System

Abstract

An object of the system according to the embodiment is to efficiently create a drawing of an office layout in consideration of laws and regulations and to uniformize quality.SOLUTION: A system according to an embodiment includes a database creation unit, an analysis unit, a generation unit, a proposal unit, a correction unit, and a quality check unit. The database creation unit creates a database of laws and regulations. The analysis unit analyzes the laws and regulations stored in the database by the database unit. The generation unit automatically generates a drawing of the office layout based on the laws and regulations analyzed by the analysis unit. The proposal unit optimizes the office layout generated by the generation unit and proposes the optimized office layout to the user. The correction unit analyzes the user's correction to the layout proposed by the proposal unit, and automatically corrects the layout so as to conform to laws and regulations. A quality check part checks the quality of the finally prepared drawing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, creating office layout drawings requires advanced know-how to take into account laws, regulations, and internal rules, and there is room for improvement in terms of standardizing quality and efficiency.

[0005] The system according to the embodiment aims to improve the efficiency of creating office layout drawings that take into account laws, regulations, and internal rules, and to standardize the quality. [Means for solving the problem]

[0006] The system according to the embodiment includes a database creation unit, an analysis unit, a generation unit, a proposal unit, a correction unit, and a quality check unit. The database creation unit creates a database of laws and regulations and internal rules. The analysis unit analyzes the laws and internal rules created in the database by the database creation unit. The generation unit automatically generates office layout drawings based on the laws and internal rules analyzed by the analysis unit. The proposal unit optimizes the office layout created by the generation unit and proposes it to the user. The correction unit analyzes user modifications to the layout proposed by the proposal unit and automatically corrects it to comply with the laws and internal rules. The quality check unit checks the quality of the final drawing created. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of creating office layout drawings that take into account laws, regulations, and internal rules, and can standardize the quality. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The office layout creation system according to an embodiment of the present invention is a system that streamlines the creation of office layout drawings and standardizes quality. This system uses AI to provide the know-how required to create drawings while taking into account laws, regulations, and internal rules. As a result, the office layout creation system can improve work efficiency and standardize quality.

[0029] An office layout creation system according to an embodiment includes a database creation unit, an analysis unit, a generation unit, a proposal unit, a correction unit, and a quality check unit. The database creation unit creates a database of laws and regulations. For example, laws and regulations such as the Building Standards Act, the Fire Service Act, and the Industrial Safety and Health Act, as well as company-specific internal regulations, are registered in the database. The analysis unit analyzes the laws and regulations and internal regulations created in the database by the database creation unit. For example, a generation AI analyzes the scope of application of the laws and regulations and internal regulations using text analysis technology. The generation unit automatically generates an office layout drawing based on the laws and internal regulations analyzed by the analysis unit. For example, the generation AI generates an optimal layout based on user instructions. The proposal unit optimizes the office layout created by the generation unit and proposes it to the user. For example, the generation AI proposes widening aisles by rearranging desks. The correction unit analyzes user modifications to the layout proposed by the proposal unit and automatically modifies it to comply with the laws and internal regulations. For example, the generation AI changes the location of conference rooms and modifies the drawing to comply with the laws and internal regulations. The quality check department checks the quality of the final drawings created. For example, the generation AI checks whether the drawings comply with the Building Standards Act. This allows the office layout creation system to improve work efficiency and standardize quality. For example, it can quickly create appropriate layouts that take laws and internal regulations into consideration, improving work efficiency. Furthermore, standardizing the quality of drawings reduces the risk of violating laws and internal regulations.

[0030] The database creation unit tracks the change history of laws and internal regulations, allowing the analysis unit to perform analysis based on the latest information. The database creation unit, for example, automatically tracks the change history of laws and internal regulations and builds a system that performs analysis based on the latest information. For example, the database creation unit records the revision date and content of laws and regulations in a database, and the generation AI performs analysis based on that information. The database creation unit also adds a function that updates the change history of laws and internal regulations in real time and reflects the latest information. For example, when laws and regulations are revised, the database is automatically updated, and the generation AI performs analysis based on that information. The database creation unit also visually displays the change history of laws and internal regulations, allowing users to understand the changes at a glance. For example, the change history is displayed in a timeline format, and the generation AI performs analysis based on that information. This makes it possible to perform analysis based on the latest laws and internal regulations, allowing drawings to be created that always reflect the latest information.

[0031] The analysis unit can collect expert opinions on the interpretation of laws, regulations, and internal rules and reflect them in the analysis. For example, the analysis unit builds a system that collects expert opinions on the interpretation of laws, regulations, and internal rules and registers them in a database. For example, expert comments and explanations are saved as text data, and the generation AI performs analysis based on that information. The analysis unit also adds a function that collects expert opinions in real time and reflects that information in the analysis by the generation AI. For example, interpretations provided online by experts are automatically registered in a database, and the generation AI performs analysis based on that information. The analysis unit also visually displays expert opinions, allowing users to understand the interpretation content at a glance. For example, expert comments are displayed in charts and graphs, and the generation AI performs analysis based on that information. In this way, by reflecting expert opinions, more accurate interpretation of laws, regulations, and internal rules is possible.

[0032] The database creation department can apply the database of laws and regulations to other tasks, promoting its use in a wide range of fields. For example, the database creation department builds a system that applies the database of laws and regulations to other tasks, such as architectural design and urban planning. For example, it adds a function that automatically checks for compliance with laws and regulations in architectural design. The database creation department also develops an interface for applying the database of laws and regulations to different tasks. For example, it adds a function in urban planning that makes proposals that take laws and regulations into consideration. The database creation department also provides training programs for applying the database of laws and regulations to other tasks. For example, it enables architectural design and urban planning professionals to use the database. This allows the database of laws and regulations to be applied to other tasks, making it possible to use it in a wide range of fields.

[0033] The database creation department can automatically translate laws and internal regulations into different languages, enabling international use. For example, the database creation department builds a system that automatically translates databased laws and internal regulations into different languages. For example, it adds a function to translate into multiple languages, such as English, French, and Chinese. The database creation department also develops a platform that allows international users to access the automatically translated laws and internal regulations. For example, it provides search and viewing functions in different languages. The database creation department also provides training programs for international users based on the translated laws and internal regulations. For example, it provides teaching materials for learning how to interpret and apply laws in different languages. This allows international use by automatically translating into different languages.

[0034] The generation unit can learn the user's past layout creation history and automatically generate layouts tailored to individual preferences. For example, the generation unit stores the user's past layout creation history in a database and builds a system that automatically generates layouts tailored to individual preferences based on that data. For example, the generation unit learns past layout patterns and the generation AI makes suggestions based on that information. The generation unit also adds a function that analyzes the layout creation history and identifies the user's preferences and trends. For example, for a user who prefers a specific layout or design, the generation unit proposes a layout that reflects those trends. The generation unit also visually displays the user's past layout creation history, and the generation AI automatically generates a layout tailored to individual preferences based on that information. For example, the generation unit displays past layouts in a timeline format, and the generation AI makes suggestions based on that information. In this way, by learning the user's past history, it is possible to automatically generate layouts tailored to individual preferences.

[0035] The generation unit can analyze business process data to propose an optimal layout according to the office's intended use and business content. The generation unit, for example, builds a system that analyzes business process data to propose an optimal layout according to the office's intended use and business content. For example, the generation unit analyzes business flow and work flow, and the generation AI makes proposals based on that information. The generation unit also adds a function that collects business process data in real time and reflects it in optimizing the office layout. For example, the generation unit analyzes the progress of work and work efficiency, and the generation AI makes proposals based on that information. The generation unit also visually displays business process data, and the generation AI optimizes the office layout based on that information. For example, the generation unit displays business flow in charts and graphs, and the generation AI makes proposals based on that information. In this way, by analyzing business process data, it is possible to propose an optimal layout according to the office's intended use and business content.

[0036] The generation unit can also be applied to the automatic generation of layouts in spaces other than offices. The generation unit will build a system that can be applied to the automatic generation of layouts in spaces other than offices (such as homes and commercial facilities). For example, it will add a function to automatically generate home floor plans and commercial facility layouts. The generation unit will also develop an interface to support the automatic generation of layouts for different spaces. For example, it will add a function to propose layouts according to the characteristics of homes and commercial facilities. The generation unit will also provide a training program for application to the automatic generation of layouts in spaces other than offices. For example, it will enable experts in homes and commercial facilities to use the generation AI. This will enable it to be applied to spaces other than offices, enabling it to be used in a wide range of fields.

[0037] The generation unit can automatically suggest the placement of furniture and equipment when generating a layout, providing a comprehensive spatial design. For example, the generation unit builds a system that automatically suggests the placement of furniture and equipment when generating a layout. For example, it adds a function to automatically generate the placement of desks, chairs, and conference rooms. The generation unit also develops an interface for providing a comprehensive spatial design that takes into account the placement of furniture and equipment. For example, it visually displays the placement of furniture and equipment, and the generation AI makes suggestions based on that information. The generation unit also provides a training program for automatically suggesting the placement of furniture and equipment. For example, it allows office design experts to utilize the generation AI. This makes it possible to provide a comprehensive spatial design that takes into account the placement of furniture and equipment.

[0038] The proposal unit can make proposals that take energy efficiency and environmental impact into consideration when optimizing a layout. The proposal unit, for example, builds a system that makes proposals that take energy efficiency into consideration when optimizing a layout. For example, it makes proposals to optimize the placement of lighting and air conditioning and reduce energy consumption. The proposal unit also develops an interface for making layout proposals that take environmental impact into consideration. For example, it adds a function that suggests the placement of recyclable materials and eco-friendly equipment. The proposal unit also visually displays energy efficiency and environmental impact, allowing users to optimize the layout based on that information. For example, it displays energy consumption and environmental impact in graphs and charts, and the generation AI makes proposals based on that information. In this way, a sustainable layout can be achieved by making proposals that take energy efficiency and environmental impact into consideration.

[0039] The suggestion unit can collect user feedback in real time and continuously optimize the layout based on that. The suggestion unit, for example, builds a system that collects user feedback in real time and continuously optimizes the layout based on that. For example, it collects feedback using an online survey or comment function. The suggestion unit also adds a function that analyzes user feedback and identifies areas for improvement in the layout. For example, it adjusts the layout arrangement and design based on user opinions. The suggestion unit also visually displays the feedback collected in real time, allowing the user to optimize the layout based on that information. For example, it displays the feedback content in charts or graphs, and the generation AI makes suggestions based on that information. In this way, by collecting user feedback in real time and continuously optimizing, it is possible to provide a layout that meets the user's needs.

[0040] The proposal unit can apply the layout optimization proposals to other tasks. For example, the proposal unit builds a system that applies the layout optimization proposals to other tasks, such as the layout of production lines and the design of logistics centers. For example, it makes proposals to improve the efficiency of production lines and optimize the work flow lines of logistics centers. The proposal unit also develops interfaces for application to different tasks. For example, it adds a function to make layout proposals according to the characteristics of production lines and logistics centers. The proposal unit also provides training programs for applying the layout optimization proposals to other tasks. For example, it enables experts in the manufacturing and logistics industries to utilize the generative AI. This allows the layout optimization proposals to be applied to other tasks, making it possible to use the system in a wide range of fields.

[0041] The proposal unit can customize the optimization proposals to suit the characteristics of different cultures and regions. For example, the proposal unit builds a system that customizes the optimization proposals to suit the characteristics of different cultures and regions. For example, it makes layout proposals that take into account the culture and customs of each region. The proposal unit also develops an interface to reflect the characteristics of different cultures and regions. For example, it adds a function to make proposals that take into account the building standards and design preferences of each region. The proposal unit also provides a training program to customize the optimization proposals to suit different cultures and regions. For example, it provides teaching materials to learn about the characteristics of each region. This makes it possible to make more appropriate proposals by customizing to suit the characteristics of different cultures and regions.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The office layout creation system can further include a health monitoring unit that monitors the user's health status and proposes a layout that takes health into consideration. For example, it can propose a layout that encourages the user to stand up periodically to prevent the user from sitting too much. It can also propose a layout that supports the user's health by optimizing the intake of natural light and air flow. It can also monitor the user's stress level and propose the creation of a space where the user can relax. This allows the office layout creation system to provide a layout that takes the user's health into consideration.

[0044] The Database Creation Department not only tracks the change history of laws and internal regulations, but can also analyze the interrelationships with other related laws and regulations. For example, it can analyze the interrelationships between the Building Standards Act and the Fire Service Act and propose layouts that comply with both laws. It can also compare the laws of different countries and regions and propose layouts that are suitable for international projects. It can also predict the impact that changes in laws and internal regulations will have on other operations and propose countermeasures in advance. This allows the Database Creation Department to support more comprehensive legal compliance.

[0045] The analysis unit not only collects expert opinions on the interpretation of laws and regulations, but also performs analysis that takes into account the user's business operations and industry-specific requirements. For example, in the IT industry, the analysis unit can make proposals for optimizing server room placement and cable management. In the medical industry, the analysis unit can also make proposals for ensuring efficient traffic flow while protecting patient privacy. Furthermore, the analysis unit can analyze the user's business processes and make layout proposals to improve business efficiency. This allows the analysis unit to provide more specific and practical layout proposals.

[0046] The Database Creation Department can apply the database of laws and regulations to other tasks, promoting its use in a wide range of fields. For example, it can be applied not only to architectural design and urban planning, but also to the design of educational and medical facilities. The Database Creation Department also develops interfaces for applying the database of laws and regulations to different tasks. For example, in educational facilities, it can add a function that suggests optimizing classroom layouts and evacuation routes. Furthermore, the Database Creation Department provides training programs for applying the database of laws and regulations to other tasks. For example, it can enable experts in educational and medical facilities to use the database. This will enable the database of laws and regulations to be applied to other tasks, making it possible to use it in a wide range of fields.

[0047] The database creation department can automatically translate laws and internal regulations into different languages, enabling international use. For example, it adds a function to translate databased laws and internal regulations into multiple languages, such as English, French, and Chinese. The database creation department also develops a platform that allows international users to access the automatically translated laws and internal regulations. For example, it can provide search and viewing functions in different languages. Furthermore, the database creation department provides training programs for international users based on the translated laws and internal regulations. For example, it can provide teaching materials for learning how to interpret and apply laws in different languages. This allows international use by automatically translating into different languages.

[0048] The generation unit can learn the user's past layout creation history and automatically generate layouts tailored to individual preferences. For example, the generation unit can learn the user's past layout patterns and make suggestions based on that information. The generation unit also adds a function to analyze the layout creation history and identify the user's preferences and trends. For example, for a user who prefers a specific layout or design, it can suggest a layout that reflects those trends. Furthermore, the generation unit visually displays the user's past layout creation history, and the generation AI can automatically generate a layout tailored to individual preferences based on that information. For example, past layouts can be displayed in a timeline format, and the generation AI can make suggestions based on that information. In this way, by learning the user's past history, it is possible to automatically generate layouts tailored to individual preferences.

[0049] The generation unit can analyze business process data to propose the optimal layout according to the office's intended use and business content. For example, it can analyze business flow and work flow, and the generation AI can make proposals based on that information. The generation unit also adds a function to collect business process data in real time and reflect it in optimizing the office layout. For example, it can analyze the progress of work and work efficiency, and the generation AI can make proposals based on that information. Furthermore, the generation unit visually displays business process data, and the generation AI can optimize the office layout based on that information. For example, it can display business flow in charts and graphs, and the generation AI can make proposals based on that information. In this way, by analyzing business process data, it is possible to propose the optimal layout according to the office's intended use and business content.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The database creation department creates a database of laws and internal regulations. For example, laws such as the Building Standards Act, Fire Service Act, and Industrial Safety and Health Act, as well as internal regulations specific to the company, are registered in the database. Step 2: The analysis unit analyzes the laws and regulations and internal rules that have been compiled into a database by the database creation unit. For example, the generation AI uses text analysis technology to analyze the scope of application of the laws and regulations and internal rules. Step 3: The generation unit automatically generates office layout drawings based on the laws and regulations analyzed by the analysis unit. For example, the generation AI generates the optimal layout based on the user's instructions. Step 4: The proposal unit optimizes the office layout generated by the generation unit and proposes it to the user. For example, the generation AI may suggest changing the desk arrangement to widen the aisles. Step 5: The correction unit analyzes the user's modifications to the layout proposed by the proposal unit and automatically corrects it to comply with laws and regulations. For example, the generation AI changes the location of conference rooms and corrects the drawing to comply with laws and regulations. Step 6: The quality check section checks the quality of the final drawing. For example, the generation AI checks whether the drawing complies with the Building Standards Act.

[0052] (Example 2) The office layout creation system according to an embodiment of the present invention is a system that streamlines the creation of office layout drawings and standardizes quality. This system uses AI to provide the know-how required to create drawings while taking into account laws, regulations, and internal rules. As a result, the office layout creation system can improve work efficiency and standardize quality.

[0053] An office layout creation system according to an embodiment includes a database creation unit, an analysis unit, a generation unit, a proposal unit, a correction unit, and a quality check unit. The database creation unit creates a database of laws and regulations. For example, laws and regulations such as the Building Standards Act, the Fire Service Act, and the Industrial Safety and Health Act, as well as company-specific internal regulations, are registered in the database. The analysis unit analyzes the laws and regulations and internal regulations created in the database by the database creation unit. For example, a generation AI analyzes the scope of application of the laws and regulations and internal regulations using text analysis technology. The generation unit automatically generates an office layout drawing based on the laws and internal regulations analyzed by the analysis unit. For example, the generation AI generates an optimal layout based on user instructions. The proposal unit optimizes the office layout created by the generation unit and proposes it to the user. For example, the generation AI proposes widening aisles by rearranging desks. The correction unit analyzes user modifications to the layout proposed by the proposal unit and automatically modifies it to comply with the laws and internal regulations. For example, the generation AI changes the location of conference rooms and modifies the drawing to comply with the laws and internal regulations. The quality check department checks the quality of the final drawings created. For example, the generation AI checks whether the drawings comply with the Building Standards Act. This allows the office layout creation system to improve work efficiency and standardize quality. For example, it can quickly create appropriate layouts that take laws and internal regulations into consideration, improving work efficiency. Furthermore, standardizing the quality of drawings reduces the risk of violating laws and internal regulations.

[0054] The database creation unit tracks the change history of laws and internal regulations, allowing the analysis unit to perform analysis based on the latest information. The database creation unit, for example, automatically tracks the change history of laws and internal regulations and builds a system that performs analysis based on the latest information. For example, the database creation unit records the revision date and content of laws and regulations in a database, and the generation AI performs analysis based on that information. The database creation unit also adds a function that updates the change history of laws and internal regulations in real time and reflects the latest information. For example, when laws and regulations are revised, the database is automatically updated, and the generation AI performs analysis based on that information. The database creation unit also visually displays the change history of laws and internal regulations, allowing users to understand the changes at a glance. For example, the change history is displayed in a timeline format, and the generation AI performs analysis based on that information. This makes it possible to perform analysis based on the latest laws and internal regulations, allowing drawings to be created that always reflect the latest information.

[0055] The analysis unit can collect expert opinions on the interpretation of laws, regulations, and internal rules and reflect them in the analysis. For example, the analysis unit builds a system that collects expert opinions on the interpretation of laws, regulations, and internal rules and registers them in a database. For example, expert comments and explanations are saved as text data, and the generation AI performs analysis based on that information. The analysis unit also adds a function that collects expert opinions in real time and reflects that information in the analysis by the generation AI. For example, interpretations provided online by experts are automatically registered in a database, and the generation AI performs analysis based on that information. The analysis unit also visually displays expert opinions, allowing users to understand the interpretation content at a glance. For example, expert comments are displayed in charts and graphs, and the generation AI performs analysis based on that information. In this way, by reflecting expert opinions, more accurate interpretation of laws, regulations, and internal rules is possible.

[0056] The analysis unit uses the emotion estimation function to analyze the user's emotions regarding the application of laws, regulations, and internal rules, and can focus on analyzing the areas where the user feels anxious. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotions regarding the application of laws, regulations, and internal rules in real time. For example, it analyzes the user's facial expressions and voice to identify the areas where the user feels anxious. The analysis unit also adds a function that focuses on analyzing factors that cause anxiety regarding the application of laws, regulations, and internal rules based on the user's emotion data. For example, it prioritizes analysis of areas with high anxiety scores, and the generation AI makes suggestions based on that information. The analysis unit also visually displays the emotion estimation data so that the user can see at a glance the areas that cause anxiety. For example, it displays the anxiety score in a color-coded manner, and the generation AI performs analysis based on that information. This reduces the user's anxiety and provides a system that can be used with greater peace of mind.

[0057] The database creation department can apply the database of laws and regulations to other tasks, promoting its use in a wide range of fields. For example, the database creation department builds a system that applies the database of laws and regulations to other tasks, such as architectural design and urban planning. For example, it adds a function that automatically checks for compliance with laws and regulations in architectural design. The database creation department also develops an interface for applying the database of laws and regulations to different tasks. For example, it adds a function in urban planning that makes proposals that take laws and regulations into consideration. The database creation department also provides training programs for applying the database of laws and regulations to other tasks. For example, it enables architectural design and urban planning professionals to use the database. This allows the database of laws and regulations to be applied to other tasks, making it possible to use it in a wide range of fields.

[0058] The database creation department can automatically translate laws and internal regulations into different languages, enabling international use. For example, the database creation department builds a system that automatically translates databased laws and internal regulations into different languages. For example, it adds a function to translate into multiple languages, such as English, French, and Chinese. The database creation department also develops a platform that allows international users to access the automatically translated laws and internal regulations. For example, it provides search and viewing functions in different languages. The database creation department also provides training programs for international users based on the translated laws and internal regulations. For example, it provides teaching materials for learning how to interpret and apply laws in different languages. This allows international use by automatically translating into different languages.

[0059] The database creation unit can use the emotion estimation function to collect users' emotional reactions to changes in laws, regulations, and internal rules and predict the impact of the changes. The database creation unit, for example, uses the emotion estimation function to build a system that collects users' emotional reactions to changes in laws, regulations, and internal rules in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The database creation unit also adds a function that predicts the impact of changes in laws, regulations, and internal rules based on the user's emotional reaction data. For example, it predicts the degree of anxiety and confusion caused by the changes. The database creation unit also visually displays the emotion estimation data so that the impact of changes in laws, regulations, and internal rules on the user can be understood at a glance. For example, it displays emotion scores in different colors to predict the impact of the changes. This makes it possible to predict the impact of changes in laws, regulations, and internal rules on the user and take appropriate measures.

[0060] The generation unit can learn the user's past layout creation history and automatically generate layouts tailored to individual preferences. For example, the generation unit stores the user's past layout creation history in a database and builds a system that automatically generates layouts tailored to individual preferences based on that data. For example, the generation unit learns past layout patterns and the generation AI makes suggestions based on that information. The generation unit also adds a function that analyzes the layout creation history and identifies the user's preferences and trends. For example, for a user who prefers a specific layout or design, the generation unit proposes a layout that reflects those trends. The generation unit also visually displays the user's past layout creation history, and the generation AI automatically generates a layout tailored to individual preferences based on that information. For example, the generation unit displays past layouts in a timeline format, and the generation AI makes suggestions based on that information. In this way, by learning the user's past history, it is possible to automatically generate layouts tailored to individual preferences.

[0061] The generation unit can analyze business process data to propose an optimal layout according to the office's intended use and business content. The generation unit, for example, builds a system that analyzes business process data to propose an optimal layout according to the office's intended use and business content. For example, the generation unit analyzes business flow and work flow, and the generation AI makes proposals based on that information. The generation unit also adds a function that collects business process data in real time and reflects it in optimizing the office layout. For example, the generation unit analyzes the progress of work and work efficiency, and the generation AI makes proposals based on that information. The generation unit also visually displays business process data, and the generation AI optimizes the office layout based on that information. For example, the generation unit displays business flow in charts and graphs, and the generation AI makes proposals based on that information. In this way, by analyzing business process data, it is possible to propose an optimal layout according to the office's intended use and business content.

[0062] The generation unit can also be applied to the automatic generation of layouts in spaces other than offices. The generation unit will build a system that can be applied to the automatic generation of layouts in spaces other than offices (such as homes and commercial facilities). For example, it will add a function to automatically generate home floor plans and commercial facility layouts. The generation unit will also develop an interface to support the automatic generation of layouts for different spaces. For example, it will add a function to propose layouts according to the characteristics of homes and commercial facilities. The generation unit will also provide a training program for application to the automatic generation of layouts in spaces other than offices. For example, it will enable experts in homes and commercial facilities to use the generation AI. This will enable it to be applied to spaces other than offices, enabling it to be used in a wide range of fields.

[0063] The generation unit can automatically suggest the placement of furniture and equipment when generating a layout, providing a comprehensive spatial design. For example, the generation unit builds a system that automatically suggests the placement of furniture and equipment when generating a layout. For example, it adds a function to automatically generate the placement of desks, chairs, and conference rooms. The generation unit also develops an interface for providing a comprehensive spatial design that takes into account the placement of furniture and equipment. For example, it visually displays the placement of furniture and equipment, and the generation AI makes suggestions based on that information. The generation unit also provides a training program for automatically suggesting the placement of furniture and equipment. For example, it allows office design experts to utilize the generation AI. This makes it possible to provide a comprehensive spatial design that takes into account the placement of furniture and equipment.

[0064] The generation unit can use the emotion estimation function to compare the user's emotional responses to different layouts and select the optimal layout. For example, the generation unit uses the emotion estimation function to build a system that compares the user's emotional responses to different layouts in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit also adds a function to select the optimal layout based on the user's emotion data. For example, it preferentially suggests layouts with high emotion scores. The generation unit also visually displays the emotion estimation data so that the user's emotional responses to different layouts can be understood at a glance. For example, it displays the emotion scores in different colors, and the generation AI selects the optimal layout based on that information. This makes it possible to select the optimal layout by comparing the user's emotional responses to different layouts.

[0065] The proposal unit can make proposals that take energy efficiency and environmental impact into consideration when optimizing a layout. The proposal unit, for example, builds a system that makes proposals that take energy efficiency into consideration when optimizing a layout. For example, it makes proposals to optimize the placement of lighting and air conditioning and reduce energy consumption. The proposal unit also develops an interface for making layout proposals that take environmental impact into consideration. For example, it adds a function that suggests the placement of recyclable materials and eco-friendly equipment. The proposal unit also visually displays energy efficiency and environmental impact, allowing users to optimize the layout based on that information. For example, it displays energy consumption and environmental impact in graphs and charts, and the generation AI makes proposals based on that information. In this way, a sustainable layout can be achieved by making proposals that take energy efficiency and environmental impact into consideration.

[0066] The suggestion unit can collect user feedback in real time and continuously optimize the layout based on that. The suggestion unit, for example, builds a system that collects user feedback in real time and continuously optimizes the layout based on that. For example, it collects feedback using an online survey or comment function. The suggestion unit also adds a function that analyzes user feedback and identifies areas for improvement in the layout. For example, it adjusts the layout arrangement and design based on user opinions. The suggestion unit also visually displays the feedback collected in real time, allowing the user to optimize the layout based on that information. For example, it displays the feedback content in charts or graphs, and the generation AI makes suggestions based on that information. In this way, by collecting user feedback in real time and continuously optimizing, it is possible to provide a layout that meets the user's needs.

[0067] The suggestion unit can use the emotion estimation function to suggest layout changes that will most satisfy the user. The suggestion unit, for example, uses the emotion estimation function to build a system that suggests layout changes that will most satisfy the user. For example, it analyzes the user's facial expressions and voice and calculates a satisfaction score. The suggestion unit also adds a function to suggest layout changes that will provide high satisfaction based on the user's emotion data. For example, it prioritizes suggesting layouts and designs with high satisfaction scores. The suggestion unit also visually displays the emotion estimation data so that the user can see at a glance which layout change will most satisfy them. For example, it displays the satisfaction score in a color-coded manner, and the generation AI makes suggestions based on that information. This makes it possible to suggest layout changes that will most satisfy the user, taking into account the user's emotions.

[0068] The proposal unit can apply the layout optimization proposals to other tasks. For example, the proposal unit builds a system that applies the layout optimization proposals to other tasks, such as the layout of production lines and the design of logistics centers. For example, it makes proposals to improve the efficiency of production lines and optimize the work flow lines of logistics centers. The proposal unit also develops interfaces for application to different tasks. For example, it adds a function to make layout proposals according to the characteristics of production lines and logistics centers. The proposal unit also provides training programs for applying the layout optimization proposals to other tasks. For example, it enables experts in the manufacturing and logistics industries to utilize the generative AI. This allows the layout optimization proposals to be applied to other tasks, making it possible to use the system in a wide range of fields.

[0069] The proposal unit can customize the optimization proposals to suit the characteristics of different cultures and regions. For example, the proposal unit builds a system that customizes the optimization proposals to suit the characteristics of different cultures and regions. For example, it makes layout proposals that take into account the culture and customs of each region. The proposal unit also develops an interface to reflect the characteristics of different cultures and regions. For example, it adds a function to make proposals that take into account the building standards and design preferences of each region. The proposal unit also provides a training program to customize the optimization proposals to suit different cultures and regions. For example, it provides teaching materials to learn about the characteristics of each region. This makes it possible to make more appropriate proposals by customizing to suit the characteristics of different cultures and regions.

[0070] The suggestion unit uses the emotion estimation function to analyze the user's emotional reactions to different optimization proposals and can make the most acceptable proposal. The suggestion unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotional reactions to different optimization proposals in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The suggestion unit also adds a function to make the most acceptable optimization proposal based on the user's emotion data. For example, it prioritizes proposals with high emotion scores. The suggestion unit also visually displays the emotion estimation data so that the user's emotional reactions to different optimization proposals can be understood at a glance. For example, it displays the emotion scores in different colors, and the generation AI makes proposals based on that information. In this way, the most acceptable proposal can be made by analyzing the user's emotional reactions.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The office layout creation system can further include a health monitoring unit that monitors the user's health status and proposes a layout that takes health into consideration. For example, it can propose a layout that encourages the user to stand up periodically to prevent the user from sitting too much. It can also propose a layout that supports the user's health by optimizing the intake of natural light and air flow. It can also monitor the user's stress level and propose the creation of a space where the user can relax. This allows the office layout creation system to provide a layout that takes the user's health into consideration.

[0073] The Database Creation Department not only tracks the change history of laws and internal regulations, but can also analyze the interrelationships with other related laws and regulations. For example, it can analyze the interrelationships between the Building Standards Act and the Fire Service Act and propose layouts that comply with both laws. It can also compare the laws of different countries and regions and propose layouts that are suitable for international projects. It can also predict the impact that changes in laws and internal regulations will have on other operations and propose countermeasures in advance. This allows the Database Creation Department to support more comprehensive legal compliance.

[0074] The analysis unit not only collects expert opinions on the interpretation of laws and regulations, but also performs analysis that takes into account the user's business operations and industry-specific requirements. For example, in the IT industry, the analysis unit can make proposals for optimizing server room placement and cable management. In the medical industry, the analysis unit can also make proposals for ensuring efficient traffic flow while protecting patient privacy. Furthermore, the analysis unit can analyze the user's business processes and make layout proposals to improve business efficiency. This allows the analysis unit to provide more specific and practical layout proposals.

[0075] The analysis unit uses the emotion estimation function to analyze the user's emotions regarding the application of laws, regulations, and internal rules, and can focus on analyzing the areas that cause the user anxiety. For example, if the user is anxious about a specific law, it can provide a detailed explanation of that law and examples of its application. It can also automatically generate and provide FAQs regarding the application of laws, regulations, and internal rules to the user to alleviate the user's anxiety. Furthermore, it can customize a training program regarding the application of laws, regulations, and internal rules based on the user's emotion data, supporting the user so that they can carry out their work with peace of mind. This allows the analysis unit to alleviate the user's anxiety and provide a system that can be used with greater peace of mind.

[0076] The Database Creation Department can apply the database of laws and regulations to other tasks, promoting its use in a wide range of fields. For example, it can be applied not only to architectural design and urban planning, but also to the design of educational and medical facilities. The Database Creation Department also develops interfaces for applying the database of laws and regulations to different tasks. For example, in educational facilities, it can add a function that suggests optimizing classroom layouts and evacuation routes. Furthermore, the Database Creation Department provides training programs for applying the database of laws and regulations to other tasks. For example, it can enable experts in educational and medical facilities to use the database. This will enable the database of laws and regulations to be applied to other tasks, making it possible to use it in a wide range of fields.

[0077] The database creation department can automatically translate laws and internal regulations into different languages, enabling international use. For example, it adds a function to translate databased laws and internal regulations into multiple languages, such as English, French, and Chinese. The database creation department also develops a platform that allows international users to access the automatically translated laws and internal regulations. For example, it can provide search and viewing functions in different languages. Furthermore, the database creation department provides training programs for international users based on the translated laws and internal regulations. For example, it can provide teaching materials for learning how to interpret and apply laws in different languages. This allows international use by automatically translating into different languages.

[0078] The database creation unit can use the emotion estimation function to collect users' emotional reactions to changes in laws, regulations, and internal rules and predict the impact of the changes. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The database creation unit also adds a function to predict the impact of changes in laws, regulations, and internal rules based on the user's emotional reaction data. For example, it can predict the degree of anxiety and confusion caused by the changes. Furthermore, the database creation unit visually displays the emotion estimation data, allowing the user to understand at a glance the impact that changes in laws, regulations, and internal rules will have on the user. For example, it can display emotion scores in different colors to predict the impact of the changes. This makes it possible to predict the impact that changes in laws, regulations, and internal rules will have on the user and take appropriate measures.

[0079] The generation unit can learn the user's past layout creation history and automatically generate layouts tailored to individual preferences. For example, the generation unit can learn the user's past layout patterns and make suggestions based on that information. The generation unit also adds a function to analyze the layout creation history and identify the user's preferences and trends. For example, for a user who prefers a specific layout or design, it can suggest a layout that reflects those trends. Furthermore, the generation unit visually displays the user's past layout creation history, and the generation AI can automatically generate a layout tailored to individual preferences based on that information. For example, past layouts can be displayed in a timeline format, and the generation AI can make suggestions based on that information. In this way, by learning the user's past history, it is possible to automatically generate layouts tailored to individual preferences.

[0080] The generation unit can analyze business process data to propose the optimal layout according to the office's intended use and business content. For example, it can analyze business flow and work flow, and the generation AI can make proposals based on that information. The generation unit also adds a function to collect business process data in real time and reflect it in optimizing the office layout. For example, it can analyze the progress of work and work efficiency, and the generation AI can make proposals based on that information. Furthermore, the generation unit visually displays business process data, and the generation AI can optimize the office layout based on that information. For example, it can display business flow in charts and graphs, and the generation AI can make proposals based on that information. In this way, by analyzing business process data, it is possible to propose the optimal layout according to the office's intended use and business content.

[0081] The generation unit can use the emotion estimation function to compare the user's emotional responses to different layouts and select the optimal layout. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. The generation unit also adds a function to select the optimal layout based on the user's emotion data. For example, it can prioritize layouts with high emotion scores. Furthermore, the generation unit visually displays the emotion estimation data, allowing the user's emotional responses to different layouts to be understood at a glance. For example, the emotion scores can be displayed in different colors, and the generation AI can select the optimal layout based on that information. This allows the optimal layout to be selected by comparing the user's emotional responses to different layouts.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The database creation department creates a database of laws and internal regulations. For example, laws such as the Building Standards Act, Fire Service Act, and Industrial Safety and Health Act, as well as internal regulations specific to the company, are registered in the database. Step 2: The analysis unit analyzes the laws and regulations and internal rules that have been compiled into a database by the database creation unit. For example, the generation AI uses text analysis technology to analyze the scope of application of the laws and regulations and internal rules. Step 3: The generation unit automatically generates office layout drawings based on the laws and regulations analyzed by the analysis unit. For example, the generation AI generates the optimal layout based on the user's instructions. Step 4: The proposal unit optimizes the office layout generated by the generation unit and proposes it to the user. For example, the generation AI may suggest changing the desk arrangement to widen the aisles. Step 5: The correction unit analyzes the user's modifications to the layout proposed by the proposal unit and automatically corrects it to comply with laws and regulations. For example, the generation AI changes the location of conference rooms and corrects the drawing to comply with laws and regulations. Step 6: The quality check section checks the quality of the final drawing. For example, the generation AI checks whether the drawing complies with the Building Standards Act.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The database department creates a database of laws and regulations, and an analysis unit that analyzes the laws and regulations and internal rules that have been databased by the database creation unit; a generation unit that automatically generates an office layout drawing based on the laws and regulations and internal rules analyzed by the analysis unit; a proposal unit that optimizes the office layout generated by the generation unit and proposes the optimized office layout to a user; a correction unit that analyzes a user's correction to the layout proposed by the proposal unit and automatically corrects the layout so that it complies with laws and regulations; and a quality check unit that checks the quality of the final created drawing. A system characterized by:

2. The database creation unit The analysis unit tracks the history of changes to the laws and regulations and performs analysis based on the latest information.

2. The system of claim 1.

3. The analysis unit Collect expert opinions on the interpretation of the aforementioned laws and regulations and incorporate them into the analysis.

2. The system of claim 1.

4. The analysis unit Analyze the user's feelings regarding the application of the laws and regulations and internal rules, and focus on analyzing the areas where the user feels uneasy.

2. The system of claim 1.

5. The database creation unit Apply the database of laws and regulations to the other business operations mentioned above, and promote its use in a wide range of fields.

2. The system of claim 1.

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