system

Abstract

The system according to this embodiment aims to improve the uniformity of quality and efficiency in creating office layout drawings. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, and a generation unit. The reception unit receives basic information about the office layout. The proposal unit proposes an office layout based on the information received by the reception unit, in accordance with laws and regulations. The generation unit generates detailed drawings based on the layout proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is necessary to consider laws and regulations and internal rules in the drawing creation of office layouts, and expertise is required, so there is room for improvement in the quality uniformity and efficiency.

[0005] [[ID=3d]] The system according to the embodiment aims to achieve quality uniformity and efficiency improvement in the drawing creation of office layouts.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, and a generation unit. The reception unit receives basic information about the office layout. The proposal unit proposes an office layout based on the information received by the reception unit, in accordance with laws and regulations and internal rules. The generation unit generates detailed drawings based on the layout proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can achieve uniform quality and efficiency in creating office layout drawings. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage �2 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The office layout proposal system according to an embodiment of the present invention is a system that receives basic information on an office layout, proposes an optimal office layout considering laws and regulations and internal rules, and automatically generates detailed drawings. This system comprises a reception unit that receives basic information on the office layout, a proposal unit that proposes an optimal office layout considering laws and regulations and internal rules based on the information received by the reception unit, and a generation unit that generates detailed drawings based on the layout proposed by the proposal unit. For example, the user inputs basic information such as the area of ​​the office, the number of rooms, and the types of furniture and equipment to be placed. Next, the proposal unit analyzes the input information and proposes an optimal office layout while considering laws and regulations and internal rules. For example, it considers securing evacuation routes in accordance with the Fire Service Act and securing a working environment in accordance with the Labor Standards Act. Furthermore, the generation unit automatically generates specific layout drawings and dimension drawings based on the proposed layout. As a result, the user can easily obtain detailed drawings. In this way, the office layout proposal system can standardize the quality of office layouts and efficiently create drawings.

[0029] The office layout proposal system according to this embodiment comprises a reception unit, a proposal unit, and a generation unit. The reception unit receives basic information about the office layout. This basic information includes, but is not limited to, the office area, the number of rooms, and the types of furniture and equipment to be placed. For example, the reception unit receives the office area and the number of rooms entered by the user. The reception unit can also receive the types of furniture and equipment to be placed. For example, the reception unit can automatically calculate the office area and the number of rooms based on the information entered by the user. The proposal unit proposes the optimal office layout based on the information received by the reception unit, taking into account laws and regulations. For example, the proposal unit considers securing evacuation routes in accordance with the Fire Service Act and securing a suitable working environment in accordance with the Labor Standards Act. For example, the proposal unit uses AI to analyze the entered information and propose the optimal office layout. The generation unit generates detailed drawings based on the layout proposed by the proposal unit. For example, the generation unit automatically generates specific layout plans and dimension drawings. The generation unit, for example, uses AI to generate specific layout plans and dimension drawings based on the proposed layout. This allows the office layout proposal system to standardize the quality of office layouts and efficiently create drawings.

[0030] The office layout proposal system includes an acquisition unit that acquires updated information on laws and internal regulations. The acquisition unit acquires updated information on laws and internal regulations. This updated information includes, but is not limited to, information on legal amendments and changes to company internal regulations. The acquisition unit can acquire the latest updated information on laws and internal regulations, for example, via the internet. The acquisition unit can also automatically acquire information on changes to company internal regulations. For example, the acquisition unit periodically checks for legal amendments and acquires the latest information. Information on changes to company internal regulations can be automatically acquired from the company's internal systems. As a result, the office layout proposal system can propose office layouts based on the latest laws and internal regulations. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input legal amendment information into the AI, and the AI ​​can automatically acquire the latest information.

[0031] The office layout proposal system includes a feedback unit that receives user feedback. The feedback unit receives user feedback, which includes, but is not limited to, questionnaires, comments, and evaluations. The feedback unit can receive user feedback, for example, through online forms. It can also receive feedback via telephone or email. For example, the feedback unit automatically collects feedback entered by users in online forms. Feedback received via telephone or email can be manually entered by the feedback unit. This allows the office layout proposal system to reflect user feedback and propose better office layouts. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input feedback entered in online forms into AI, which can then automatically analyze the feedback.

[0032] The proposal department can propose layouts based on evaluation criteria for efficiency, safety, and comfort. The proposal department proposes layouts based on evaluation criteria for efficiency, safety, and comfort. Evaluation criteria for efficiency include, for example, reducing work time and making effective use of space, but are not limited to such examples. Evaluation criteria for safety include, for example, ensuring evacuation routes and fire prevention measures, but are not limited to such examples. Evaluation criteria for comfort include, for example, the temperature of the work environment and the brightness of the lighting, but are not limited to such examples. The proposal department can propose layouts based on evaluation criteria for efficiency, safety, and comfort, for example, by using AI. For example, the proposal department can propose layouts that prioritize reducing work time. The proposal department can also propose layouts that prioritize ensuring evacuation routes. Furthermore, the proposal department can also propose layouts that take into account the temperature of the work environment and the brightness of the lighting. In this way, the proposal department can propose the optimal office layout based on the evaluation criteria. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input evaluation criteria for efficiency, safety, and comfort into the AI, which can then automatically propose the optimal layout.

[0033] The generation unit can generate detailed drawings of specific layout plans, dimension drawings, and evacuation route diagrams. The generation unit generates detailed drawings of specific layout plans, dimension drawings, and evacuation route diagrams. Specific layout plans include, for example, furniture placement and corridor locations, but are not limited to such examples. Dimension drawings include, for example, room dimensions and furniture sizes, but are not limited to such examples. Evacuation route diagrams include, for example, the display of evacuation routes and the locations of emergency exits, but are not limited to such examples. The generation unit can generate detailed drawings of specific layout plans, dimension drawings, and evacuation route diagrams, for example, using AI. For example, the generation unit can generate layout plans showing furniture placement and corridor locations. The generation unit can also generate dimension drawings showing room dimensions and furniture sizes. Furthermore, the generation unit can also generate evacuation route diagrams showing evacuation routes and the locations of emergency exits. In this way, the generation unit can reduce the burden on the user by automatically generating detailed drawings. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without using AI. For example, the generation unit can input the specific layout plan, dimension drawing, and evacuation route map into the AI, which can then automatically generate detailed drawings.

[0034] The reception desk can analyze the user's past input history and suggest the optimal input format. This past input history includes, but is not limited to, analysis of past input data and input patterns. For example, the reception desk can automatically display basic office layout information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest basic office layout information to be used during specific time periods based on the user's past input history. This allows the reception desk to improve input efficiency by suggesting the optimal input format based on past input history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input past input data into an AI, which can then automatically suggest the optimal input format.

[0035] The reception desk can automatically customize input fields based on the office area and the number of rooms. The office area includes, but is not limited to, units such as square meters or tsubo. The number of rooms includes, but is not limited to, conference rooms, private rooms, and open spaces. For example, if the office area is large, the reception desk can add fields for detailed room layout and furniture arrangement. If there are many rooms, the reception desk can also add fields for detailed information about the purpose and facilities of each room. Furthermore, the reception desk can automatically display input fields related to necessary laws and regulations depending on the office area and the number of rooms. This allows the reception desk to improve input accuracy by providing input fields tailored to the office area and the number of rooms. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input office area and room number data into the AI, which can then automatically customize the input fields.

[0036] The reception desk can add input fields based on region-specific laws and regulations, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific laws and regulations include, but are not limited to, local government ordinances and regional building codes. For example, if the user is in a specific region, the reception desk can add input fields based on the building codes of that region. It can also add input fields based on the fire safety regulations of a specific city if the user is in that city. Furthermore, if the user is in a specific country, the reception desk can add input fields based on the labor standards laws of that country. This makes compliance easier by providing input fields based on region-specific laws and regulations. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then automatically add input fields based on region-specific laws and regulations.

[0037] The reception desk can analyze users' social media activity and provide relevant office layout trend information. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Trend information includes, but is not limited to, the latest design trends and popular layout styles. The reception desk can, for example, analyze posts from accounts that users follow on social media and provide the latest office layout trend information. The reception desk can also analyze posts that users "like" or "share" on social media and suggest relevant office layout ideas. Furthermore, the reception desk can analyze posts from groups that users participate in on social media and provide relevant office layout trend information. This allows the reception desk to propose layouts that meet user needs by providing the latest trend information based on social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input social media data into AI, which can automatically analyze trend information.

[0038] The proposal department can adjust the level of detail in a proposal based on the importance of the laws and regulations at the time of proposal submission. The importance of laws and regulations includes, but is not limited to, legal binding force and the presence or absence of penalties. The level of detail in a proposal includes, but is not limited to, the provision of detailed drawings or simple layout proposals. For example, the proposal department can prioritize proposals based on important laws and regulations and provide detailed explanations. The proposal department can also simplify proposals based on less important laws and regulations and provide only the minimum necessary information. Furthermore, the proposal department can adjust the level of detail in a stepwise manner according to the importance of the laws and regulations. This makes compliance with laws and regulations easier by allowing the proposal department to provide proposals appropriate to the importance of the laws and regulations. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of laws and regulations into the AI, and the AI ​​can automatically adjust the level of detail in the proposal.

[0039] The proposal department can apply different proposal algorithms depending on the office category when making a proposal. Office categories include, but are not limited to, sales offices, development offices, and administrative offices. Proposal algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, in the case of a general office, the proposal department will propose a layout that prioritizes efficiency. In the case of a creative office, the proposal department can also propose a layout that prioritizes creativity. Furthermore, in the case of a medical facility, the proposal department can propose a layout that prioritizes safety and hygiene. This allows the proposal department to provide optimal proposals tailored to the office category, thereby proposing layouts that meet user needs. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input the office category into the AI, which can then automatically apply a different proposal algorithm.

[0040] The proposal department can determine the priority of proposals based on the intended use of the office when making a proposal. The intended use of the office includes, but is not limited to, meeting rooms, work areas, and break areas. The priority of proposals includes, but is not limited to, importance and urgency. For example, the proposal department may prioritize suggesting meeting room layouts to support efficient meeting management. It may also prioritize suggesting workspace layouts to improve work efficiency. Furthermore, it may prioritize suggesting break area layouts to support employee refreshment. In this way, the proposal department can propose layouts that meet user needs by providing proposals tailored to the intended use of the office. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the intended use of the office into the AI, and the AI ​​can automatically determine the priority of proposals.

[0041] The proposal department can adjust the order of proposals based on office relevance when making proposals. Office relevance includes, but is not limited to, relationships between departments or collaboration between tasks. The order of proposals includes, but is not limited to, importance or relevance. For example, the proposal department might propose layouts for important rooms or areas first, allowing for priority review. It can also propose layouts for highly relevant rooms or areas consecutively, making it easier to grasp the overall flow. Furthermore, it can prioritize important proposals by postponing less relevant room or area layouts. This allows the proposal department to propose layouts that meet user needs by providing a proposal order based on office relevance. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input office relevance into AI, which can automatically adjust the order of proposals.

[0042] The generation unit can analyze the user's past drawing creation history to select the optimal generation method during generation. The generation unit analyzes the user's past drawing creation history to select the optimal generation method. Past drawing creation history includes, but is not limited to, analysis of past drawing data and creation patterns. For example, the generation unit can select the optimal generation method based on the style of drawings previously created by the user. Furthermore, the generation unit can propose an efficient generation method based on the user's past drawing creation history. In addition, the generation unit can analyze the user's past drawing creation history and select the most suitable generation method. This allows the generation unit to improve the efficiency of drawing creation by providing the optimal generation method based on past drawing creation history. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input past drawing data into AI, which can then automatically select the optimal generation method.

[0043] The generation unit can customize the level of detail of the drawings based on the current office situation during generation. The generation unit customizes the level of detail of the drawings based on the current office situation during generation. The current office situation includes, but is not limited to, the current layout and usage. The level of detail of the drawings includes, but is not limited to, detailed drawings and simplified drawings. For example, the generation unit generates a detailed drawing based on the current office layout. The generation unit can also adjust the required level of detail according to the current office situation. Furthermore, the generation unit can generate an optimal drawing considering the current office situation. In this way, the generation unit can generate drawings that meet user needs by providing a level of detail of drawings that matches the current office situation. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the current office layout data into the AI, and the AI ​​can automatically customize the level of detail of the drawings.

[0044] The generation unit can generate optimal drawings while considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Optimal drawings include, but are not limited to, drawings that comply with laws and regulations or meet the user's needs. For example, if the user is in a specific region, the generation unit can generate drawings based on the building codes of that region. Furthermore, if the user is in a specific city, the generation unit can generate drawings based on the fire safety regulations of that city. In addition, if the user is in a specific country, the generation unit can generate drawings based on the labor standards laws of that country. This makes legal compliance easier by providing optimal drawings based on geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input the user's geographical location information into the AI, which can then automatically generate the optimal drawing.

[0045] The generation unit can analyze the user's social media activity during generation and suggest relevant drawings. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Relevant drawings include, but are not limited to, trend-based drawings and drawings tailored to the user's needs. For example, the generation unit can analyze posts from accounts the user follows on social media and generate drawings based on the latest office layout trend information. The generation unit can also analyze posts the user has "liked" or "shared" on social media and generate drawings based on relevant office layout ideas. Furthermore, the generation unit can analyze posts from groups the user participates in on social media and generate drawings based on relevant office layout trend information. In this way, the generation unit can generate drawings tailored to the user's needs by providing the latest trend information based on social media activity. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input social media data into AI, and the AI ​​can automatically suggest relevant drawings.

[0046] The acquisition unit can analyze the update history of past laws and regulations and select the optimal acquisition method. The acquisition unit analyzes the update history of past laws and regulations and selects the optimal acquisition method. The update history of past laws and regulations includes, but is not limited to, analysis of past update data and update patterns. For example, the acquisition unit can prioritize acquiring important update information of laws and regulations based on past update history. The acquisition unit can also identify laws and regulations with high update frequency and select an efficient acquisition method. Furthermore, the acquisition unit can analyze past update history and select the most efficient acquisition method. As a result, the acquisition unit enables efficient information acquisition by providing the optimal acquisition method based on past update history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past update data into AI, and the AI ​​can automatically select the optimal acquisition method.

[0047] The acquisition unit can prioritize the acquisition of highly relevant information when acquiring updated information on laws and regulations, taking into account the user's geographical location. The acquisition unit prioritizes the acquisition of highly relevant information when acquiring updated information on laws and regulations, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, local laws and regulations. For example, if the user is in a specific region, the acquisition unit will prioritize the acquisition of updated information on the laws and regulations of that region. The acquisition unit can also prioritize the acquisition of updated information on the laws and regulations of a specific city if the user is in that city. Furthermore, if the user is in a specific country, the acquisition unit can also prioritize the acquisition of updated information on the laws and regulations of that country if the user is in that country. In this way, the acquisition unit makes it easier to comply with laws and regulations by providing updated information based on geographical location. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's geographical location information into the AI, which can then automatically prioritize and acquire highly relevant information.

[0048] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when receiving feedback. Past feedback history includes, but is not limited to, analysis of past feedback data and feedback patterns. For example, the feedback unit may prioritize suggesting feedback methods (voice, text, etc.) that the user has frequently used in the past. The feedback unit can also predict and suggest feedback methods to be used during specific time periods based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history to select the most efficient feedback method. This allows the feedback unit to improve feedback efficiency by providing the optimal feedback method based on past feedback history. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input past feedback data into AI, which can then automatically select the optimal feedback method.

[0049] The feedback unit can prioritize receiving highly relevant feedback when receiving feedback, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant feedback includes, but is not limited to, feedback based on local needs or feedback related to business operations. For example, if a user is in a specific region, the feedback unit will prioritize receiving feedback relevant to that region. Furthermore, if a user is in a specific city, the feedback unit can prioritize receiving feedback relevant to that city. In addition, if a user is in a specific country, the feedback unit can prioritize receiving feedback relevant to that country. This allows the feedback unit to provide geographical location-based feedback, enabling responses tailored to user needs. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can input the user's geographical location information into an AI, which can then automatically prioritize receiving highly relevant feedback.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The proposal department can apply different proposal algorithms depending on the office category when making a proposal. Office categories include, but are not limited to, sales offices, development offices, and administrative offices. Proposal algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the proposal department might propose an efficiency-focused layout for a general office. It could also propose a creativity-focused layout for a creative office. Furthermore, for a medical facility, it could propose a layout that prioritizes safety and hygiene. This allows the proposal department to provide optimal proposals tailored to the office category, thereby suggesting layouts that meet user needs. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department could input the office category into an AI, which could then automatically apply a different proposal algorithm.

[0052] The generation unit can customize the level of detail of the drawings based on the current office situation during generation. The current office situation includes, but is not limited to, the current layout and usage. The level of detail of the drawings includes, but is not limited to, detailed drawings and simplified drawings. For example, the generation unit generates detailed drawings based on the current office layout. The generation unit can also adjust the required level of detail according to the current office situation. Furthermore, the generation unit can generate optimal drawings considering the current office situation. In this way, the generation unit can generate drawings that meet user needs by providing a level of detail of drawings that matches the current office situation. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the current office layout data into the AI, and the AI ​​can automatically customize the level of detail of the drawings.

[0053] The data acquisition unit can analyze the update history of past laws and regulations and select the optimal acquisition method. The update history of past laws and regulations includes, but is not limited to, analysis of past update data and update patterns. For example, the data acquisition unit can prioritize acquiring important update information for laws and regulations based on past update history. The data acquisition unit can also identify laws and regulations with high update frequency and select an efficient acquisition method. Furthermore, the data acquisition unit can analyze past update history and select the most efficient acquisition method. As a result, the data acquisition unit enables efficient information acquisition by providing the optimal acquisition method based on past update history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past update data into AI, and the AI ​​can automatically select the optimal acquisition method.

[0054] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when receiving feedback. Past feedback history includes, but is not limited to, analysis of past feedback data and feedback patterns. For example, the feedback unit may prioritize suggesting feedback methods (voice, text, etc.) that the user has frequently used in the past. The feedback unit can also predict and suggest feedback methods to be used during specific time periods based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history to select the most efficient feedback method. This allows the feedback unit to improve feedback efficiency by providing the optimal feedback method based on past feedback history. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can input past feedback data into an AI, which can then automatically select the optimal feedback method.

[0055] The proposal department can adjust the level of detail in a proposal based on the importance of the laws and regulations. The importance of laws and regulations includes, but is not limited to, legal binding force and the presence or absence of penalties. The level of detail in a proposal includes, but is not limited to, the provision of detailed drawings or simplified layouts. The proposal department will, for example, prioritize proposals based on important laws and regulations and provide detailed explanations. Furthermore, the proposal department will simplify proposals based on less important laws and regulations, and provide additional content.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives basic office layout information. This information includes the office area, the number of rooms, and the types of furniture and equipment to be placed. For example, it accepts the office area and the number of rooms entered by the user. The reception desk can also accept the types of furniture and equipment to be placed. Furthermore, the reception desk can automatically calculate the office area and the number of rooms based on the information entered by the user. Step 2: Based on the information received by the reception department, the proposal department proposes the optimal office layout, taking into account laws and internal regulations. For example, it considers securing evacuation routes in accordance with the Fire Service Act and ensuring a suitable working environment in accordance with the Labor Standards Act. The proposal department uses AI to analyze the input information and propose the optimal office layout. Step 3: The generation unit generates detailed drawings based on the layout proposed by the proposal unit. The generation unit automatically generates specific layout plans and dimension drawings. The generation unit uses AI to generate specific layout plans and dimension drawings based on the proposed layout.

[0058] (Example of form 2) The office layout proposal system according to an embodiment of the present invention is a system that receives basic information on an office layout, proposes an optimal office layout considering laws and regulations and internal rules, and automatically generates detailed drawings. This system comprises a reception unit that receives basic information on the office layout, a proposal unit that proposes an optimal office layout considering laws and regulations and internal rules based on the information received by the reception unit, and a generation unit that generates detailed drawings based on the layout proposed by the proposal unit. For example, the user inputs basic information such as the area of ​​the office, the number of rooms, and the types of furniture and equipment to be placed. Next, the proposal unit analyzes the input information and proposes an optimal office layout while considering laws and regulations and internal rules. For example, it considers securing evacuation routes in accordance with the Fire Service Act and securing a working environment in accordance with the Labor Standards Act. Furthermore, the generation unit automatically generates specific layout drawings and dimension drawings based on the proposed layout. As a result, the user can easily obtain detailed drawings. In this way, the office layout proposal system can standardize the quality of office layouts and efficiently create drawings.

[0059] The office layout proposal system according to this embodiment comprises a reception unit, a proposal unit, and a generation unit. The reception unit receives basic information about the office layout. This basic information includes, but is not limited to, the office area, the number of rooms, and the types of furniture and equipment to be placed. For example, the reception unit receives the office area and the number of rooms entered by the user. The reception unit can also receive the types of furniture and equipment to be placed. For example, the reception unit can automatically calculate the office area and the number of rooms based on the information entered by the user. The proposal unit proposes the optimal office layout based on the information received by the reception unit, taking into account laws and regulations. For example, the proposal unit considers securing evacuation routes in accordance with the Fire Service Act and securing a suitable working environment in accordance with the Labor Standards Act. For example, the proposal unit uses AI to analyze the entered information and propose the optimal office layout. The generation unit generates detailed drawings based on the layout proposed by the proposal unit. For example, the generation unit automatically generates specific layout plans and dimension drawings. The generation unit, for example, uses AI to generate specific layout plans and dimension drawings based on the proposed layout. This allows the office layout proposal system to standardize the quality of office layouts and efficiently create drawings.

[0060] The office layout proposal system includes an acquisition unit that acquires updated information on laws and internal regulations. The acquisition unit acquires updated information on laws and internal regulations. This updated information includes, but is not limited to, information on legal amendments and changes to company internal regulations. The acquisition unit can acquire the latest updated information on laws and internal regulations, for example, via the internet. The acquisition unit can also automatically acquire information on changes to company internal regulations. For example, the acquisition unit periodically checks for legal amendments and acquires the latest information. Information on changes to company internal regulations can be automatically acquired from the company's internal systems. As a result, the office layout proposal system can propose office layouts based on the latest laws and internal regulations. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input legal amendment information into the AI, and the AI ​​can automatically acquire the latest information.

[0061] The office layout proposal system includes a feedback unit that receives user feedback. The feedback unit receives user feedback, which includes, but is not limited to, questionnaires, comments, and evaluations. The feedback unit can receive user feedback, for example, through online forms. It can also receive feedback via telephone or email. For example, the feedback unit automatically collects feedback entered by users in online forms. Feedback received via telephone or email can be manually entered by the feedback unit. This allows the office layout proposal system to reflect user feedback and propose better office layouts. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input feedback entered in online forms into AI, which can then automatically analyze the feedback.

[0062] The proposal department can propose layouts based on evaluation criteria for efficiency, safety, and comfort. The proposal department proposes layouts based on evaluation criteria for efficiency, safety, and comfort. Evaluation criteria for efficiency include, for example, reducing work time and making effective use of space, but are not limited to such examples. Evaluation criteria for safety include, for example, ensuring evacuation routes and fire prevention measures, but are not limited to such examples. Evaluation criteria for comfort include, for example, the temperature of the work environment and the brightness of the lighting, but are not limited to such examples. The proposal department can propose layouts based on evaluation criteria for efficiency, safety, and comfort, for example, by using AI. For example, the proposal department can propose layouts that prioritize reducing work time. The proposal department can also propose layouts that prioritize ensuring evacuation routes. Furthermore, the proposal department can also propose layouts that take into account the temperature of the work environment and the brightness of the lighting. In this way, the proposal department can propose the optimal office layout based on the evaluation criteria. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input evaluation criteria for efficiency, safety, and comfort into the AI, which can then automatically propose the optimal layout.

[0063] The generation unit can generate detailed drawings of specific layout plans, dimension drawings, and evacuation route diagrams. The generation unit generates detailed drawings of specific layout plans, dimension drawings, and evacuation route diagrams. Specific layout plans include, for example, furniture placement and corridor locations, but are not limited to such examples. Dimension drawings include, for example, room dimensions and furniture sizes, but are not limited to such examples. Evacuation route diagrams include, for example, the display of evacuation routes and the locations of emergency exits, but are not limited to such examples. The generation unit can generate detailed drawings of specific layout plans, dimension drawings, and evacuation route diagrams, for example, using AI. For example, the generation unit can generate layout plans showing furniture placement and corridor locations. The generation unit can also generate dimension drawings showing room dimensions and furniture sizes. Furthermore, the generation unit can also generate evacuation route diagrams showing evacuation routes and the locations of emergency exits. In this way, the generation unit can reduce the burden on the user by automatically generating detailed drawings. Some or all of the above processing in the generation unit may be performed using, for example, AI, or without using AI. For example, the generation unit can input the specific layout plan, dimension drawing, and evacuation route map into the AI, which can then automatically generate detailed drawings.

[0064] The reception desk can estimate the user's emotions and adjust the input method for basic office layout information based on the estimated emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of basic office layout information. This allows the reception desk to reduce the user's burden by providing input methods tailored to their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generating AI, which can then automatically estimate the emotion.

[0065] The reception desk can analyze the user's past input history and suggest the optimal input format. This past input history includes, but is not limited to, analysis of past input data and input patterns. For example, the reception desk can automatically display basic office layout information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest basic office layout information to be used during specific time periods based on the user's past input history. This allows the reception desk to improve input efficiency by suggesting the optimal input format based on past input history. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input past input data into an AI, which can then automatically suggest the optimal input format.

[0066] The reception desk can automatically customize input fields based on the office area and the number of rooms. The office area includes, but is not limited to, units such as square meters or tsubo. The number of rooms includes, but is not limited to, conference rooms, private rooms, and open spaces. For example, if the office area is large, the reception desk can add fields for detailed room layout and furniture arrangement. If there are many rooms, the reception desk can also add fields for detailed information about the purpose and facilities of each room. Furthermore, the reception desk can automatically display input fields related to necessary laws and regulations depending on the office area and the number of rooms. This allows the reception desk to improve input accuracy by providing input fields tailored to the office area and the number of rooms. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input office area and room number data into the AI, which can then automatically customize the input fields.

[0067] The reception desk can estimate the user's emotions and determine the priority of input fields based on the estimated emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the reception desk can prioritize displaying important input fields to allow for quick input. If the user is relaxed, the reception desk can also sequentially display detailed input fields and provide a customizable input method. Furthermore, if the user is in a hurry, the reception desk can display the most important input fields first to allow for quick input. In this way, the reception desk can improve input efficiency by providing priority of input fields according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's facial expression data into a generating AI, which can then automatically estimate the emotion.

[0068] The reception desk can add input fields based on region-specific laws and regulations, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Region-specific laws and regulations include, but are not limited to, local government ordinances and regional building codes. For example, if the user is in a specific region, the reception desk can add input fields based on the building codes of that region. It can also add input fields based on the fire safety regulations of a specific city if the user is in that city. Furthermore, if the user is in a specific country, the reception desk can add input fields based on the labor standards laws of that country. This makes compliance easier by providing input fields based on region-specific laws and regulations. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not. For example, the reception desk can input the user's geographical location information into the AI, which can then automatically add input fields based on region-specific laws and regulations.

[0069] The reception desk can analyze users' social media activity and provide relevant office layout trend information. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Trend information includes, but is not limited to, the latest design trends and popular layout styles. The reception desk can, for example, analyze posts from accounts that users follow on social media and provide the latest office layout trend information. The reception desk can also analyze posts that users "like" or "share" on social media and suggest relevant office layout ideas. Furthermore, the reception desk can analyze posts from groups that users participate in on social media and provide relevant office layout trend information. This allows the reception desk to propose layouts that meet user needs by providing the latest trend information based on social media activity. Some or all of the above processing in the reception desk may be performed using, for example, AI, or not using AI. For example, the reception desk can input social media data into AI, which can automatically analyze trend information.

[0070] The suggestion unit can estimate the user's emotions and adjust its layout suggestion method based on the estimated emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the suggestion unit can provide simple and intuitive layout suggestions. If the user is relaxed, the suggestion unit can also provide detailed layout suggestions and customizable options. Furthermore, if the user is in a hurry, the suggestion unit can quickly suggest the optimal layout and allow for immediate confirmation. In this way, the suggestion unit can improve user satisfaction by providing suggestions tailored to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the proposal unit can input user facial expression data into a generating AI, which can then automatically estimate emotions.

[0071] The proposal department can adjust the level of detail in a proposal based on the importance of the laws and regulations at the time of proposal submission. The importance of laws and regulations includes, but is not limited to, legal binding force and the presence or absence of penalties. The level of detail in a proposal includes, but is not limited to, the provision of detailed drawings or simple layout proposals. For example, the proposal department can prioritize proposals based on important laws and regulations and provide detailed explanations. The proposal department can also simplify proposals based on less important laws and regulations and provide only the minimum necessary information. Furthermore, the proposal department can adjust the level of detail in a stepwise manner according to the importance of the laws and regulations. This makes compliance with laws and regulations easier by allowing the proposal department to provide proposals appropriate to the importance of the laws and regulations. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the importance of laws and regulations into the AI, and the AI ​​can automatically adjust the level of detail in the proposal.

[0072] The proposal department can apply different proposal algorithms depending on the office category when making a proposal. Office categories include, but are not limited to, sales offices, development offices, and administrative offices. Proposal algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, in the case of a general office, the proposal department will propose a layout that prioritizes efficiency. In the case of a creative office, the proposal department can also propose a layout that prioritizes creativity. Furthermore, in the case of a medical facility, the proposal department can propose a layout that prioritizes safety and hygiene. This allows the proposal department to provide optimal proposals tailored to the office category, thereby proposing layouts that meet user needs. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input the office category into the AI, which can then automatically apply a different proposal algorithm.

[0073] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide a short suggestion that can be quickly reviewed. In this way, the suggestion unit can improve user satisfaction by providing suggestion lengths that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial data into a generative AI, which can then automatically estimate emotions.

[0074] The proposal department can determine the priority of proposals based on the intended use of the office when making a proposal. The intended use of the office includes, but is not limited to, meeting rooms, work areas, and break areas. The priority of proposals includes, but is not limited to, importance and urgency. For example, the proposal department may prioritize suggesting meeting room layouts to support efficient meeting management. It may also prioritize suggesting workspace layouts to improve work efficiency. Furthermore, it may prioritize suggesting break area layouts to support employee refreshment. In this way, the proposal department can propose layouts that meet user needs by providing proposals tailored to the intended use of the office. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not using AI. For example, the proposal department can input the intended use of the office into the AI, and the AI ​​can automatically determine the priority of proposals.

[0075] The proposal department can adjust the order of proposals based on office relevance when making proposals. Office relevance includes, but is not limited to, relationships between departments or collaboration between tasks. The order of proposals includes, but is not limited to, importance or relevance. For example, the proposal department might propose layouts for important rooms or areas first, allowing for priority review. It can also propose layouts for highly relevant rooms or areas consecutively, making it easier to grasp the overall flow. Furthermore, it can prioritize important proposals by postponing less relevant room or area layouts. This allows the proposal department to propose layouts that meet user needs by providing a proposal order based on office relevance. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input office relevance into AI, which can automatically adjust the order of proposals.

[0076] The generation unit can estimate the user's emotions and adjust the drawing generation method based on the estimated user emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the generation unit can generate a simple and intuitive drawing. If the user is relaxed, the generation unit can also generate a detailed drawing and provide customizable options. Furthermore, if the user is in a hurry, the generation unit can generate a simplified drawing that can be quickly reviewed. In this way, the generation unit can improve user satisfaction by providing a drawing generation method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which can then automatically estimate the emotion.

[0077] The generation unit can analyze the user's past drawing creation history to select the optimal generation method during generation. The generation unit analyzes the user's past drawing creation history to select the optimal generation method. Past drawing creation history includes, but is not limited to, analysis of past drawing data and creation patterns. For example, the generation unit can select the optimal generation method based on the style of drawings previously created by the user. Furthermore, the generation unit can propose an efficient generation method based on the user's past drawing creation history. In addition, the generation unit can analyze the user's past drawing creation history and select the most suitable generation method. This allows the generation unit to improve the efficiency of drawing creation by providing the optimal generation method based on past drawing creation history. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input past drawing data into AI, which can then automatically select the optimal generation method.

[0078] The generation unit can customize the level of detail of the drawings based on the current office situation during generation. The generation unit customizes the level of detail of the drawings based on the current office situation during generation. The current office situation includes, but is not limited to, the current layout and usage. The level of detail of the drawings includes, but is not limited to, detailed drawings and simplified drawings. For example, the generation unit generates a detailed drawing based on the current office layout. The generation unit can also adjust the required level of detail according to the current office situation. Furthermore, the generation unit can generate an optimal drawing considering the current office situation. In this way, the generation unit can generate drawings that meet user needs by providing a level of detail of drawings that matches the current office situation. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the current office layout data into the AI, and the AI ​​can automatically customize the level of detail of the drawings.

[0079] The generation unit can estimate the user's emotions and determine the priority of drawings based on the estimated emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the generation unit can prioritize generating important drawings for quick review. If the user is relaxed, the generation unit can sequentially generate detailed drawings and provide customizable options. Furthermore, if the user is in a hurry, the generation unit can generate the most important drawings first for quick review. In this way, the generation unit can improve user satisfaction by providing drawing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI. For example, the generation unit inputs the user's facial expression data into the generation AI, which can then automatically estimate the emotion.

[0080] The generation unit can generate optimal drawings while considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Optimal drawings include, but are not limited to, drawings that comply with laws and regulations or meet the user's needs. For example, if the user is in a specific region, the generation unit can generate drawings based on the building codes of that region. Furthermore, if the user is in a specific city, the generation unit can generate drawings based on the fire safety regulations of that city. In addition, if the user is in a specific country, the generation unit can generate drawings based on the labor standards laws of that country. This makes legal compliance easier by providing optimal drawings based on geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input the user's geographical location information into the AI, which can then automatically generate the optimal drawing.

[0081] The generation unit can analyze the user's social media activity during generation and suggest relevant drawings. Social media activity includes, but is not limited to, analysis of post content and follower analysis. Relevant drawings include, but are not limited to, trend-based drawings and drawings tailored to the user's needs. For example, the generation unit can analyze posts from accounts the user follows on social media and generate drawings based on the latest office layout trend information. The generation unit can also analyze posts the user has "liked" or "shared" on social media and generate drawings based on relevant office layout ideas. Furthermore, the generation unit can analyze posts from groups the user participates in on social media and generate drawings based on relevant office layout trend information. In this way, the generation unit can generate drawings tailored to the user's needs by providing the latest trend information based on social media activity. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input social media data into AI, and the AI ​​can automatically suggest relevant drawings.

[0082] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring updated information on laws and regulations based on the estimated emotions. The acquisition unit estimates the user's emotions and adjusts the timing of acquiring updated information on laws and regulations based on the estimated emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the acquisition unit can reduce the frequency of acquiring updated information and acquire only important information. Also, if the user is relaxed, the acquisition unit can acquire and provide detailed updated information frequently. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring the most important updated information and provide it quickly. In this way, the acquisition unit can reduce the burden on the user by providing update information acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit inputs the user's facial expression data into a generating AI, which can then automatically estimate the emotion.

[0083] The acquisition unit can analyze the update history of past laws and regulations and select the optimal acquisition method. The acquisition unit analyzes the update history of past laws and regulations and selects the optimal acquisition method. The update history of past laws and regulations includes, but is not limited to, analysis of past update data and update patterns. For example, the acquisition unit can prioritize acquiring important update information of laws and regulations based on past update history. The acquisition unit can also identify laws and regulations with high update frequency and select an efficient acquisition method. Furthermore, the acquisition unit can analyze past update history and select the most efficient acquisition method. As a result, the acquisition unit enables efficient information acquisition by providing the optimal acquisition method based on past update history. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input past update data into AI, and the AI ​​can automatically select the optimal acquisition method.

[0084] The acquisition unit can estimate the user's emotions and determine the priority of update information to acquire based on the estimated user emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the acquisition unit can prioritize acquiring important update information and provide it quickly. Also, if the user is relaxed, the acquisition unit can sequentially acquire and provide detailed update information. Furthermore, if the user is in a hurry, the acquisition unit can acquire the most important update information first and provide it quickly. In this way, the acquisition unit can quickly acquire important information by providing priority of update information according to the user's emotions. Emotion estimation is implemented using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit inputs the user's facial expression data into a generating AI, which can then automatically estimate the emotion.

[0085] The acquisition unit can prioritize the acquisition of highly relevant information when acquiring updated information on laws and regulations, taking into account the user's geographical location. The acquisition unit prioritizes the acquisition of highly relevant information when acquiring updated information on laws and regulations, taking into account the user's geographical location. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant information includes, but is not limited to, local laws and regulations. For example, if the user is in a specific region, the acquisition unit will prioritize the acquisition of updated information on the laws and regulations of that region. The acquisition unit can also prioritize the acquisition of updated information on the laws and regulations of a specific city if the user is in that city. Furthermore, if the user is in a specific country, the acquisition unit can also prioritize the acquisition of updated information on the laws and regulations of that country if the user is in that country. In this way, the acquisition unit makes it easier to comply with laws and regulations by providing updated information based on geographical location. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's geographical location information into the AI, which can then automatically prioritize and acquire highly relevant information.

[0086] The feedback unit can estimate the user's emotions and adjust the feedback process based on those emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the feedback unit can provide a simple feedback form and minimize input steps. If the user is relaxed, the feedback unit can also provide detailed feedback options and suggest customizable input methods. Furthermore, if the user is in a hurry, the feedback unit can prioritize voice input and provide feedback quickly. This allows the feedback unit to reduce the user's burden by providing feedback methods tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI or not. For example, the feedback unit inputs the user's facial expression data into a generating AI, which can then automatically estimate the emotion.

[0087] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when receiving feedback. Past feedback history includes, but is not limited to, analysis of past feedback data and feedback patterns. For example, the feedback unit may prioritize suggesting feedback methods (voice, text, etc.) that the user has frequently used in the past. The feedback unit can also predict and suggest feedback methods to be used during specific time periods based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history to select the most efficient feedback method. This allows the feedback unit to improve feedback efficiency by providing the optimal feedback method based on past feedback history. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or without AI. For example, the feedback unit can input past feedback data into AI, which can then automatically select the optimal feedback method.

[0088] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. The user's emotions are estimated using technologies such as facial recognition or voice analysis. For example, if the user is stressed, the feedback unit can prioritize receiving important feedback and respond quickly. Also, if the user is relaxed, the feedback unit can sequentially receive and respond to detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can first receive the most important feedback and respond quickly. In this way, the feedback unit can provide feedback prioritization according to the user's emotions, enabling a quick response to important feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit inputs the user's facial expression data into a generating AI, which can then automatically estimate the emotion.

[0089] The feedback unit can prioritize receiving highly relevant feedback when receiving feedback, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. Highly relevant feedback includes, but is not limited to, feedback based on local needs or feedback related to business operations. For example, if a user is in a specific region, the feedback unit will prioritize receiving feedback relevant to that region. Furthermore, if a user is in a specific city, the feedback unit can prioritize receiving feedback relevant to that city. In addition, if a user is in a specific country, the feedback unit can prioritize receiving feedback relevant to that country. This allows the feedback unit to provide geographical location-based feedback, enabling responses tailored to user needs. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can input the user's geographical location information into an AI, which can then automatically prioritize receiving highly relevant feedback.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The suggestion unit can estimate the user's emotions and adjust the level of detail of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. If the user is relaxed, the suggestion unit can provide suggestions with detailed explanations and customizable options. Furthermore, if the user is in a hurry, the suggestion unit can provide short suggestions that can be quickly reviewed. In this way, the suggestion unit can improve user satisfaction by providing a level of detail in suggestions that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can then automatically estimate emotions.

[0092] The generation unit can estimate the user's emotions and adjust the drawing generation method based on the estimated user emotions. For example, if the user is stressed, the generation unit can generate a simple and intuitive drawing. If the user is relaxed, the generation unit can generate a detailed drawing and offer customizable options. Furthermore, if the user is in a hurry, the generation unit can generate a simplified drawing that can be quickly reviewed. In this way, the generation unit can improve user satisfaction by providing a drawing generation method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user facial expression data into the generation AI, which can then automatically estimate emotions.

[0093] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring updated information on laws and regulations based on the estimated emotions. For example, if the user is stressed, the acquisition unit can reduce the frequency of acquiring updated information and acquire only important information. If the user is relaxed, the acquisition unit can acquire and provide detailed updated information frequently. Furthermore, if the user is in a hurry, the acquisition unit can prioritize acquiring the most important updated information and provide it quickly. In this way, the acquisition unit can reduce the burden on the user by providing update information acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input the user's facial expression data into the generative AI, which can automatically estimate emotions.

[0094] The feedback unit can estimate the user's emotions and adjust the feedback process based on those emotions. For example, if the user is stressed, the feedback unit can provide a simple feedback form and minimize the input steps. If the user is relaxed, the feedback unit can provide detailed feedback options and suggest customizable input methods. Furthermore, if the user is in a hurry, the feedback unit can prioritize voice input and receive feedback quickly. In this way, the feedback unit can reduce the user's burden by providing feedback methods that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's facial expression data into a generative AI, which can then automatically estimate emotions.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide a short, to-the-point suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with more detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can provide a short suggestion that can be quickly reviewed. In this way, the suggestion unit can improve user satisfaction by providing suggestion lengths that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can then automatically estimate emotions.

[0096] The proposal department can apply different proposal algorithms depending on the office category when making a proposal. Office categories include, but are not limited to, sales offices, development offices, and administrative offices. Proposal algorithms include, but are not limited to, rule-based and machine learning-based algorithms. For example, the proposal department might propose an efficiency-focused layout for a general office. It could also propose a creativity-focused layout for a creative office. Furthermore, for a medical facility, it could propose a layout that prioritizes safety and hygiene. This allows the proposal department to provide optimal proposals tailored to the office category, thereby suggesting layouts that meet user needs. Some or all of the above processing in the proposal department may be performed using, for example, AI, or not. For example, the proposal department could input the office category into an AI, which could then automatically apply a different proposal algorithm.

[0097] The generation unit can customize the level of detail of the drawings based on the current office situation during generation. The current office situation includes, but is not limited to, the current layout and usage. The level of detail of the drawings includes, but is not limited to, detailed drawings and simplified drawings. For example, the generation unit generates detailed drawings based on the current office layout. The generation unit can also adjust the required level of detail according to the current office situation. Furthermore, the generation unit can generate optimal drawings considering the current office situation. In this way, the generation unit can generate drawings that meet user needs by providing a level of detail of drawings that matches the current office situation. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the current office layout data into the AI, and the AI ​​can automatically customize the level of detail of the drawings.

[0098] The data acquisition unit can analyze the update history of past laws and regulations and select the optimal acquisition method. The update history of past laws and regulations includes, but is not limited to, analysis of past update data and update patterns. For example, the data acquisition unit can prioritize acquiring important update information for laws and regulations based on past update history. The data acquisition unit can also identify laws and regulations with high update frequency and select an efficient acquisition method. Furthermore, the data acquisition unit can analyze past update history and select the most efficient acquisition method. As a result, the data acquisition unit enables efficient information acquisition by providing the optimal acquisition method based on past update history. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input past update data into AI, and the AI ​​can automatically select the optimal acquisition method.

[0099] The feedback unit can analyze the user's past feedback history to select the optimal feedback method when receiving feedback. Past feedback history includes, but is not limited to, analysis of past feedback data and feedback patterns. For example, the feedback unit may prioritize suggesting feedback methods (voice, text, etc.) that the user has frequently used in the past. The feedback unit can also predict and suggest feedback methods to be used during specific time periods based on the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history to select the most efficient feedback method. This allows the feedback unit to improve feedback efficiency by providing the optimal feedback method based on past feedback history. Some or all of the above processing in the feedback unit may be performed using, for example, AI, or not. For example, the feedback unit can input past feedback data into an AI, which can then automatically select the optimal feedback method.

[0100] The proposal department can adjust the level of detail in a proposal based on the importance of the laws and regulations. The importance of laws and regulations includes, but is not limited to, legal binding force and the presence or absence of penalties. The level of detail in a proposal includes, but is not limited to, the provision of detailed drawings or simplified layouts. The proposal department will, for example, prioritize proposals based on important laws and regulations and provide detailed explanations. Furthermore, the proposal department will simplify proposals based on less important laws and regulations, and provide additional content.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The reception desk receives basic office layout information. This information includes the office area, the number of rooms, and the types of furniture and equipment to be placed. For example, it accepts the office area and the number of rooms entered by the user. The reception desk can also accept the types of furniture and equipment to be placed. Furthermore, the reception desk can automatically calculate the office area and the number of rooms based on the information entered by the user. Step 2: Based on the information received by the reception department, the proposal department proposes the optimal office layout, taking into account laws and internal regulations. For example, it considers securing evacuation routes in accordance with the Fire Service Act and ensuring a suitable working environment in accordance with the Labor Standards Act. The proposal department uses AI to analyze the input information and propose the optimal office layout. Step 3: The generation unit generates detailed drawings based on the layout proposed by the proposal unit. The generation unit automatically generates specific layout plans and dimension drawings. The generation unit uses AI to generate specific layout plans and dimension drawings based on the proposed layout.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0105] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, acquisition unit, and feedback unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the office area and number of rooms entered by the user. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal office layout considering laws and regulations. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates specific layout drawings and dimension drawings. The acquisition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and acquires the latest updated information on laws and regulations via the internet. The feedback unit is implemented by, for example, the control unit 46A of the smart device 14 and receives user feedback through an online form. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0113] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0114] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0115] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0116] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0117] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, acquisition unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the office area and number of rooms entered by the user. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal office layout considering laws and regulations. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates specific layout diagrams and dimension drawings. The acquisition unit is implemented by the specific processing unit 290 of the data processing unit 12 and obtains the latest updated information on laws and regulations via the internet. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and receives user feedback via an online form. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, acquisition unit, and feedback unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the office area and number of rooms entered by the user. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal office layout considering laws and regulations. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates specific layout diagrams and dimension diagrams. The acquisition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and acquires the latest updated information on laws and regulations via the internet. The feedback unit is implemented by, for example, the control unit 46A of the headset terminal 314 and receives user feedback through an online form. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0147] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, acquisition unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the office area and number of rooms entered by the user. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal office layout considering laws and regulations. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates specific layout drawings and dimension drawings. The acquisition unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and obtains the latest updated information on laws and regulations via the internet. The feedback unit is implemented by, for example, the control unit 46A of the robot 414 and receives user feedback through an online form. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0158] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0159] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0160] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0164] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0165] 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.

[0166] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0167] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0168] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0169] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0171] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0172] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0173] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0174] (Note 1) The reception desk receives basic information about the office layout, Based on the information received by the aforementioned reception department, the proposal department proposes office layouts in accordance with laws and regulations and internal rules. The system includes a generation unit that generates detailed drawings based on the layout proposed by the proposal unit. A system characterized by the following features. (Note 2) It is equipped with an acquisition unit that obtains updated information on laws and internal regulations. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a feedback section for receiving user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose layouts based on evaluation criteria for efficiency, safety, and comfort. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generates detailed drawings including specific layout plans, dimension drawings, and evacuation route diagrams. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic office layout information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The input fields are automatically customized based on the office area and the number of rooms. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Considering the user's geographical location, add input fields based on region-specific laws and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is We analyze users' social media activity and provide relevant office layout trend information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the layout suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of laws and internal regulations. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When submitting proposals, different proposal algorithms are applied depending on the office category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making proposals, prioritize them based on the intended use of the office. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the office. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and adjusts the drawing generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the system analyzes the user's past drawing creation history to select the optimal generation method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail in the drawing is customized based on the current state of the office. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is The system estimates the user's emotions and determines the priority of drawings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, the system takes the user's geographical location information into consideration to generate the optimal drawing. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system analyzes the user's social media activity and suggests relevant drawings. The system described in Appendix 1, characterized by the features described herein. (Note 24) The acquisition unit is, The system estimates user sentiment and adjusts the timing of obtaining updated information on laws and internal regulations based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 25) The acquisition unit is, We will analyze the update history of past laws and internal regulations and select the most suitable acquisition method. The system described in Appendix 2, characterized by the features described herein. (Note 26) The acquisition unit is, It estimates the user's sentiment and determines the priority of update information to retrieve based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 27) The acquisition unit is, When acquiring updated information on laws and regulations, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is received based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When receiving feedback, the system analyzes the user's past feedback history to select the most suitable method for receiving it. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When receiving feedback, the system prioritizes receiving highly relevant feedback by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk receives basic information about the office layout, Based on the information received by the aforementioned reception department, the proposal department proposes office layouts in accordance with laws and regulations and internal rules. The system includes a generation unit that generates detailed drawings based on the layout proposed by the proposal unit. A system characterized by the following features.

2. It is equipped with an acquisition unit that obtains updated information on laws and internal regulations. The system according to feature 1.

3. It includes a feedback section for receiving user feedback. The system according to feature 1.

4. The aforementioned proposal section is, We propose layouts based on evaluation criteria for efficiency, safety, and comfort. The system according to feature 1.

5. The generating unit is Generates detailed drawings including specific layout plans, dimension drawings, and evacuation route diagrams. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for basic office layout information based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system according to feature 1.

8. The aforementioned reception unit is The input fields are automatically customized based on the office area and the number of rooms. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input fields based on those emotions. The system according to feature 1.

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