system

Abstract

The system according to this embodiment aims to enable entrepreneurs and startup companies to efficiently handle everything from office rental to interior construction in one go. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a search unit, a reservation unit, a generation unit, an output unit, a simulation unit, a contract unit, and a notification unit. The reception unit takes user conditions as input. The collection unit collects information based on the conditions entered by the reception unit. The search unit searches for the most suitable office property based on the information collected by the collection unit. The reservation unit makes a reservation to view the office property found by the search unit. The generation unit automatically generates an interior design after the office property reserved by the reservation unit has been decided. The output unit outputs a report of the interior design generated by the generation unit. The simulation unit provides a cost simulation based on the report output by the output unit. The contract unit handles the contract for the office property, contacts construction companies and moving companies, and purchases furniture in one go, based on the cost simulation provided by the simulation unit. The notification unit creates a schedule and sends reminder notifications based on the contracts and purchases made by the contract unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for entrepreneurs and startup companies to efficiently perform tasks from office rental to interior construction in one go.

[0005] The system according to the embodiment aims to enable entrepreneurs and startup companies to efficiently perform tasks from office rental to interior construction in one go.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, a search unit, a reservation unit, a generation unit, an output unit, a simulation unit, a contract unit, and a notification unit. The reception unit takes user conditions as input. The collection unit collects information based on the conditions entered by the reception unit. The search unit searches for the most suitable office property based on the information collected by the collection unit. The reservation unit makes a reservation to view the office property found by the search unit. The generation unit automatically generates an interior design after the reservation unit has determined the office property. The output unit outputs a report of the interior design generated by the generation unit. The simulation unit provides a cost simulation based on the report output by the output unit. The contract unit handles the contract for the office property, contacts construction companies and moving companies, and purchases furniture in one go, based on the cost simulation provided by the simulation unit. The notification unit creates a schedule and sends reminder notifications based on the contracts and purchases made by the contract unit. [Effects of the Invention]

[0007] The system according to this embodiment allows entrepreneurs and startup companies to efficiently handle everything from office rental to interior construction in one go. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 32 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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 support system according to an embodiment of the present invention is a comprehensive system that supports entrepreneurs and startup companies from office rental to interior construction. This office support system allows users to input conditions such as office capacity, design, area, and budget, and the generating AI collects information from the internet and databases to search for the most suitable office property. Based on the search results, viewing appointments are made. After the office property is decided, the generating AI automatically generates an interior design and outputs a report. This report includes details of the office property, a list of office furniture, and estimates from construction companies. Furthermore, it integrates with e-commerce sites to provide necessary cost simulations. Users can handle office property contracts, contact construction and moving companies, and purchase chairs and tables all in one go. Finally, a schedule is created, and reminder notifications are sent for interior construction, furniture arrival, and office occupancy. This creates an environment where entrepreneurs can focus on their core business. For example, when a user inputs conditions such as office capacity, design, area, and budget, the generating AI collects information from the internet and databases to search for the most suitable office property. Based on the search results, viewing appointments are made. After the office property is decided, the generating AI automatically generates an interior design and outputs a report. This report includes details of the office property, a list of office furniture, and quotes from construction companies. Furthermore, it integrates with e-commerce sites to provide necessary cost simulations. Users can handle everything from contracting the office property, contacting construction and moving companies, to purchasing chairs and tables, all in one place. Finally, they can create a schedule and receive reminder notifications for interior work, furniture arrival, and office occupancy. This creates an environment where entrepreneurs can focus on their core business. Thus, the office support system can provide comprehensive support to entrepreneurs and startups, from office rental to interior construction.

[0029] The office support system according to this embodiment comprises a reception unit, a collection unit, a search unit, a reservation unit, a generation unit, an output unit, a simulation unit, a contract unit, and a notification unit. The reception unit receives user conditions as input. User conditions include, for example, office location, size, budget, etc., but are not limited to such examples. The reception unit, for example, stores the conditions entered by the user in a database. The reception unit can also analyze the conditions entered by the user and issue instructions for collecting necessary information. The collection unit collects information from the internet and databases. For example, the collection unit collects information on office properties from real estate information sites and databases on the internet. The collection unit can also collect information for searching for the most suitable office property based on the user's conditions. The search unit searches for the most suitable office property based on the information collected by the collection unit. For example, the search unit analyzes the collected information and searches for office properties that match the user's conditions. The search unit can also present the search results to the user. The reservation unit makes reservations for viewing the office properties found by the search unit. The reservation unit, for example, allows users to make online reservations for viewing office properties they have selected. The reservation unit can also manage the viewing schedule. The generation unit automatically generates interior designs after the office property has been selected by the reservation unit. The generation unit uses, for example, generation AI to automatically generate office interior designs. The generation unit can also output the generated interior designs as a report. The output unit outputs the interior design report generated by the generation unit. The output unit outputs a report that includes, for example, details of the office property, a list of office furniture, and estimates from construction companies. The output unit can also provide this report to the user. The simulation unit provides cost simulations based on the report output by the output unit. The simulation unit provides necessary cost simulations, for example, by linking with e-commerce sites. The simulation unit can also present the cost simulation results to the user.The Contracts Department handles the contracting of office properties, contact with construction and moving companies, and purchase of furniture, all based on cost simulations provided by the Simulation Department. For example, the Contracts Department can handle the contracting procedures for office properties online. The Contracts Department can also handle the contact with construction and moving companies and the purchase of furniture all at once. The Notification Department creates schedules and sends reminder notifications based on the contracts and purchases made by the Contracts Department. For example, the Notification Department sends reminder notifications for interior construction, furniture arrival, and the start of office use. The Notification Department can also manage the reminder notification schedule. As a result, the office support system according to this embodiment can perform office property searches, viewing reservations, automatic generation of interior designs, cost simulations, contracts, and reminder notifications all at once based on the user's conditions.

[0030] The reception desk inputs the user's requirements. These requirements may include, but are not limited to, office location, size, and budget. The reception desk can, for example, save the user's input into a database. It can also analyze the user's input and issue instructions for collecting necessary information. Specifically, the user's input is sent to the system through a dedicated input form. The input form is designed for intuitive user operation, allowing for easy input of items such as location, size, and budget. The entered data is saved to the database in real time and used for subsequent processing. Furthermore, the reception desk is equipped with AI to analyze the user's input, automatically classifying the entered requirements and issuing instructions for collecting necessary information. For example, if a user requests an office in the city center with a size of 100 square meters or more and a budget of 500,000 yen or less per month, the reception desk will analyze these requirements and issue specific information collection instructions to the data collection department. In this way, the reception desk can respond flexibly to the user's needs.

[0031] The data collection unit gathers information from the internet and databases. For example, it collects information on office properties from real estate information websites and databases on the internet. The data collection unit can also collect information to search for the most suitable office property based on the user's criteria. Specifically, the data collection unit accesses multiple real estate information websites and automatically collects information on office properties that match the user's criteria. The collected information is stored in a database and used for analysis by the subsequent search unit. The data collection unit can efficiently collect information from the internet using web scraping technology. Furthermore, the data collection unit can also collect information on specific areas or property characteristics based on the user's criteria. For example, if a user wants an office within a 5-minute walk from a train station, the data collection unit will prioritize collecting information on properties that match that criterion. In this way, the data collection unit can efficiently collect information that meets the user's needs, improving the accuracy and efficiency of the entire system.

[0032] The search unit searches for the most suitable office properties based on the information collected by the data collection unit. For example, the search unit analyzes the collected information to find office properties that match the user's criteria. The search unit can also present the search results to the user. Specifically, the search unit retrieves the collected information from a database and filters it based on the user's criteria. Natural language processing technology using AI is used for filtering to accurately understand the user's criteria and identify the most suitable properties. The search results are presented to the user in a visually easy-to-understand format and include detailed property information, photos, maps, etc. Furthermore, the search unit can learn the user's past search history and preferences to provide more personalized search results. For example, it can prioritize displaying similar properties based on the characteristics of properties the user has searched for in the past. In this way, the search unit can quickly find and present the most suitable office properties that meet the user's needs.

[0033] The reservation department handles the scheduling of office property viewings found by the search department. For example, the reservation department allows users to schedule viewings of selected office properties online. The reservation department can also manage the viewing schedule. Specifically, the reservation department accepts the user's desired viewing date and time through an input form, and then coordinates with real estate agents to confirm the viewing reservation based on that information. The reservation department manages the viewing schedule in a calendar format and can also send reminder notifications to users regarding viewing dates and times. Furthermore, the reservation department handles changes and cancellations of viewing reservations, supporting users in flexibly adjusting their schedules. For example, if a user needs to change their viewing date and time due to a sudden change in plans, the reservation department accepts the request and quickly adjusts the new viewing date and time. In this way, the reservation department enhances user convenience and enables smooth viewing reservations.

[0034] The generation unit automatically generates interior designs after the office property reserved by the reservation unit has been finalized. For example, the generation unit uses a generation AI to automatically generate office interior designs. The generation unit can also output the generated interior designs as a report. Specifically, the generation unit receives input such as the user's desired interior style, color scheme, and functionality, and the generation AI generates the optimal interior design based on these conditions. The generation AI learns from past design data and trend information, enabling it to provide creative designs that meet user needs. The generated interior designs are output as 3D models and high-resolution images, allowing users to visually confirm the designs. Furthermore, the generation unit can create a list of specific furniture and decorations based on the generated designs and propose them to the user. In this way, the generation unit can automatically generate interior designs that meet user preferences, maximizing the appeal of the office.

[0035] The output unit outputs a report of the interior design generated by the generation unit. The output unit outputs a report that includes, for example, details of the office property, a list of office furniture, and estimates from construction companies. The output unit can also provide the report to the user. Specifically, the output unit creates a detailed report based on the interior design data provided by the generation unit. The report includes basic information about the office property, the interior design concept, a list of furniture and decorations to be used, and estimates from construction companies. This allows the user to grasp the overall picture of the office interior. Furthermore, the output unit can output the report in PDF or printable format and provide it to the user. The user can use these reports as a reference to make final decisions about the interior design. In this way, the output unit can provide the user with detailed information and support the office interior design process.

[0036] The simulation unit provides cost simulations based on reports output by the output unit. For example, the simulation unit can integrate with e-commerce sites to provide necessary cost simulations. The simulation unit can also present the results of the cost simulation to the user. Specifically, the simulation unit calculates the total cost based on the furniture and construction estimates listed in the report. The simulation unit can obtain the latest pricing information from e-commerce sites and perform cost simulations in real time. Furthermore, the simulation unit provides cost simulations based on different scenarios, allowing users to compare multiple options. For example, it can simulate and present to the user the cost differences resulting from different interior designs and furniture combinations. In this way, the simulation unit can provide users with detailed cost information and support budget management.

[0037] The Contracts Department handles the entire process, from contracting for office properties to contacting construction and moving companies and purchasing furniture, based on cost simulations provided by the Simulation Department. For example, the Contracts Department can handle office property contract procedures online. It can also handle contacting construction and moving companies and purchasing furniture in a single process. Specifically, the Contracts Department provides a platform for users to complete the contract procedures for their selected office properties online. The Contracts Department automates communication with construction and moving companies, ensuring smooth collaboration. Furthermore, the Contracts Department handles furniture purchasing procedures in a single process, enabling quick procurement of necessary furniture. In addition, the Contracts Department manages the progress of contract procedures in real time and notifies users as needed. In this way, the Contracts Department significantly reduces the user's workload and supports efficient contract procedures.

[0038] The Notifications Department creates schedules and sends reminder notifications based on contracts and purchases made by the Contracts Department. For example, the Notifications Department sends reminder notifications for interior construction, furniture arrival, and office use commencement. The Notifications Department can also manage the reminder notification schedule. Specifically, the Notifications Department creates a schedule of important events based on information provided by the Contracts Department. The Notifications Department sends reminder notifications to users to help them remember important events. Reminder notifications are sent using multiple communication methods, such as email, SMS, and app push notifications. Furthermore, the Notifications Department can collect user feedback and continuously improve the accuracy and timing of notifications. In this way, the Notifications Department can reliably convey important information to users and support smooth office use.

[0039] The data collection unit can collect information from the internet and databases. For example, the data collection unit can collect information on office properties from real estate information sites and databases on the internet. The data collection unit can also collect information to search for the most suitable office property based on the user's conditions. In this way, by collecting information from the internet and databases, information for searching for the most suitable office property can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze information in order to collect information on office properties from real estate information sites on the internet and collect information for searching for the most suitable office property.

[0040] The generation unit can automatically generate interior designs using a generation AI. For example, the generation unit can automatically generate an office interior design using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. The generation unit can also output the generated interior design as a report. This allows for efficient creation of interior designs by automatically generating them using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can automatically generate an office interior design using a generation AI and output that design as a report.

[0041] The output unit can output a report that includes details of the office property, a list of office furniture, and a quote from a construction company. For example, the output unit can output a report that includes details of the office property, a list of office furniture, and a quote from a construction company. The output unit can also provide this report to the user. This allows the user to obtain all the necessary information in one place by outputting a report that includes details of the office property, a list of office furniture, and a quote from a construction company. Some or all of the processing described above in the output unit may be performed using AI, or not. For example, the output unit can use AI to automatically generate a report that includes details of the office property, a list of office furniture, and a quote from a construction company, and then provide that report to the user.

[0042] The simulation unit can provide cost simulations in conjunction with e-commerce sites. For example, the simulation unit can provide the necessary cost simulations in conjunction with e-commerce sites. The simulation unit can also present the results of the cost simulations to the user. This allows users to understand the necessary costs by providing cost simulations in conjunction with e-commerce sites. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze data obtained from e-commerce sites and provide cost simulations.

[0043] The contracts department can handle the entire process of securing office space, contacting construction and moving companies, and purchasing chairs and tables, all in one go. For example, the contracts department can handle the office space contract process online. It can also handle contacting construction and moving companies and purchasing furniture in a single process. This streamlines the process for users by consolidating the office space contract, contacting construction and moving companies, and purchasing chairs and tables. Some or all of the above processes in the contracts department may be performed using AI, or not. For example, the contracts department could use AI to automate the office space contract process and handle contacting construction and moving companies and purchasing furniture in a single process.

[0044] The notification unit can send reminder notifications for things like interior construction, furniture arrival, and office occupancy. The notification unit can also manage the schedule for these reminder notifications. This makes it easier for users to keep track of their schedules by sending reminder notifications for things like interior construction, furniture arrival, and office occupancy. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can use AI to automatically generate reminder notifications for things like interior construction, furniture arrival, and office occupancy, and then provide those notifications to the user.

[0045] The reception desk can analyze the user's past condition input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as candidates. 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 conditions to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past condition input history. 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 use AI to analyze the user's past condition input history and suggest the optimal input method based on the analysis results.

[0046] The reception desk can customize input fields based on the user's current project status when conditions are entered. For example, if a user starts a new project, the reception desk will automatically display the necessary condition fields. Furthermore, if a user updates an ongoing project, the reception desk can prioritize displaying relevant condition fields. Additionally, if a user manages multiple projects, the reception desk can customize and display different condition fields for each project. This allows users to efficiently enter the necessary conditions by customizing input fields based on their current project status. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can use AI to understand the user's current project status and customize input fields based on that status.

[0047] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location when they enter conditions. For example, if the user is in a specific area, the reception desk will prioritize displaying input fields related to that area. Furthermore, if the user is on the move, the reception desk can suggest the most relevant input fields based on their current location. Additionally, if the user is working on a project in a specific region, the reception desk can prioritize displaying input fields related to that region. This allows users to efficiently input the necessary information by prioritizing the display of highly relevant input fields based on their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can use AI to analyze the user's geographical location and prioritize displaying highly relevant input fields based on that information.

[0048] The reception desk can analyze the user's social media activity when conditions are entered and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on information the user has shared on social media. The reception desk can also prioritize displaying input fields related to a specific project based on the user's social media activity. Furthermore, the reception desk can suggest relevant input fields based on topics the user has shown interest in on social media. In this way, relevant input fields can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use AI to analyze the user's social media activity and suggest relevant input fields based on the results of that analysis.

[0049] The data collection unit can analyze the user's past search history to select the optimal data collection method when collecting information. For example, the data collection unit can prioritize collecting relevant information based on information the user has previously searched for. The data collection unit can also prioritize collecting specific information sources from the user's past search history. Furthermore, the data collection unit can analyze the user's past search history to select the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's past search history and select the optimal data collection method based on the analysis results.

[0050] The data collection unit can filter the data to be collected based on the user's current project status. For example, if a user starts a new project, the data collection unit will prioritize collecting relevant information. It can also prioritize collecting necessary information if a user updates an ongoing project. Furthermore, if a user manages multiple projects, the data collection unit can filter and collect different information for each project. This allows for efficient collection of the information the user needs by filtering the data based on their current project status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's current project status and filter the data to be collected based on that status.

[0051] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific area, the data collection unit will prioritize the collection of information related to that area. Furthermore, if the user is on the move, the data collection unit can also collect the most relevant information based on their current location. Additionally, if the user is working on a project in a specific region, the data collection unit can prioritize the collection of information related to that region. This allows the user to efficiently collect the information they need by prioritizing the collection of highly relevant information while considering their geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can use AI to analyze the user's geographical location and prioritize the collection of highly relevant information based on that analysis.

[0052] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. The data collection unit can also prioritize the collection of information related to a specific project from the user's social media activity. Furthermore, the data collection unit can collect relevant information based on topics the user has shown interest in on social media. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze a user's social media activity and collect relevant information based on the results of that analysis.

[0053] The search unit can analyze the user's past search history and apply the optimal search algorithm during a search. For example, the search unit can prioritize displaying relevant search results based on information the user has previously searched for. It can also prioritize displaying specific information sources based on the user's past search history. Furthermore, the search unit can analyze the user's past search history and apply the most efficient search algorithm. This allows the optimal search algorithm to be applied by analyzing the user's past search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can use AI to analyze the user's past search history and apply the optimal search algorithm based on the analysis results.

[0054] The search unit can filter search results based on the user's current project status during a search. For example, if a user starts a new project, the search unit will prioritize displaying relevant search results. It can also prioritize displaying necessary search results if a user updates an ongoing project. Furthermore, if a user manages multiple projects, the search unit can filter and display different search results for each project. This allows users to efficiently obtain the necessary search results by filtering them based on their current project status. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can use AI to analyze the user's current project status and filter search results based on that status.

[0055] The search unit can prioritize displaying highly relevant search results by considering the user's geographical location during a search. For example, if the user is in a specific area, the search unit will prioritize displaying search results related to that area. Furthermore, if the user is on the move, the search unit can display the most relevant search results based on their current location. Additionally, if the user is working on a project in a specific region, the search unit can prioritize displaying search results related to that region. This allows users to efficiently obtain the necessary search results by prioritizing highly relevant results based on their geographical location. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit can use AI to analyze the user's geographical location and prioritize displaying highly relevant search results based on that information.

[0056] The search unit can analyze a user's social media activity during a search and display relevant search results. For example, the search unit can display relevant search results based on information shared by the user on social media. The search unit can also prioritize displaying search results related to specific projects based on the user's social media activity. Furthermore, the search unit can display relevant search results based on topics the user has shown interest in on social media. In this way, relevant search results can be displayed by analyzing the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can use AI to analyze a user's social media activity and display relevant search results based on the analysis results.

[0057] The reservation department can analyze a user's past reservation history when they make a reservation for a tour and suggest the most suitable reservation method. For example, the reservation department can prioritize reservations for relevant tours based on the tour schedule the user has previously booked. The reservation department can also prioritize suggesting specific tour methods based on the user's past reservation history. Furthermore, the reservation department can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, by analyzing the user's past reservation history, the optimal reservation method can be suggested. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can use AI to analyze the user's past reservation history and suggest the most suitable reservation method based on the results of that analysis.

[0058] The reservation system can customize the reservation date and time based on the user's current schedule when booking a tour. For example, if a user is starting a new project, the reservation system will prioritize booking related tours. It can also prioritize booking necessary tours if a user is updating an ongoing project. Furthermore, if a user is managing multiple projects, the reservation system can customize and book different tours for each project. This allows users to efficiently book tours by customizing the reservation date and time based on their current schedule. Some or all of the above processes in the reservation system may be performed using AI, for example, or not. For example, the reservation system can use AI to analyze the user's current schedule and customize the reservation date and time based on that schedule.

[0059] The reservation system can prioritize highly relevant reservations when a user makes a reservation, taking into account their geographical location. For example, if a user is in a specific area, the reservation system will prioritize reservations related to that area. Furthermore, if a user is on the move, the reservation system can also reserve the most suitable reservations based on their current location. Additionally, if a user is working on a project in a specific region, the reservation system can prioritize reservations related to that region. This allows users to efficiently make reservations by prioritizing highly relevant reservations based on their geographical location. Some or all of the above processing in the reservation system may be performed using AI, or not. For example, the reservation system could use AI to analyze the user's geographical location and prioritize highly relevant reservations based on that information.

[0060] The reservation department can analyze a user's social media activity when they make a reservation for a tour and suggest relevant tours. For example, the reservation department can book relevant tours based on information the user has shared on social media. It can also prioritize tours related to specific projects based on the user's social media activity. Furthermore, the reservation department can book relevant tours based on topics the user has shown interest in on social media. This allows the department to suggest relevant tours by analyzing the user's social media activity. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can use AI to analyze a user's social media activity and suggest relevant tours based on the analysis results.

[0061] The generation unit can analyze the user's past design history and propose the optimal design when generating interior designs. For example, the generation unit's AI can propose related designs based on designs previously selected by the user. The generation unit can also prioritize suggesting specific design styles based on the user's past design history. Furthermore, the generation unit can analyze the user's past design history and have the AI ​​propose the most efficient design. In this way, the optimal design can be proposed by analyzing the user's past design history. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can use the generation AI to analyze the user's past design history and propose the optimal design based on the analysis results.

[0062] The generation unit can customize the design based on the user's current project status when generating interior designs. For example, if a user starts a new project, the generation unit's AI will prioritize generating the relevant designs. The generation unit can also prioritize generating the necessary designs when a user updates an ongoing project. Furthermore, if a user manages multiple projects, the generation unit can customize and generate different designs for each project. This allows users to efficiently obtain the designs they need by customizing the designs based on their current project status. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not. For example, the generation unit can use the generation AI to analyze the user's current project status and customize the design based on that status.

[0063] The generation unit can prioritize generating highly relevant designs by considering the user's geographical location information when generating interior designs. For example, if the user is in a specific area, the generation unit's AI will prioritize generating designs related to that area. Furthermore, if the user is on the move, the generation unit's AI can generate the optimal design based on their current location. Additionally, if the user is working on a project in a specific region, the generation unit's AI can prioritize generating designs related to that region. This allows users to efficiently obtain the designs they need by prioritizing the generation of highly relevant designs based on their geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without it. For example, the generation unit can use AI to analyze the user's geographical location information and prioritize the generation of highly relevant designs based on that information.

[0064] The generation unit can analyze the user's social media activity and propose relevant designs when generating interior designs. For example, the generation unit's AI can propose relevant designs based on information shared by the user on social media. The generation unit can also have the AI ​​prioritize suggesting specific design styles based on the user's social media activity. Furthermore, the generation unit can have the AI ​​propose relevant designs based on topics the user has shown interest in on social media. In this way, relevant designs can be proposed by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can use AI to analyze the user's social media activity and propose relevant designs based on the analysis results.

[0065] The output unit can analyze the user's past report history and propose the optimal output method when generating reports. For example, the output unit can prioritize outputting relevant reports based on reports previously generated by the user. It can also prioritize suggesting specific output methods based on the user's past report history. Furthermore, the output unit can analyze the user's past report history and propose the most efficient output method. This allows the output unit to propose the optimal output method by analyzing the user's past report history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can use AI to analyze the user's past report history and propose the optimal output method based on the analysis results.

[0066] The output unit can customize report content based on the user's current project status when generating reports. For example, if a user starts a new project, the output unit will prioritize outputting relevant reports. It can also prioritize outputting necessary reports when a user updates an ongoing project. Furthermore, if a user manages multiple projects, the output unit can customize and output different reports for each project. This allows users to efficiently obtain the reports they need by customizing report content based on their current project status. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can use AI to analyze the user's current project status and customize report content based on that status.

[0067] The output unit can prioritize outputting highly relevant reports by considering the user's geographical location when generating reports. For example, if the user is in a specific area, the output unit will prioritize outputting reports related to that area. Furthermore, if the user is on the move, the output unit can output the most relevant reports based on their current location. Additionally, if the user is working on a project in a specific region, the output unit can prioritize outputting reports related to that region. This allows users to efficiently obtain the reports they need by prioritizing highly relevant reports based on their geographical location. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit can use AI to analyze the user's geographical location and prioritize outputting highly relevant reports based on that information.

[0068] The output unit can prioritize outputting highly relevant reports by considering the user's geographical location when generating reports. For example, if the user is in a specific area, the output unit will prioritize outputting reports related to that area. Furthermore, if the user is on the move, the output unit can output the most relevant reports based on their current location. Additionally, if the user is working on a project in a specific region, the output unit can prioritize outputting reports related to that region. This allows users to efficiently obtain the reports they need by prioritizing highly relevant reports based on their geographical location. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit can use AI to analyze the user's geographical location and prioritize outputting highly relevant reports based on that information.

[0069] The output unit can analyze the user's social media activity and suggest relevant reports when generating reports. For example, the output unit can output relevant reports based on information shared by the user on social media. The output unit can also prioritize outputting reports related to specific projects based on the user's social media activity. Furthermore, the output unit can output relevant reports based on topics the user has shown interest in on social media. This allows for the suggestion of relevant reports by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can use AI to analyze the user's social media activity and suggest relevant reports based on the analysis results.

[0070] The simulation unit can analyze the user's past simulation history and propose the optimal simulation method during cost simulation. For example, the simulation unit can prioritize providing relevant simulations based on the user's past simulations. It can also prioritize suggesting specific simulation methods based on the user's past simulation history. Furthermore, the simulation unit can analyze the user's past simulation history and propose the most efficient simulation method. Thus, by analyzing the user's past simulation history, the optimal simulation method can be proposed. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze the user's past simulation history and propose the optimal simulation method based on the analysis results.

[0071] The simulation unit can customize the simulation content based on the user's current project status during cost simulations. For example, if the user starts a new project, the simulation unit will prioritize providing relevant simulations. It can also prioritize providing necessary simulations if the user updates an ongoing project. Furthermore, if the user manages multiple projects, the simulation unit can customize and provide different simulations for each project. This allows the user to efficiently obtain the necessary simulations by customizing the simulation content based on their current project status. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze the user's current project status and customize the simulation content based on that status.

[0072] The simulation unit can prioritize highly relevant simulations during cost simulations by considering the user's geographical location. For example, if the user is in a specific area, the simulation unit will prioritize providing simulations related to that area. Furthermore, if the user is on the move, the simulation unit can provide the optimal simulation based on their current location. Additionally, if the user is working on a project in a specific region, the simulation unit can prioritize providing simulations related to that region. This allows the user to efficiently obtain the necessary simulations by prioritizing highly relevant simulations based on their geographical location. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze the user's geographical location and prioritize highly relevant simulations based on that information.

[0073] The simulation unit can analyze a user's social media activity during cost simulations and propose relevant simulations. For example, the simulation unit can provide relevant simulations based on information shared by the user on social media. The simulation unit can also prioritize providing simulations related to specific projects based on the user's social media activity. Furthermore, the simulation unit can provide relevant simulations based on topics the user has shown interest in on social media. This allows the simulation unit to propose relevant simulations by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze a user's social media activity and propose relevant simulations based on the analysis results.

[0074] The contracts department can analyze a user's past contract history during the contracting process and propose the most suitable procedure. For example, the contracts department can prioritize providing relevant contract procedures based on the user's past contracts. It can also prioritize suggesting specific contract procedures based on the user's past contract history. Furthermore, the contracts department can analyze the user's past contract history and propose the most efficient contract procedure. This allows for the proposal of the optimal procedure by analyzing the user's past contract history. Some or all of the above processes in the contracts department may be performed using AI, for example, or not. For example, the contracts department can use AI to analyze a user's past contract history and propose the optimal procedure based on the analysis results.

[0075] The contracts department can customize contract procedures based on the user's current project status. For example, if a user starts a new project, the contracts department will prioritize providing the relevant contract procedures. It can also prioritize providing the necessary contract procedures if a user is renewing an ongoing project. Furthermore, if a user manages multiple projects, the contracts department can customize and provide different contract procedures for each project. This allows users to efficiently perform the necessary procedures by customizing them based on their current project status. Some or all of the above processing in the contracts department may be performed using AI, for example, or not. For example, the contracts department can use AI to analyze the user's current project status and customize procedures based on that status.

[0076] The contracts department can prioritize relevant procedures during the contract process, taking into account the user's geographical location. For example, if a user is in a specific area, the contracts department can prioritize contract procedures related to that area. Furthermore, if a user is on the move, the contracts department can provide the most relevant contract procedures based on their current location. Additionally, if a user is working on a project in a specific region, the contracts department can prioritize contract procedures related to that region. This allows users to efficiently complete contract procedures by prioritizing relevant procedures based on their geographical location. Some or all of the above processing in the contracts department may be performed using AI, or not. For example, the contracts department can use AI to analyze the user's geographical location and prioritize relevant procedures based on that information.

[0077] The contracts department can analyze a user's social media activity during the contracting process and propose relevant procedures. For example, the contracts department can provide relevant contract procedures based on information shared by the user on social media. The contracts department can also prioritize providing contract procedures related to a specific project based on the user's social media activity. Furthermore, the contracts department can provide relevant contract procedures based on topics the user has shown interest in on social media. This allows for the proposal of relevant procedures by analyzing the user's social media activity. Some or all of the above processes in the contracts department may be performed using AI, for example, or not. For example, the contracts department can use AI to analyze a user's social media activity and propose relevant procedures based on the analysis results.

[0078] The notification unit can analyze the user's past notification history to suggest the optimal notification method when sending reminder notifications. For example, the notification unit can prioritize providing relevant reminder notifications based on notifications the user has received in the past. The notification unit can also prioritize suggesting specific notification methods based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history to suggest the most efficient notification method. In this way, the notification unit can suggest the optimal notification method by analyzing the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to analyze the user's past notification history and suggest the optimal notification method based on the analysis results.

[0079] The notification unit can customize the content of reminder notifications based on the user's current schedule. For example, if a user starts a new project, the notification unit will prioritize providing relevant reminder notifications. It can also prioritize providing necessary reminder notifications if a user updates an ongoing project. Furthermore, if a user manages multiple projects, the notification unit can customize and provide different reminder notifications for each project. This allows users to efficiently receive the notifications they need by customizing the content based on their current schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can use AI to analyze the user's current schedule and customize the notification content based on that schedule.

[0080] The notification unit can prioritize highly relevant notifications by considering the user's geographical location when sending reminder notifications. For example, if the user is in a specific area, the notification unit can prioritize notifications related to that area. Furthermore, if the user is on the move, the notification unit can provide optimal notifications based on their current location. Additionally, if the user is working on a project in a specific region, the notification unit can prioritize notifications related to that region. This allows users to efficiently receive the notifications they need by prioritizing highly relevant notifications based on their geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can use AI to analyze the user's geographical location and prioritize highly relevant notifications based on that information.

[0081] The notification unit can analyze the user's social media activity and suggest relevant notifications when sending reminder notifications. For example, the notification unit can provide relevant notifications based on information the user has shared on social media. It can also prioritize notifications related to specific projects based on the user's social media activity. Furthermore, the notification unit can provide relevant notifications based on topics the user has shown interest in on social media. In this way, relevant notifications can be suggested by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to analyze the user's social media activity and suggest relevant notifications based on the results of that analysis.

[0082] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0083] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display conditions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions that the user will use during specific time periods based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method.

[0084] The data collection unit can analyze the user's past search history to select the optimal data collection method. For example, it can prioritize collecting relevant information based on information the user has previously searched for. It can also prioritize collecting information from specific sources based on the user's past search history. Furthermore, it can analyze the user's past search history to select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past search history.

[0085] The search function can analyze a user's past search history and apply the optimal search algorithm during a search. For example, it can prioritize displaying relevant search results based on information the user has previously searched for. It can also prioritize displaying specific information sources based on the user's past search history. Furthermore, it can analyze the user's past search history and apply the most efficient search algorithm. In this way, by analyzing the user's past search history, the optimal search algorithm can be applied.

[0086] The design generation unit can analyze the user's past design history to propose the optimal design when generating interior designs. For example, the AI ​​can suggest related designs based on designs the user has previously selected. It can also prioritize suggesting specific design styles based on the user's past design history. Furthermore, the AI ​​can analyze the user's past design history and propose the most efficient design. In this way, by analyzing the user's past design history, the AI ​​can propose the optimal design.

[0087] The output unit can analyze the user's past report history and suggest the optimal output method when generating reports. For example, it can prioritize outputting related reports based on reports the user has previously generated. It can also prioritize suggesting specific output methods based on the user's past report history. Furthermore, it can analyze the user's past report history and suggest the most efficient output method. In this way, by analyzing the user's past report history, it can suggest the optimal output method.

[0088] The following briefly describes the processing flow for example form 1.

[0089] Step 1: The reception desk enters the user's criteria. These criteria may include, for example, the office's location, size, and budget. The reception desk can also save the user's entered criteria to a database and issue instructions to collect the necessary information. Step 2: The collection unit gathers information from the internet and databases. The collection unit collects information on office properties from real estate information sites and databases on the internet, and gathers information to search for the most suitable office properties based on the user's criteria. Step 3: The search unit searches for the most suitable office property based on the information collected by the data collection unit. The search unit analyzes the collected information, searches for office properties that match the user's criteria, and presents the search results to the user. Step 4: The reservation department makes viewing reservations for office properties found by the search department. The reservation department makes viewing reservations for the office properties selected by the user online and manages the viewing reservation schedule. Step 5: The generation unit automatically generates interior designs after the office property reserved by the reservation unit has been determined. The generation unit uses generation AI to automatically generate office interior designs and outputs the generated interior designs as a report. Step 6: The output unit outputs the interior design report generated by the generation unit. The output unit outputs a report that includes details of the office property, a list of office furniture, and a quote from the construction company, and provides it to the user. Step 7: The simulation unit provides a cost simulation based on the report output by the output unit. The simulation unit works in conjunction with the e-commerce site to provide the necessary cost simulation and presents the cost simulation results to the user. Step 8: The Contracts Department handles the entire process, from contracting for office properties to contacting construction and moving companies and purchasing furniture, based on the cost simulations provided by the Simulation Department. The Contracts Department handles the office property contract procedures online and also handles the contact with construction and moving companies and the furniture purchase procedures in one go. Step 9: The Notifications Department creates schedules and sends reminder notices based on contracts and purchases made by the Contracts Department. The Notifications Department sends reminder notices for things like interior work, furniture arrival, and office occupancy start, and manages the reminder notice schedule.

[0090] (Example of form 2) The office support system according to an embodiment of the present invention is a comprehensive system that supports entrepreneurs and startup companies from office rental to interior construction. This office support system allows users to input conditions such as office capacity, design, area, and budget, and the generating AI collects information from the internet and databases to search for the most suitable office property. Based on the search results, viewing appointments are made. After the office property is decided, the generating AI automatically generates an interior design and outputs a report. This report includes details of the office property, a list of office furniture, and estimates from construction companies. Furthermore, it integrates with e-commerce sites to provide necessary cost simulations. Users can handle office property contracts, contact construction and moving companies, and purchase chairs and tables all in one go. Finally, a schedule is created, and reminder notifications are sent for interior construction, furniture arrival, and office occupancy. This creates an environment where entrepreneurs can focus on their core business. For example, when a user inputs conditions such as office capacity, design, area, and budget, the generating AI collects information from the internet and databases to search for the most suitable office property. Based on the search results, viewing appointments are made. After the office property is decided, the generating AI automatically generates an interior design and outputs a report. This report includes details of the office property, a list of office furniture, and quotes from construction companies. Furthermore, it integrates with e-commerce sites to provide necessary cost simulations. Users can handle everything from contracting the office property, contacting construction and moving companies, to purchasing chairs and tables, all in one place. Finally, they can create a schedule and receive reminder notifications for interior work, furniture arrival, and office occupancy. This creates an environment where entrepreneurs can focus on their core business. Thus, the office support system can provide comprehensive support to entrepreneurs and startups, from office rental to interior construction.

[0091] The office support system according to this embodiment comprises a reception unit, a collection unit, a search unit, a reservation unit, a generation unit, an output unit, a simulation unit, a contract unit, and a notification unit. The reception unit receives user conditions as input. User conditions include, for example, office location, size, budget, etc., but are not limited to such examples. The reception unit, for example, stores the conditions entered by the user in a database. The reception unit can also analyze the conditions entered by the user and issue instructions for collecting necessary information. The collection unit collects information from the internet and databases. For example, the collection unit collects information on office properties from real estate information sites and databases on the internet. The collection unit can also collect information for searching for the most suitable office property based on the user's conditions. The search unit searches for the most suitable office property based on the information collected by the collection unit. For example, the search unit analyzes the collected information and searches for office properties that match the user's conditions. The search unit can also present the search results to the user. The reservation unit makes reservations for viewing the office properties found by the search unit. The reservation unit, for example, allows users to make online reservations for viewing office properties they have selected. The reservation unit can also manage the viewing schedule. The generation unit automatically generates interior designs after the office property has been selected by the reservation unit. The generation unit uses, for example, generation AI to automatically generate office interior designs. The generation unit can also output the generated interior designs as a report. The output unit outputs the interior design report generated by the generation unit. The output unit outputs a report that includes, for example, details of the office property, a list of office furniture, and estimates from construction companies. The output unit can also provide this report to the user. The simulation unit provides cost simulations based on the report output by the output unit. The simulation unit provides necessary cost simulations, for example, by linking with e-commerce sites. The simulation unit can also present the cost simulation results to the user.The Contracts Department handles the contracting of office properties, contact with construction and moving companies, and purchase of furniture, all based on cost simulations provided by the Simulation Department. For example, the Contracts Department can handle the contracting procedures for office properties online. The Contracts Department can also handle the contact with construction and moving companies and the purchase of furniture all at once. The Notification Department creates schedules and sends reminder notifications based on the contracts and purchases made by the Contracts Department. For example, the Notification Department sends reminder notifications for interior construction, furniture arrival, and the start of office use. The Notification Department can also manage the reminder notification schedule. As a result, the office support system according to this embodiment can perform office property searches, viewing reservations, automatic generation of interior designs, cost simulations, contracts, and reminder notifications all at once based on the user's conditions.

[0092] The reception desk inputs the user's requirements. These requirements may include, but are not limited to, office location, size, and budget. The reception desk can, for example, save the user's input into a database. It can also analyze the user's input and issue instructions for collecting necessary information. Specifically, the user's input is sent to the system through a dedicated input form. The input form is designed for intuitive user operation, allowing for easy input of items such as location, size, and budget. The entered data is saved to the database in real time and used for subsequent processing. Furthermore, the reception desk is equipped with AI to analyze the user's input, automatically classifying the entered requirements and issuing instructions for collecting necessary information. For example, if a user requests an office in the city center with a size of 100 square meters or more and a budget of 500,000 yen or less per month, the reception desk will analyze these requirements and issue specific information collection instructions to the data collection department. In this way, the reception desk can respond flexibly to the user's needs.

[0093] The data collection unit gathers information from the internet and databases. For example, it collects information on office properties from real estate information websites and databases on the internet. The data collection unit can also collect information to search for the most suitable office property based on the user's criteria. Specifically, the data collection unit accesses multiple real estate information websites and automatically collects information on office properties that match the user's criteria. The collected information is stored in a database and used for analysis by the subsequent search unit. The data collection unit can efficiently collect information from the internet using web scraping technology. Furthermore, the data collection unit can also collect information on specific areas or property characteristics based on the user's criteria. For example, if a user wants an office within a 5-minute walk from a train station, the data collection unit will prioritize collecting information on properties that match that criterion. In this way, the data collection unit can efficiently collect information that meets the user's needs, improving the accuracy and efficiency of the entire system.

[0094] The search unit searches for the most suitable office properties based on the information collected by the data collection unit. For example, the search unit analyzes the collected information to find office properties that match the user's criteria. The search unit can also present the search results to the user. Specifically, the search unit retrieves the collected information from a database and filters it based on the user's criteria. Natural language processing technology using AI is used for filtering to accurately understand the user's criteria and identify the most suitable properties. The search results are presented to the user in a visually easy-to-understand format and include detailed property information, photos, maps, etc. Furthermore, the search unit can learn the user's past search history and preferences to provide more personalized search results. For example, it can prioritize displaying similar properties based on the characteristics of properties the user has searched for in the past. In this way, the search unit can quickly find and present the most suitable office properties that meet the user's needs.

[0095] The reservation department handles the scheduling of office property viewings found by the search department. For example, the reservation department allows users to schedule viewings of selected office properties online. The reservation department can also manage the viewing schedule. Specifically, the reservation department accepts the user's desired viewing date and time through an input form, and then coordinates with real estate agents to confirm the viewing reservation based on that information. The reservation department manages the viewing schedule in a calendar format and can also send reminder notifications to users regarding viewing dates and times. Furthermore, the reservation department handles changes and cancellations of viewing reservations, supporting users in flexibly adjusting their schedules. For example, if a user needs to change their viewing date and time due to a sudden change in plans, the reservation department accepts the request and quickly adjusts the new viewing date and time. In this way, the reservation department enhances user convenience and enables smooth viewing reservations.

[0096] The generation unit automatically generates interior designs after the office property reserved by the reservation unit has been finalized. For example, the generation unit uses a generation AI to automatically generate office interior designs. The generation unit can also output the generated interior designs as a report. Specifically, the generation unit receives input such as the user's desired interior style, color scheme, and functionality, and the generation AI generates the optimal interior design based on these conditions. The generation AI learns from past design data and trend information, enabling it to provide creative designs that meet user needs. The generated interior designs are output as 3D models and high-resolution images, allowing users to visually confirm the designs. Furthermore, the generation unit can create a list of specific furniture and decorations based on the generated designs and propose them to the user. In this way, the generation unit can automatically generate interior designs that meet user preferences, maximizing the appeal of the office.

[0097] The output unit outputs a report of the interior design generated by the generation unit. The output unit outputs a report that includes, for example, details of the office property, a list of office furniture, and estimates from construction companies. The output unit can also provide the report to the user. Specifically, the output unit creates a detailed report based on the interior design data provided by the generation unit. The report includes basic information about the office property, the interior design concept, a list of furniture and decorations to be used, and estimates from construction companies. This allows the user to grasp the overall picture of the office interior. Furthermore, the output unit can output the report in PDF or printable format and provide it to the user. The user can use these reports as a reference to make final decisions about the interior design. In this way, the output unit can provide the user with detailed information and support the office interior design process.

[0098] The simulation unit provides cost simulations based on reports output by the output unit. For example, the simulation unit can integrate with e-commerce sites to provide necessary cost simulations. The simulation unit can also present the results of the cost simulation to the user. Specifically, the simulation unit calculates the total cost based on the furniture and construction estimates listed in the report. The simulation unit can obtain the latest pricing information from e-commerce sites and perform cost simulations in real time. Furthermore, the simulation unit provides cost simulations based on different scenarios, allowing users to compare multiple options. For example, it can simulate and present to the user the cost differences resulting from different interior designs and furniture combinations. In this way, the simulation unit can provide users with detailed cost information and support budget management.

[0099] The Contracts Department handles the entire process, from contracting for office properties to contacting construction and moving companies and purchasing furniture, based on cost simulations provided by the Simulation Department. For example, the Contracts Department can handle office property contract procedures online. It can also handle contacting construction and moving companies and purchasing furniture in a single process. Specifically, the Contracts Department provides a platform for users to complete the contract procedures for their selected office properties online. The Contracts Department automates communication with construction and moving companies, ensuring smooth collaboration. Furthermore, the Contracts Department handles furniture purchasing procedures in a single process, enabling quick procurement of necessary furniture. In addition, the Contracts Department manages the progress of contract procedures in real time and notifies users as needed. In this way, the Contracts Department significantly reduces the user's workload and supports efficient contract procedures.

[0100] The Notifications Department creates schedules and sends reminder notifications based on contracts and purchases made by the Contracts Department. For example, the Notifications Department sends reminder notifications for interior construction, furniture arrival, and office use commencement. The Notifications Department can also manage the reminder notification schedule. Specifically, the Notifications Department creates a schedule of important events based on information provided by the Contracts Department. The Notifications Department sends reminder notifications to users to help them remember important events. Reminder notifications are sent using multiple communication methods, such as email, SMS, and app push notifications. Furthermore, the Notifications Department can collect user feedback and continuously improve the accuracy and timing of notifications. In this way, the Notifications Department can reliably convey important information to users and support smooth office use.

[0101] The data collection unit can collect information from the internet and databases. For example, the data collection unit can collect information on office properties from real estate information sites and databases on the internet. The data collection unit can also collect information to search for the most suitable office property based on the user's conditions. In this way, by collecting information from the internet and databases, information for searching for the most suitable office property can be obtained. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze information in order to collect information on office properties from real estate information sites on the internet and collect information for searching for the most suitable office property.

[0102] The generation unit can automatically generate interior designs using a generation AI. For example, the generation unit can automatically generate an office interior design using a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or an image generation AI, but is not limited to such examples. The generation unit can also output the generated interior design as a report. This allows for efficient creation of interior designs by automatically generating them using a generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can automatically generate an office interior design using a generation AI and output that design as a report.

[0103] The output unit can output a report that includes details of the office property, a list of office furniture, and a quote from a construction company. For example, the output unit can output a report that includes details of the office property, a list of office furniture, and a quote from a construction company. The output unit can also provide this report to the user. This allows the user to obtain all the necessary information in one place by outputting a report that includes details of the office property, a list of office furniture, and a quote from a construction company. Some or all of the processing described above in the output unit may be performed using AI, or not. For example, the output unit can use AI to automatically generate a report that includes details of the office property, a list of office furniture, and a quote from a construction company, and then provide that report to the user.

[0104] The simulation unit can provide cost simulations in conjunction with e-commerce sites. For example, the simulation unit can provide the necessary cost simulations in conjunction with e-commerce sites. The simulation unit can also present the results of the cost simulations to the user. This allows users to understand the necessary costs by providing cost simulations in conjunction with e-commerce sites. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze data obtained from e-commerce sites and provide cost simulations.

[0105] The contracts department can handle the entire process of securing office space, contacting construction and moving companies, and purchasing chairs and tables, all in one go. For example, the contracts department can handle the office space contract process online. It can also handle contacting construction and moving companies and purchasing furniture in a single process. This streamlines the process for users by consolidating the office space contract, contacting construction and moving companies, and purchasing chairs and tables. Some or all of the above processes in the contracts department may be performed using AI, or not. For example, the contracts department could use AI to automate the office space contract process and handle contacting construction and moving companies and purchasing furniture in a single process.

[0106] The notification unit can send reminder notifications for things like interior construction, furniture arrival, and office occupancy. The notification unit can also manage the schedule for these reminder notifications. This makes it easier for users to keep track of their schedules by sending reminder notifications for things like interior construction, furniture arrival, and office occupancy. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can use AI to automatically generate reminder notifications for things like interior construction, furniture arrival, and office occupancy, and then provide those notifications to the user.

[0107] The reception desk can estimate the user's emotions and adjust the condition input interface based on the estimated emotions. 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 condition input. This allows the user to comfortably input conditions by adjusting the condition input interface based on their 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 or not. For example, the reception desk can use AI to estimate the user's emotions and adjust the condition input interface based on those emotions.

[0108] The reception desk can analyze the user's past condition input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as candidates. 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 conditions to be used during specific time periods based on the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past condition input history. 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 use AI to analyze the user's past condition input history and suggest the optimal input method based on the analysis results.

[0109] The reception desk can customize input fields based on the user's current project status when conditions are entered. For example, if a user starts a new project, the reception desk will automatically display the necessary condition fields. Furthermore, if a user updates an ongoing project, the reception desk can prioritize displaying relevant condition fields. Additionally, if a user manages multiple projects, the reception desk can customize and display different condition fields for each project. This allows users to efficiently enter the necessary conditions by customizing input fields based on their current project status. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can use AI to understand the user's current project status and customize input fields based on that status.

[0110] The reception desk can estimate the user's emotions and prioritize input fields based on those emotions. For example, if the user is stressed, the reception desk can prioritize displaying important input fields and simplify the input process. If the user is relaxed, the reception desk can also provide detailed input fields and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize displaying the most important input fields to allow for quick input. This allows the user to prioritize important fields by prioritizing input fields based on 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 processing in the reception desk may be performed using AI or not. For example, the reception desk can use AI to estimate the user's emotions and prioritize input fields based on those emotions.

[0111] The reception desk can prioritize displaying input fields that are highly relevant to the user's geographical location when they enter conditions. For example, if the user is in a specific area, the reception desk will prioritize displaying input fields related to that area. Furthermore, if the user is on the move, the reception desk can suggest the most relevant input fields based on their current location. Additionally, if the user is working on a project in a specific region, the reception desk can prioritize displaying input fields related to that region. This allows users to efficiently input the necessary information by prioritizing the display of highly relevant input fields based on their geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can use AI to analyze the user's geographical location and prioritize displaying highly relevant input fields based on that information.

[0112] The reception desk can analyze the user's social media activity when conditions are entered and suggest relevant input fields. For example, the reception desk can suggest relevant input fields based on information the user has shared on social media. The reception desk can also prioritize displaying input fields related to a specific project based on the user's social media activity. Furthermore, the reception desk can suggest relevant input fields based on topics the user has shown interest in on social media. In this way, relevant input fields can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can use AI to analyze the user's social media activity and suggest relevant input fields based on the results of that analysis.

[0113] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only important information. If the user is relaxed, the data collection unit can also collect detailed information and provide it to the user. Furthermore, if the user is in a hurry, the data collection unit can quickly collect information and provide only the necessary information. In this way, by adjusting the timing of information collection based on the user's emotions, the user can obtain the information they need at the right time. 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can use AI to estimate the user's emotions and adjust the timing of information collection based on those emotions.

[0114] The data collection unit can analyze the user's past search history to select the optimal data collection method when collecting information. For example, the data collection unit can prioritize collecting relevant information based on information the user has previously searched for. The data collection unit can also prioritize collecting specific information sources from the user's past search history. Furthermore, the data collection unit can analyze the user's past search history to select the most efficient data collection method. This allows the optimal data collection method to be selected by analyzing the user's past search history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's past search history and select the optimal data collection method based on the analysis results.

[0115] The data collection unit can filter the data to be collected based on the user's current project status. For example, if a user starts a new project, the data collection unit will prioritize collecting relevant information. It can also prioritize collecting necessary information if a user updates an ongoing project. Furthermore, if a user manages multiple projects, the data collection unit can filter and collect different information for each project. This allows for efficient collection of the information the user needs by filtering the data based on their current project status. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze the user's current project status and filter the data to be collected based on that status.

[0116] The data collection unit can estimate the user's emotions and prioritize the information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting and providing important information. If the user is relaxed, the data collection unit can also collect and provide detailed information. Furthermore, if the user is in a hurry, the data collection unit can quickly collect and provide necessary information. This allows the user to obtain important information preferentially by prioritizing the information to collect based on 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 processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to estimate the user's emotions and prioritize the information to collect based on those emotions.

[0117] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific area, the data collection unit will prioritize the collection of information related to that area. Furthermore, if the user is on the move, the data collection unit can also collect the most relevant information based on their current location. Additionally, if the user is working on a project in a specific region, the data collection unit can prioritize the collection of information related to that region. This allows the user to efficiently collect the information they need by prioritizing the collection of highly relevant information while considering their geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can use AI to analyze the user's geographical location and prioritize the collection of highly relevant information based on that analysis.

[0118] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. The data collection unit can also prioritize the collection of information related to a specific project from the user's social media activity. Furthermore, the data collection unit can collect relevant information based on topics the user has shown interest in on social media. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to analyze a user's social media activity and collect relevant information based on the results of that analysis.

[0119] The search unit can estimate the user's emotions and adjust how search results are displayed based on those emotions. For example, if the user is stressed, the search unit can provide a simple and highly visible display. If the user is relaxed, the search unit can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the search unit can provide a concise display. By adjusting how search results are displayed based on the user's emotions, the user can comfortably browse the search results. 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 search unit may be performed using AI, for example, or without AI. For example, the search unit can use AI to estimate the user's emotions and adjust how search results are displayed based on those emotions.

[0120] The search unit can analyze the user's past search history and apply the optimal search algorithm during a search. For example, the search unit can prioritize displaying relevant search results based on information the user has previously searched for. It can also prioritize displaying specific information sources based on the user's past search history. Furthermore, the search unit can analyze the user's past search history and apply the most efficient search algorithm. This allows the optimal search algorithm to be applied by analyzing the user's past search history. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can use AI to analyze the user's past search history and apply the optimal search algorithm based on the analysis results.

[0121] The search unit can filter search results based on the user's current project status during a search. For example, if a user starts a new project, the search unit will prioritize displaying relevant search results. It can also prioritize displaying necessary search results if a user updates an ongoing project. Furthermore, if a user manages multiple projects, the search unit can filter and display different search results for each project. This allows users to efficiently obtain the necessary search results by filtering them based on their current project status. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can use AI to analyze the user's current project status and filter search results based on that status.

[0122] The search unit can estimate the user's emotions and prioritize search results based on those emotions. For example, if the user is stressed, the search unit will prioritize displaying important search results. It can also provide detailed search results if the user is relaxed. Furthermore, if the user is in a hurry, the search unit can quickly display the necessary search results. This allows the user to obtain important search results preferentially by prioritizing them based on 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 processing in the search unit may be performed using AI, or not. For example, the search unit can use AI to estimate the user's emotions and prioritize search results based on those emotions.

[0123] The search unit can prioritize displaying highly relevant search results by considering the user's geographical location during a search. For example, if the user is in a specific area, the search unit will prioritize displaying search results related to that area. Furthermore, if the user is on the move, the search unit can display the most relevant search results based on their current location. Additionally, if the user is working on a project in a specific region, the search unit can prioritize displaying search results related to that region. This allows users to efficiently obtain the necessary search results by prioritizing highly relevant results based on their geographical location. Some or all of the above processing in the search unit may be performed using AI, or not. For example, the search unit can use AI to analyze the user's geographical location and prioritize displaying highly relevant search results based on that information.

[0124] The search unit can analyze a user's social media activity during a search and display relevant search results. For example, the search unit can display relevant search results based on information shared by the user on social media. The search unit can also prioritize displaying search results related to specific projects based on the user's social media activity. Furthermore, the search unit can display relevant search results based on topics the user has shown interest in on social media. In this way, relevant search results can be displayed by analyzing the user's social media activity. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can use AI to analyze a user's social media activity and display relevant search results based on the analysis results.

[0125] The booking system can estimate the user's emotions and adjust the timing of tour reservations based on those emotions. For example, if the user is stressed, the booking system can reduce the frequency of tour reservations and only book important tours. If the user is relaxed, the booking system can also provide a detailed tour schedule and book tours at a time that suits the user. Furthermore, if the user is in a hurry, the booking system can quickly book tours and only book necessary tours. This allows users to make tour reservations comfortably by adjusting the timing of tour reservations based on their 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 booking system may be performed using AI or not. For example, the booking system can use AI to estimate the user's emotions and adjust the timing of tour reservations based on those emotions.

[0126] The reservation department can analyze a user's past reservation history when they make a reservation for a tour and suggest the most suitable reservation method. For example, the reservation department can prioritize reservations for relevant tours based on the tour schedule the user has previously booked. The reservation department can also prioritize suggesting specific tour methods based on the user's past reservation history. Furthermore, the reservation department can analyze the user's past reservation history and suggest the most efficient reservation method. In this way, by analyzing the user's past reservation history, the optimal reservation method can be suggested. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can use AI to analyze the user's past reservation history and suggest the most suitable reservation method based on the results of that analysis.

[0127] The reservation system can customize the reservation date and time based on the user's current schedule when booking a tour. For example, if a user is starting a new project, the reservation system will prioritize booking related tours. It can also prioritize booking necessary tours if a user is updating an ongoing project. Furthermore, if a user is managing multiple projects, the reservation system can customize and book different tours for each project. This allows users to efficiently book tours by customizing the reservation date and time based on their current schedule. Some or all of the above processes in the reservation system may be performed using AI, for example, or not. For example, the reservation system can use AI to analyze the user's current schedule and customize the reservation date and time based on that schedule.

[0128] The booking system can estimate the user's emotions and prioritize bookings based on those emotions. For example, if the user is stressed, the system can prioritize important bookings and simplify the booking process. If the user is relaxed, the system can offer detailed booking options and suggest customizable booking methods. Furthermore, if the user is in a hurry, the system can prioritize the most important bookings to allow for quick booking. This allows users to prioritize important bookings by prioritizing them based on 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 processing in the booking system may be performed using AI or not. For example, the booking system can use AI to estimate the user's emotions and prioritize bookings based on those emotions.

[0129] The reservation system can prioritize highly relevant reservations when a user makes a reservation, taking into account their geographical location. For example, if a user is in a specific area, the reservation system will prioritize reservations related to that area. Furthermore, if a user is on the move, the reservation system can also reserve the most suitable reservations based on their current location. Additionally, if a user is working on a project in a specific region, the reservation system can prioritize reservations related to that region. This allows users to efficiently make reservations by prioritizing highly relevant reservations based on their geographical location. Some or all of the above processing in the reservation system may be performed using AI, or not. For example, the reservation system could use AI to analyze the user's geographical location and prioritize highly relevant reservations based on that information.

[0130] The reservation department can analyze a user's social media activity when they make a reservation for a tour and suggest relevant tours. For example, the reservation department can book relevant tours based on information the user has shared on social media. It can also prioritize tours related to specific projects based on the user's social media activity. Furthermore, the reservation department can book relevant tours based on topics the user has shown interest in on social media. This allows the department to suggest relevant tours by analyzing the user's social media activity. Some or all of the above processes in the reservation department may be performed using AI, for example, or not. For example, the reservation department can use AI to analyze a user's social media activity and suggest relevant tours based on the analysis results.

[0131] The generation unit can estimate the user's emotions and adjust the way the interior design is presented based on those estimated emotions. For example, if the user is relaxed, the generation AI can generate a relaxed design. If the user is in a hurry, the generation AI can also generate a simple and efficient design. Furthermore, if the user is excited, the generation AI can generate a visually stimulating design. This allows the user to comfortably view the interior design by adjusting its presentation based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as 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-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can use a generation AI to estimate the user's emotions and adjust the way the interior design is presented based on those emotions.

[0132] The generation unit can analyze the user's past design history and propose the optimal design when generating interior designs. For example, the generation unit's AI can propose related designs based on designs previously selected by the user. The generation unit can also prioritize suggesting specific design styles based on the user's past design history. Furthermore, the generation unit can analyze the user's past design history and have the AI ​​propose the most efficient design. In this way, the optimal design can be proposed by analyzing the user's past design history. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can use the generation AI to analyze the user's past design history and propose the optimal design based on the analysis results.

[0133] The generation unit can customize the design based on the user's current project status when generating interior designs. For example, if a user starts a new project, the generation unit's AI will prioritize generating the relevant designs. The generation unit can also prioritize generating the necessary designs when a user updates an ongoing project. Furthermore, if a user manages multiple projects, the generation unit can customize and generate different designs for each project. This allows users to efficiently obtain the designs they need by customizing the designs based on their current project status. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not. For example, the generation unit can use the generation AI to analyze the user's current project status and customize the design based on that status.

[0134] The generation unit can estimate the user's emotions and determine design priorities based on those estimated emotions. For example, if the user is stressed, the generation unit's generating AI will prioritize generating important designs. If the user is relaxed, the generation unit's generating AI can also generate detailed designs. Furthermore, if the user is in a hurry, the generation unit's generating AI can quickly generate the necessary designs. This ensures that users receive important designs preferentially by prioritizing them based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can use a generation AI to estimate the user's emotions and determine design priorities based on those emotions.

[0135] The generation unit can prioritize generating highly relevant designs by considering the user's geographical location information when generating interior designs. For example, if the user is in a specific area, the generation unit's AI will prioritize generating designs related to that area. Furthermore, if the user is on the move, the generation unit's AI can generate the optimal design based on their current location. Additionally, if the user is working on a project in a specific region, the generation unit's AI can prioritize generating designs related to that region. This allows users to efficiently obtain the designs they need by prioritizing the generation of highly relevant designs based on their geographical location information. Some or all of the above-described processes in the generation unit may be performed using, for example, the generation AI, or without it. For example, the generation unit can use AI to analyze the user's geographical location information and prioritize the generation of highly relevant designs based on that information.

[0136] The generation unit can analyze the user's social media activity and propose relevant designs when generating interior designs. For example, the generation unit's AI can propose relevant designs based on information shared by the user on social media. The generation unit can also have the AI ​​prioritize suggesting specific design styles based on the user's social media activity. Furthermore, the generation unit can have the AI ​​propose relevant designs based on topics the user has shown interest in on social media. In this way, relevant designs can be proposed by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can use AI to analyze the user's social media activity and propose relevant designs based on the analysis results.

[0137] The output unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is stressed, the output unit can provide a simple and easy-to-read report. If the user is relaxed, the output unit can provide a report with more detailed information. Furthermore, if the user is in a hurry, the output unit can provide a concise report. This allows the user to comfortably review the report by adjusting its presentation based on their 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 processing described above in the output unit may be performed using AI or not. For example, the output unit can use AI to estimate the user's emotions and adjust the report's presentation based on those emotions.

[0138] The output unit can analyze the user's past report history and propose the optimal output method when generating reports. For example, the output unit can prioritize outputting relevant reports based on reports previously generated by the user. It can also prioritize suggesting specific output methods based on the user's past report history. Furthermore, the output unit can analyze the user's past report history and propose the most efficient output method. This allows the output unit to propose the optimal output method by analyzing the user's past report history. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can use AI to analyze the user's past report history and propose the optimal output method based on the analysis results.

[0139] The output unit can customize report content based on the user's current project status when generating reports. For example, if a user starts a new project, the output unit will prioritize outputting relevant reports. It can also prioritize outputting necessary reports when a user updates an ongoing project. Furthermore, if a user manages multiple projects, the output unit can customize and output different reports for each project. This allows users to efficiently obtain the reports they need by customizing report content based on their current project status. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can use AI to analyze the user's current project status and customize report content based on that status.

[0140] The output unit can estimate the user's emotions and prioritize reports based on those emotions. For example, if the user is stressed, the output unit will prioritize outputting important reports. It can also output detailed reports if the user is relaxed. Furthermore, if the user is in a hurry, the output unit can quickly output the necessary reports. This ensures that users receive important reports preferentially by prioritizing reports based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can use AI to estimate the user's emotions and prioritize reports based on those emotions.

[0141] The output unit can prioritize outputting highly relevant reports by considering the user's geographical location when generating reports. For example, if the user is in a specific area, the output unit will prioritize outputting reports related to that area. Furthermore, if the user is on the move, the output unit can output the most relevant reports based on their current location. Additionally, if the user is working on a project in a specific region, the output unit can prioritize outputting reports related to that region. This allows users to efficiently obtain the reports they need by prioritizing highly relevant reports based on their geographical location. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit can use AI to analyze the user's geographical location and prioritize outputting highly relevant reports based on that information.

[0142] The output unit can prioritize outputting highly relevant reports by considering the user's geographical location when generating reports. For example, if the user is in a specific area, the output unit will prioritize outputting reports related to that area. Furthermore, if the user is on the move, the output unit can output the most relevant reports based on their current location. Additionally, if the user is working on a project in a specific region, the output unit can prioritize outputting reports related to that region. This allows users to efficiently obtain the reports they need by prioritizing highly relevant reports based on their geographical location. Some or all of the above processing in the output unit may be performed using AI, or not. For example, the output unit can use AI to analyze the user's geographical location and prioritize outputting highly relevant reports based on that information.

[0143] The output unit can analyze the user's social media activity and suggest relevant reports when generating reports. For example, the output unit can output relevant reports based on information shared by the user on social media. The output unit can also prioritize outputting reports related to specific projects based on the user's social media activity. Furthermore, the output unit can output relevant reports based on topics the user has shown interest in on social media. This allows for the suggestion of relevant reports by analyzing the user's social media activity. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can use AI to analyze the user's social media activity and suggest relevant reports based on the analysis results.

[0144] The simulation unit can estimate the user's emotions and adjust the presentation of the cost simulation based on the estimated emotions. For example, if the user is stressed, the simulation unit can provide a simple and easy-to-understand cost simulation. If the user is relaxed, the simulation unit can also provide a cost simulation with more detailed information. Furthermore, if the user is in a hurry, the simulation unit can provide a concise cost simulation. By adjusting the presentation of the cost simulation based on the user's emotions, the user can comfortably review the cost simulation. 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 simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to estimate the user's emotions and adjust the presentation of the cost simulation based on those emotions.

[0145] The simulation unit can analyze the user's past simulation history and propose the optimal simulation method during cost simulation. For example, the simulation unit can prioritize providing relevant simulations based on the user's past simulations. It can also prioritize suggesting specific simulation methods based on the user's past simulation history. Furthermore, the simulation unit can analyze the user's past simulation history and propose the most efficient simulation method. Thus, by analyzing the user's past simulation history, the optimal simulation method can be proposed. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze the user's past simulation history and propose the optimal simulation method based on the analysis results.

[0146] The simulation unit can customize the simulation content based on the user's current project status during cost simulations. For example, if the user starts a new project, the simulation unit will prioritize providing relevant simulations. It can also prioritize providing necessary simulations if the user updates an ongoing project. Furthermore, if the user manages multiple projects, the simulation unit can customize and provide different simulations for each project. This allows the user to efficiently obtain the necessary simulations by customizing the simulation content based on their current project status. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze the user's current project status and customize the simulation content based on that status.

[0147] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is stressed, the simulation unit will prioritize providing important simulations. It can also provide detailed simulations if the user is relaxed. Furthermore, if the user is in a hurry, the simulation unit can quickly provide the necessary simulations. This ensures that users receive important simulations preferentially by prioritizing simulations based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI or not. For example, the simulation unit can use AI to estimate the user's emotions and determine the priority of simulations based on those emotions.

[0148] The simulation unit can prioritize highly relevant simulations during cost simulations by considering the user's geographical location. For example, if the user is in a specific area, the simulation unit will prioritize providing simulations related to that area. Furthermore, if the user is on the move, the simulation unit can provide the optimal simulation based on their current location. Additionally, if the user is working on a project in a specific region, the simulation unit can prioritize providing simulations related to that region. This allows the user to efficiently obtain the necessary simulations by prioritizing highly relevant simulations based on their geographical location. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze the user's geographical location and prioritize highly relevant simulations based on that information.

[0149] The simulation unit can analyze a user's social media activity during cost simulations and propose relevant simulations. For example, the simulation unit can provide relevant simulations based on information shared by the user on social media. The simulation unit can also prioritize providing simulations related to specific projects based on the user's social media activity. Furthermore, the simulation unit can provide relevant simulations based on topics the user has shown interest in on social media. This allows the simulation unit to propose relevant simulations by analyzing the user's social media activity. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can use AI to analyze a user's social media activity and propose relevant simulations based on the analysis results.

[0150] The contracts department can estimate the user's emotions and adjust the way contract procedures are presented based on those estimated emotions. For example, if the user is nervous, the contracts department can provide simple and highly visible contract procedures. If the user is relaxed, the contracts department can also provide contract procedures that include detailed information. Furthermore, if the user is in a hurry, the contracts department can provide contract procedures that get straight to the point. In this way, by adjusting the way contract procedures are presented based on the user's emotions, the user can comfortably complete the contract process. 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 contracts department may be performed using AI, for example, or not using AI. For example, the contracts department can use AI to estimate the user's emotions and adjust the way contract procedures are presented based on those emotions.

[0151] The contracts department can analyze a user's past contract history during the contracting process and propose the most suitable procedure. For example, the contracts department can prioritize providing relevant contract procedures based on the user's past contracts. It can also prioritize suggesting specific contract procedures based on the user's past contract history. Furthermore, the contracts department can analyze the user's past contract history and propose the most efficient contract procedure. This allows for the proposal of the optimal procedure by analyzing the user's past contract history. Some or all of the above processes in the contracts department may be performed using AI, for example, or not. For example, the contracts department can use AI to analyze a user's past contract history and propose the optimal procedure based on the analysis results.

[0152] The contracts department can customize contract procedures based on the user's current project status. For example, if a user starts a new project, the contracts department will prioritize providing the relevant contract procedures. It can also prioritize providing the necessary contract procedures if a user is renewing an ongoing project. Furthermore, if a user manages multiple projects, the contracts department can customize and provide different contract procedures for each project. This allows users to efficiently perform the necessary procedures by customizing them based on their current project status. Some or all of the above processing in the contracts department may be performed using AI, for example, or not. For example, the contracts department can use AI to analyze the user's current project status and customize procedures based on that status.

[0153] The contracts department can estimate the user's emotions and prioritize contract procedures based on those emotions. For example, if the user is stressed, the contracts department can prioritize important contract procedures. It can also provide detailed contract procedures if the user is relaxed. Furthermore, if the user is in a hurry, the contracts department can quickly provide necessary contract procedures. This allows users to prioritize important contract procedures by prioritizing them based on 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 processing in the contracts department may be performed using AI or not. For example, the contracts department can use AI to estimate the user's emotions and prioritize contract procedures based on those emotions.

[0154] The contracts department can prioritize relevant procedures during the contract process, taking into account the user's geographical location. For example, if a user is in a specific area, the contracts department can prioritize contract procedures related to that area. Furthermore, if a user is on the move, the contracts department can provide the most relevant contract procedures based on their current location. Additionally, if a user is working on a project in a specific region, the contracts department can prioritize contract procedures related to that region. This allows users to efficiently complete contract procedures by prioritizing relevant procedures based on their geographical location. Some or all of the above processing in the contracts department may be performed using AI, or not. For example, the contracts department can use AI to analyze the user's geographical location and prioritize relevant procedures based on that information.

[0155] The contracts department can analyze a user's social media activity during the contracting process and propose relevant procedures. For example, the contracts department can provide relevant contract procedures based on information shared by the user on social media. The contracts department can also prioritize providing contract procedures related to a specific project based on the user's social media activity. Furthermore, the contracts department can provide relevant contract procedures based on topics the user has shown interest in on social media. This allows for the proposal of relevant procedures by analyzing the user's social media activity. Some or all of the above processes in the contracts department may be performed using AI, for example, or not. For example, the contracts department can use AI to analyze a user's social media activity and propose relevant procedures based on the analysis results.

[0156] The notification unit can estimate the user's emotions and adjust the presentation of reminder notifications based on those emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible reminder notification. If the user is relaxed, the notification unit can also provide a reminder notification with more detailed information. Furthermore, if the user is in a hurry, the notification unit can provide a concise reminder notification. This allows the user to comfortably review reminder notifications by adjusting their presentation based on their 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 notification unit may be performed using AI or not. For example, the notification unit can use AI to estimate the user's emotions and adjust the presentation of reminder notifications based on those emotions.

[0157] The notification unit can analyze the user's past notification history to suggest the optimal notification method when sending reminder notifications. For example, the notification unit can prioritize providing relevant reminder notifications based on notifications the user has received in the past. The notification unit can also prioritize suggesting specific notification methods based on the user's past notification history. Furthermore, the notification unit can analyze the user's past notification history to suggest the most efficient notification method. In this way, the notification unit can suggest the optimal notification method by analyzing the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to analyze the user's past notification history and suggest the optimal notification method based on the analysis results.

[0158] The notification unit can customize the content of reminder notifications based on the user's current schedule. For example, if a user starts a new project, the notification unit will prioritize providing relevant reminder notifications. It can also prioritize providing necessary reminder notifications if a user updates an ongoing project. Furthermore, if a user manages multiple projects, the notification unit can customize and provide different reminder notifications for each project. This allows users to efficiently receive the notifications they need by customizing the content based on their current schedule. Some or all of the above processing in the notification unit may be performed using AI, for example, or not. For example, the notification unit can use AI to analyze the user's current schedule and customize the notification content based on that schedule.

[0159] The notification unit can estimate the user's emotions and prioritize notifications based on those emotions. For example, if the user is stressed, the notification unit will prioritize important notifications. It can also provide detailed notifications if the user is relaxed. Furthermore, if the user is in a hurry, the notification unit can provide urgent notifications. This ensures that users receive important notifications preferentially by prioritizing them based on 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 processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to estimate the user's emotions and prioritize notifications based on those emotions.

[0160] The notification unit can prioritize highly relevant notifications by considering the user's geographical location when sending reminder notifications. For example, if the user is in a specific area, the notification unit can prioritize notifications related to that area. Furthermore, if the user is on the move, the notification unit can provide optimal notifications based on their current location. Additionally, if the user is working on a project in a specific region, the notification unit can prioritize notifications related to that region. This allows users to efficiently receive the notifications they need by prioritizing highly relevant notifications based on their geographical location. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can use AI to analyze the user's geographical location and prioritize highly relevant notifications based on that information.

[0161] The notification unit can analyze the user's social media activity and suggest relevant notifications when sending reminder notifications. For example, the notification unit can provide relevant notifications based on information the user has shared on social media. It can also prioritize notifications related to specific projects based on the user's social media activity. Furthermore, the notification unit can provide relevant notifications based on topics the user has shown interest in on social media. In this way, relevant notifications can be suggested by analyzing the user's social media activity. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use AI to analyze the user's social media activity and suggest relevant notifications based on the results of that analysis.

[0162] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0163] The reception desk can estimate the user's emotions and adjust the input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of the conditions. In this way, by adjusting the input interface based on the user's emotions, the user can comfortably input the conditions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0164] The information gathering unit can estimate the user's emotions and adjust the timing of information gathering based on those emotions. For example, if the user is stressed, the frequency of information gathering will be reduced, and only important information will be collected. If the user is relaxed, detailed information can be collected and provided to the user. Furthermore, if the user is in a hurry, information can be collected quickly, and only the necessary information can be provided. In this way, by adjusting the timing of information gathering based on the user's emotions, the user can obtain the information they need at the right time. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0165] The search engine can estimate the user's emotions and adjust how search results are displayed based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes more detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how search results are displayed based on the user's emotions, the system allows users to browse search results comfortably. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0166] The generation unit can estimate the user's emotions and adjust the way the interior design is expressed based on those emotions. For example, if the user is relaxed, the generation AI will generate a relaxed design. If the user is in a hurry, the generation AI can generate a simple and efficient design. Furthermore, if the user is excited, the generation AI can generate a visually stimulating design. By adjusting the way the interior design is expressed based on the user's emotions, the user can comfortably view the interior design. Emotion estimation is achieved using an emotion engine or generation AI, among other methods.

[0167] The output unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read report. If the user is relaxed, it can provide a report with more detailed information. Furthermore, if the user is in a hurry, it can provide a concise report. By adjusting the report's presentation based on the user's emotions, the user can comfortably review the report. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0168] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, it can automatically display conditions that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest conditions that the user will use during specific time periods based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method.

[0169] The data collection unit can analyze the user's past search history to select the optimal data collection method. For example, it can prioritize collecting relevant information based on information the user has previously searched for. It can also prioritize collecting information from specific sources based on the user's past search history. Furthermore, it can analyze the user's past search history to select the most efficient data collection method. In this way, the optimal data collection method can be selected by analyzing the user's past search history.

[0170] The search function can analyze a user's past search history and apply the optimal search algorithm during a search. For example, it can prioritize displaying relevant search results based on information the user has previously searched for. It can also prioritize displaying specific information sources based on the user's past search history. Furthermore, it can analyze the user's past search history and apply the most efficient search algorithm. In this way, by analyzing the user's past search history, the optimal search algorithm can be applied.

[0171] The design generation unit can analyze the user's past design history to propose the optimal design when generating interior designs. For example, the AI ​​can suggest related designs based on designs the user has previously selected. It can also prioritize suggesting specific design styles based on the user's past design history. Furthermore, the AI ​​can analyze the user's past design history and propose the most efficient design. In this way, by analyzing the user's past design history, the AI ​​can propose the optimal design.

[0172] The output unit can analyze the user's past report history and suggest the optimal output method when generating reports. For example, it can prioritize outputting related reports based on reports the user has previously generated. It can also prioritize suggesting specific output methods based on the user's past report history. Furthermore, it can analyze the user's past report history and suggest the most efficient output method. In this way, by analyzing the user's past report history, it can suggest the optimal output method.

[0173] The following briefly describes the processing flow for example form 2.

[0174] Step 1: The reception desk enters the user's criteria. These criteria may include, for example, the office's location, size, and budget. The reception desk can also save the user's entered criteria to a database and issue instructions to collect the necessary information. Step 2: The collection unit gathers information from the internet and databases. The collection unit collects information on office properties from real estate information sites and databases on the internet, and gathers information to search for the most suitable office properties based on the user's criteria. Step 3: The search unit searches for the most suitable office property based on the information collected by the data collection unit. The search unit analyzes the collected information, searches for office properties that match the user's criteria, and presents the search results to the user. Step 4: The reservation department makes viewing reservations for office properties found by the search department. The reservation department makes viewing reservations for the office properties selected by the user online and manages the viewing reservation schedule. Step 5: The generation unit automatically generates interior designs after the office property reserved by the reservation unit has been determined. The generation unit uses generation AI to automatically generate office interior designs and outputs the generated interior designs as a report. Step 6: The output unit outputs the interior design report generated by the generation unit. The output unit outputs a report that includes details of the office property, a list of office furniture, and a quote from the construction company, and provides it to the user. Step 7: The simulation unit provides a cost simulation based on the report output by the output unit. The simulation unit works in conjunction with the e-commerce site to provide the necessary cost simulation and presents the cost simulation results to the user. Step 8: The Contracts Department handles the entire process, from contracting for office properties to contacting construction and moving companies and purchasing furniture, based on the cost simulations provided by the Simulation Department. The Contracts Department handles the office property contract procedures online and also handles the contact with construction and moving companies and the furniture purchase procedures in one go. Step 9: The Notifications Department creates schedules and sends reminder notices based on contracts and purchases made by the Contracts Department. The Notifications Department sends reminder notices for things like interior work, furniture arrival, and office occupancy start, and manages the reminder notice schedule.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the reception unit, collection unit, search unit, reservation unit, generation unit, output unit, simulation unit, contract unit, and notification 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 accepts user condition input. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information from the network and databases. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for the most suitable office property based on the collected information. The reservation unit is implemented by the control unit 46A of the smart device 14 and makes a reservation to view the searched office property. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates interior designs. The output unit is implemented by the control unit 46A of the smart device 14 and outputs a report of the generated interior designs. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a cost simulation. The contracting unit is implemented by the control unit 46A of the smart device 14 and handles the contracting of office properties, contact with construction companies and moving companies, and the purchase of furniture all in one place. The notification unit is implemented by the control unit 46A of the smart device 14 and creates schedules and sends reminder notifications. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0179] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] Each of the multiple elements described above, including the reception unit, collection unit, search unit, reservation unit, generation unit, output unit, simulation unit, contract unit, and notification unit, is implemented, for example, by 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 accepts user condition input. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information from the network and database. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for the optimal office property based on the collected information. The reservation unit is implemented by the control unit 46A of the smart glasses 214 and makes a reservation to view the searched office property. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates interior designs. The output unit is implemented by the control unit 46A of the smart glasses 214 and outputs a report of the generated interior designs. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a cost simulation. The contracting function is implemented by the control unit 46A of the smart glasses 214, and handles the contracting of office properties, contact with construction companies and moving companies, and the purchase of furniture all in one place. The notification function is also implemented by the control unit 46A of the smart glasses 214, and creates schedules and sends reminder notifications. The correspondence between each function and the device or control unit is not limited to the example described above, and various changes are possible.

[0195] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] Each of the multiple elements described above, including the reception unit, collection unit, search unit, reservation unit, generation unit, output unit, simulation unit, contract unit, and notification unit, is implemented by 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 accepts user condition input. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information from the network and database. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for the most suitable office property based on the collected information. The reservation unit is implemented by the control unit 46A of the headset terminal 314 and makes a reservation to view the searched office property. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates interior designs. The output unit is implemented by the control unit 46A of the headset terminal 314 and outputs a report of the generated interior designs. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a cost simulation. The contracting function is implemented by the control unit 46A of the headset terminal 314, and handles the contracting of office properties, contact with construction companies and moving companies, and the purchase of furniture all in one place. The notification function is implemented by the control unit 46A of the headset terminal 314, and creates schedules and sends reminder notifications. The correspondence between each function and the devices and control units is not limited to the example described above, and various changes are possible.

[0211] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.).

[0224] 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.

[0225] 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.

[0226] 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.

[0227] Each of the multiple elements described above, including the reception unit, collection unit, search unit, reservation unit, generation unit, output unit, simulation unit, contract unit, and notification 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 accepts user condition input. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects information from the network and database. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for the most suitable office property based on the collected information. The reservation unit is implemented by the control unit 46A of the robot 414 and makes a reservation to view the searched office property. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates interior designs. The output unit is implemented by the control unit 46A of the robot 414 and outputs a report of the generated interior designs. The simulation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a cost simulation. The contracting unit is implemented by the control unit 46A of the robot 414 and handles the contracting of office properties, contact with construction companies and moving companies, and the purchase of furniture all in one place. The notification unit is also implemented by the control unit 46A of the robot 414 and creates schedules and sends reminder notifications. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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."

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] (Note 1) A reception area where users enter their conditions, A collection unit that collects information based on the conditions entered by the reception unit, A search unit searches for the most suitable office property based on the information collected by the aforementioned collection unit, A reservation unit that makes reservations for viewing office properties found by the search unit, After the office property reserved by the aforementioned reservation unit is determined, a generation unit automatically generates an interior design, An output unit that outputs a report of the interior design generated by the generation unit, A simulation unit provides a cost simulation based on the report output by the output unit, Based on the cost simulations provided by the aforementioned simulation department, the contract department handles the entire process, including contracting for office properties, contacting construction companies and moving companies, and purchasing furniture. The system includes a notification unit that creates a schedule and sends reminder notifications based on contracts and purchases made by the aforementioned contract unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather information from the internet and databases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is The AI ​​automatically generates interior designs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The output unit is, Output a report that includes details of the office property, a list of office furniture, and quotes from construction companies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, We provide cost simulations in conjunction with e-commerce sites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned contracts department, We handle everything from signing office lease agreements and contacting construction and moving companies to purchasing chairs and tables. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned notification unit, Send reminder notifications for interior construction, furniture arrival, and office occupancy. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering conditions, the input fields are customized based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned reception unit is When users enter conditions, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users enter criteria, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When gathering information, the system analyzes the user's past search history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is When collecting information, filter the data to be collected based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned search unit, At the time of search, analyze the user's past search history and apply an optimal search algorithm The system according to appended note 1, characterized by the above (Appended note 23) The search unit At the time of search, filter the search results based on the user's current project status The system according to appended note 1, characterized by the above (Appended note 24) The search unit Estimate the user's emotion and determine the priority of search results based on the estimated user emotion The system according to appended note 1, characterized by the above (Appended note 25) The search unit At the time of search, preferentially display highly relevant search results considering the user's geographical location information The system according to appended note 1, characterized by the above (Appended note 26) The search unit At the time of search, analyze the user's social media activities and display relevant search results The system according to appended note 1, characterized by the above (Appended note 27) The reservation unit Estimate the user's emotion and adjust the timing of the visit reservation based on the estimated user emotion The system according to appended note 1, characterized by the above (Appended note 28) The reservation unit At the time of visit reservation, analyze the user's past reservation history and propose an optimal reservation method The system according to appended note 1, characterized by the above (Appended note 29) The reservation unit At the time of visit reservation, customize the reservation date and time based on the user's current schedule The system according to appended note 1, characterized by the above (Appended note 30) The reservation unit The system estimates the user's emotions and determines reservation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned reservation section is, When making a reservation for a tour, the system prioritizes reservations based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reservation section is, When a user makes a reservation for a tour, the system analyzes their social media activity and suggests relevant reservations. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is The system estimates the user's emotions and adjusts the interior design's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is When generating interior designs, we analyze the user's past design history to propose the optimal design. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is When generating interior designs, the design is customized based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 36) The generating unit is We estimate user emotions and determine design priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The generating unit is When generating interior designs, the system prioritizes generating highly relevant designs by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The generating unit is When generating the interior design, analyze the user's social media activities and propose relevant designs The system according to appended claim 1, characterized in that (Appended claim 39) The output unit Estimate the user's emotion and adjust the expression method of the report based on the estimated user emotion The system according to appended claim 1, characterized in that (Appended claim 40) The output unit When outputting the report, analyze the user's past report history and propose an optimal output method The system according to appended claim 1, characterized in that (Appended claim 41) The output unit When outputting the report, customize the report content based on the user's current project status The system according to appended claim 1, characterized in that (Appended claim 42) The output unit Estimate the user's emotion and determine the priority of the report based on the estimated user emotion The system according to appended claim 1, characterized in that (Appended claim 43) The output unit When outputting the report, preferentially output relevant reports considering the user's geographical location information The system according to appended claim 1, characterized in that (Appended claim 44) The output unit When outputting the report, analyze the user's social media activities and propose relevant reports The system according to appended claim 1, characterized in that<00009​​​​​​​​(Note 46) The aforementioned simulation unit, During cost simulations, we analyze the user's past simulation history to propose the optimal simulation method. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned simulation unit, During cost simulation, the simulation content is customized based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned simulation unit, During cost simulations, the system prioritizes simulations that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 50) The aforementioned simulation unit, During cost simulations, we analyze users' social media activity and propose relevant simulations. The system described in Appendix 1, characterized by the features described herein. (Note 51) The aforementioned contracts department, The system estimates the user's emotions and adjusts the wording of contract procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned contracts department, During the contract process, we analyze the user's past contract history and propose the most suitable procedure. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned contracts department, During the contract process, the procedure will be customized based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned contracts department, The system estimates the user's emotions and determines the priority of contract procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned contracts department, During the contract process, the system prioritizes procedures that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 56) The aforementioned contracts department, During the contract process, we analyze the user's social media activity and propose relevant procedures. The system described in Appendix 1, characterized by the features described herein. (Note 57) The aforementioned notification unit, The system estimates the user's emotions and adjusts the way reminder notifications are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 58) The aforementioned notification unit, When sending reminder notifications, the system analyzes the user's past notification history to suggest the most suitable notification method. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned notification unit, When sending reminder notifications, customize the notification content based on the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned notification unit, When sending reminder notifications, the system prioritizes highly relevant notifications by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 62) The aforementioned notification unit, When sending reminder notifications, the system analyzes the user's social media activity and suggests relevant notifications. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0247] 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. A reception area where users enter their conditions, A collection unit that collects information based on the conditions entered by the reception unit, A search unit searches for the most suitable office property based on the information collected by the aforementioned collection unit, A reservation unit that makes reservations for viewing office properties found by the search unit, After the office property reserved by the aforementioned reservation unit is determined, a generation unit automatically generates an interior design, An output unit that outputs a report of the interior design generated by the generation unit, A simulation unit provides a cost simulation based on the report output by the output unit, Based on the cost simulations provided by the aforementioned simulation department, the contract department handles the entire process, including contracting for office properties, contacting construction companies and moving companies, and purchasing furniture. The system includes a notification unit that creates a schedule and sends reminder notifications based on contracts and purchases made by the aforementioned contract unit. A system characterized by the following features.

2. The aforementioned collection unit is Gather information from the internet and databases. The system according to feature 1.

3. The generating unit is The AI ​​automatically generates interior designs. The system according to feature 1.

4. The output unit is, Output a report that includes details of the office property, a list of office furniture, and quotes from construction companies. The system according to feature 1.

5. The aforementioned simulation unit, We provide cost simulations in conjunction with e-commerce sites. The system according to feature 1.

6. The aforementioned contracts department, We handle everything from signing office lease agreements and contacting construction and moving companies to purchasing chairs and tables. The system according to feature 1.

7. The aforementioned notification unit, Send reminder notifications for interior construction, furniture arrival, and office occupancy. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface based on those estimated emotions. The system according to feature 1.

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