm based on past translation results and user feedback. The verification unit can also analyze past verification data to identify improvements to the verification algorithm. For example, the verification unit analyzes past verification data to find improvements to improve the efficiency of the verification algorithm. The verification unit can also adjust parameters of the verification algorithm by referring to past verification data. For example, the verification unit optimizes parameters of the verification algorithm based on past verification data. In this way, the optimal verification algorithm can be applied by referring to the past verification data. Some or all of the above-mentioned processing in the verification unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the verification unit can analyze past verification data using a generation AI to optimize the verification algorithm.

[0090] The verification unit can estimate the user's emotions and adjust the frequency of verification based on the estimated user emotions. The verification unit can estimate the user's emotions and adjust the frequency of verification based on the estimated user emotions. The verification unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the verification unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The verification unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the verification unit can analyze the tone and speed of the voice and calculate an emotion score. The verification unit can also analyze the user's text data and estimate the emotions. For example, the verification unit can analyze the user's written text and calculate an emotion score. This enables efficient verification by adjusting the frequency of verification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function using, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the verification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the verification unit may use the generation AI to estimate the user's emotions and adjust the frequency of verification.

[0091] The verification unit can weight the verification data based on the time of submission of the information during verification. The verification unit weights the verification data based on the time of submission of the information during verification. For example, the verification unit weights the verification data based on the time of submission of the information when the submission deadline is approaching. For example, the verification unit prioritizes verification of business procedure manuals whose submission deadline is approaching. The verification unit can also weight the information with less time to submit by lowering the weight. For example, the verification unit postpones verification of business manuals whose submission deadline is far away. The verification unit can also adjust the weighting of the verification data depending on the time of submission. For example, the verification unit adjusts the weighting of the verification data based on the progress and urgency of the work. As a result, weighting the verification data based on the time of submission of the information enables efficient verification. Some or all of the above-mentioned processing in the verification unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the verification unit can evaluate the time of submission of the information using a generation AI and weight the verification data. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, translation unit, and provision unit, described above, may be realized by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects work procedures and manuals using the camera 42 and microphone 38B of the smart device 14, and collects the information using the control unit 46A. The translation unit may be realized by the specific processing unit 290 of the data processing device 12, for example, and translates the collected information into multiple languages ​​using a generation AI. The provision unit provides the translated information to each employee using the output device 40 of the smart device 14, for example. The collection unit, translation unit, and provision unit may be realized by the specific processing unit 290 of the data processing device 12, for example. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, translation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects work procedures and manuals using the camera 42 and microphone 238 of the smart glasses 214, and collects the information using the control unit 46A. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates the collected information into multiple languages ​​using a generation AI. The provision unit provides the translated information to each employee using, for example, the speaker 240 of the smart glasses 214. The collection unit, translation unit, and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, translation unit, and provision unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects work procedures and manuals using the camera 42 and microphone 238 of the headset terminal 314, and collects the information using the control unit 46A. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates the collected information into multiple languages ​​using a generation AI. The provision unit provides the translated information to each employee using, for example, the display 343 of the headset terminal 314. The collection unit, translation unit, and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, translation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects work procedures and manuals using the camera 42 and microphone 238 of the robot 414, and collects the information using the control unit 46A. The translation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and translates the collected information into multiple languages ​​using a generation AI. The provision unit provides the translated information to each employee using, for example, the speaker 240 of the robot 414. The collection unit, translation unit, and provision unit may be realized, for example, by the specific processing unit 290 of the data processing device 12.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can temporarily delay providing information. If the user is relaxed, the providing unit can provide information immediately. Furthermore, if the user is concentrating, the providing unit can provide important information preferentially. This makes it possible to provide information at the optimal timing according to the user's emotions.

[0094] The collection unit can analyze the user's past behavioral history and select the most appropriate information collection method. For example, it can prioritize collection of information sources that the user has frequently accessed in the past. It can also avoid information sources that the user has ignored in the past. Furthermore, it can analyze the user's behavioral patterns and determine the optimal collection timing. This enables efficient information collection based on the user's behavioral history.

[0095] The translation unit can estimate the user's emotions and adjust the tone of the translation based on the estimated user's emotions. For example, if the user is nervous, the translation unit can translate in a relaxed tone. If the user is excited, the translation unit can translate in a calm tone. Furthermore, if the user is sad, the translation unit can translate in an encouraging tone. This makes it possible to translate in an appropriate tone according to the user's emotions.

[0096] When providing information, the providing unit can select the optimal providing method taking into consideration the remaining battery level of the user's device. For example, when the remaining battery level is low, the providing unit can provide information in a lightweight data format. When the remaining battery level is sufficient, the providing unit can provide detailed information. Furthermore, when the remaining battery level is medium, the providing unit can provide information with a medium level of detail. This makes it possible to provide optimal information according to the battery status of the device.

[0097] The collection unit can estimate the user's emotions and determine the category of information to collect based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information related to relaxation. If the user is excited, the collection unit can collect information to help the user regain their composure. Furthermore, if the user is sad, the collection unit can collect encouraging information. This makes it possible to collect appropriate information according to the user's emotions.

[0098] During translation, the translation unit can adjust the level of detail of the translation based on the reliability of the information. For example, if the information is highly reliable, a detailed translation can be performed. If the information is less reliable, a concise translation can be performed. Furthermore, if the information is medium reliable, a translation with a medium level of detail can be performed. This allows for appropriate translation according to the reliability of the information.

[0099] The providing unit can estimate the user's emotions and adjust the format of information provision based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide information in a format that visually relaxes the user. If the user is excited, the providing unit can provide information in a format that encourages the user to stay calm. Furthermore, if the user is sad, the providing unit can provide information in a format that includes an encouraging message. This makes it possible to provide information in an appropriate format according to the user's emotions.

[0100] When collecting information, the collection unit can select the optimal collection method taking into account the user's internet connection status. For example, if the internet connection is unstable, the collection unit can collect information in a lightweight data format. If the internet connection is stable, the collection unit can collect detailed information. Furthermore, if the internet connection is medium, the collection unit can collect information with a medium level of detail. This makes it possible to collect optimal information according to the internet connection status.

[0101] The translation unit can estimate the user's emotions and adjust the translation speed based on the estimated user emotions. For example, if the user is in a hurry, the translation unit can translate quickly. If the user is relaxed, the translation unit can translate in detail. Furthermore, if the user is concentrating, the translation unit can prioritize translating important information. This enables translation at an appropriate speed according to the user's emotions.

[0102] When providing information, the providing unit can select the optimal delivery method by referring to the user's past feedback. For example, it can give priority to delivery methods that the user has previously rated highly. It can also avoid delivery methods that the user has previously rated poorly. Furthermore, it can analyze the user's feedback and identify areas for improvement in the delivery method. This makes it possible to provide optimal information based on the user's past feedback.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The collection department collects information related to internal operations. The collection department collects information such as work procedures, manuals, and internal communications. For example, the collection department can collect information in the form of PDF files, Word documents, emails, etc. The collection department can also use AI to automatically collect information related to operations. For example, the collection department extracts and collects information from internal databases and document management systems. Step 2: The translation department translates the information collected by the collection department into multiple languages. Using generation AI, the translation department analyzes the collected information and provides translations appropriate for each language. For example, the translation department translates a Japanese work procedure manual into English, Chinese, Spanish, etc. The generation AI is trained to handle technical terms and expressions specific to the work. For example, the generation AI can understand the contents of the work procedure manual and provide appropriate translations. Step 3: The provision department provides the information translated by the translation department to each employee. The provision department provides the translated information, for example, by email or on an internal portal site. For example, the provision department sends the translated work procedure manual by email so that employees can view the procedure manual in their own language. The provision department also posts the translated information on the internal portal site so that employees can refer to the information they need at any time.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 information about the company's operations; a translation unit that translates the information collected by the collection unit into multiple languages; a providing unit that provides each employee with the information translated by the translation unit; Equipped with A system characterized by:

2. The collecting unit Collect information on work procedures, manuals, and internal communications 2. The system of claim 1.

3. The translation unit Equipped with a learning department for learning specialized terminology and expressions specific to the job 2. The system of claim 1.

4. The translation unit Translate collected information into English, Chinese, and Spanish 2. The system of claim 1.

5. The providing unit Provide translated information via email or internal portal site 2. The system of claim 1.

6. The translation unit Equipped with a verification department to check the quality of the translation 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit Analyze past information collection history and select the most appropriate collection method 2. The system of claim 1.

9. The collecting unit When collecting information, adjust the level of detail based on business priorities.

2. The system of claim 1.

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