system

Abstract

The system according to this embodiment aims to improve the efficiency of office use and enhance customer satisfaction. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis 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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, data analysis and proposals for improving the efficiency of office use and customer satisfaction have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of office use and customer satisfaction.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes a proposal based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can improve the efficiency of office use and enhance customer satisfaction. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 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 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The Workplace Insights AI system according to an embodiment of the present invention is a system that utilizes data analysis to provide workplace managers and coworking space operators with efficient office use and improved customer satisfaction. This system uses an AI agent to collect and analyze data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing, thereby understanding office usage patterns and customer behavior. Based on this, the AI ​​makes suggestions for efficient office use and improved customer satisfaction. For example, for a company's general affairs department, the AI ​​agent comprehensively visualizes the use of headquarters, teleworking, and coworking spaces, and uses heat map analysis to identify surpluses or shortages of meeting rooms and phone booths. For coworking space operators, it provides advice on investment plans and operational policies, such as congestion reduction and vacancy reduction, based on real-time analysis. This system allows a company's general affairs department to understand usage patterns and optimize costs and maximize employee satisfaction. Furthermore, coworking space operators can conduct precise customer analysis and optimize operations. As a result, the Workplace Insights AI system can achieve efficient office use and improved customer satisfaction.

[0029] The Workplace Insights AI system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects data such as CSV data of entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. For example, the data collection unit collects CSV data of entry and exit, obtaining the date and time of entry and exit and identification information of individuals. The data collection unit can also set the method for measuring congestion and the display format of the heat map in order to create a heat map of congestion levels. Furthermore, the data collection unit can perform cost analysis of headquarters and facility operations and collect detailed data such as operating costs and equipment costs. For example, the data collection unit collects CSV data of entry and exit in real time and generates a heat map of congestion levels. The data collection unit can also perform natural language processing of customer reviews and conduct sentiment analysis. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. The analysis department analyzes office usage patterns, for example, to understand usage frequency and time of day. It can also analyze customer behavior to identify behavioral history and patterns. Furthermore, based on the collected data, the analysis department can make suggestions for improving office efficiency and customer satisfaction. For example, the analysis department uses collected data to analyze office usage patterns in detail and understand customer behavior. The proposal department makes suggestions based on the analysis results obtained by the analysis department. For example, the proposal department might propose to a company's general affairs department a comprehensive visualization of usage in the head office, remote work, and co-working spaces, and use heatmap analysis to identify surpluses or shortages of meeting rooms and phone booths. The proposal department might also make suggestions to a company's general affairs department for improving office efficiency and optimizing costs. Additionally, the proposal department can advise co-working space operators on investment plans and operational policies for reducing congestion and vacancies. For example, the proposal department might make specific suggestions to co-working space operators for reducing congestion and vacancies.As a result, the Workplace Insights AI system according to this embodiment can achieve efficient office use and improved customer satisfaction.

[0030] The data collection unit collects data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. Specifically, when collecting CSV data on entry and exit, the unit obtains the date and time of entry and exit for each employee and visitor, as well as identification information of the person. This allows for an accurate understanding of office usage and the frequency of entry and exit. In addition, to create heat maps of congestion levels, data is collected from sensors and cameras installed in various areas of the office to measure congestion levels in real time. This allows for a visual display of congestion levels in specific time periods or areas. Furthermore, the data collection unit collects detailed data such as operating costs and equipment costs in order to conduct cost analysis of headquarters and facility operations. For example, it collects operating costs such as electricity bills, water bills, and cleaning costs, as well as equipment purchase costs and maintenance costs, enabling a comprehensive cost analysis. Finally, to perform sentiment analysis using natural language processing of customer reviews, the unit collects reviews from online review sites and social media and performs sentiment analysis using natural language processing technology. This allows for understanding customer satisfaction and dissatisfaction, which can then be used to improve services. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection department can collect data efficiently and effectively, improving the overall performance of the system.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department analyzes the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. Specifically, it analyzes CSV data on entry and exit to understand the frequency and time of office use by employees and visitors. This allows for a detailed understanding of office usage, which can be used to improve efficient office management. It also analyzes heat maps of congestion to understand the degree of congestion at specific times and in specific areas. This allows for the development of measures to alleviate congestion. Furthermore, it analyzes cost analysis data to understand the details of costs incurred in operating the headquarters and facilities. This allows for the development of concrete measures to reduce costs. In addition, it performs natural language analysis on customer reviews to conduct sentiment analysis. This allows for an understanding of customer satisfaction and dissatisfaction, which can be used to improve services. Based on this data, the Analysis Department can make proposals to improve the efficiency of office use and enhance customer satisfaction. For example, by analyzing office usage patterns in detail and understanding the frequency and time of use, it can propose optimizing the office layout and equipment placement. Furthermore, by analyzing customer behavior and identifying behavioral history and patterns, it becomes possible to propose services tailored to customer needs. In addition, the analytics department can perform long-term trend analysis and predictions based on the collected data. This allows for the development of strategies to respond to future fluctuations in office usage and changes in customer needs.

[0032] The Proposal Department makes proposals based on the analysis results obtained by the Analysis Department. For example, the Proposal Department might propose to a company's general affairs department a comprehensive visualization of the use of the head office, teleworking, and coworking spaces, and use heatmap analysis to identify surpluses or shortages of meeting rooms and phone booths. Specifically, by visualizing office usage and understanding the frequency and time of use of meeting rooms and phone booths, they can identify surpluses or shortages and propose optimal placement and operation methods. They can also comprehensively visualize the usage of teleworking and coworking spaces and propose efficient work styles. Furthermore, the Proposal Department can advise coworking space operators on investment plans and operational policies such as congestion reduction and vacancy reduction. For example, based on a heatmap of congestion, they can understand the level of congestion at specific times or in specific areas and propose concrete measures to alleviate congestion. They can also propose operational policies to reduce vacancies and support efficient space management. Through these proposals, the Proposal Department can help companies and coworking space operators achieve efficient office use and improved customer satisfaction. Furthermore, the Proposal Department can continuously monitor the effectiveness of the proposals and revise them as needed. This allows the proposal department to always provide optimal proposals based on the latest information and to respond flexibly to the needs of companies and coworking space operators.

[0033] The data collection unit can collect data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. For example, the data collection unit can collect CSV data on entry and exit, obtaining the date and time of entry and exit, as well as identification information of individuals. The data collection unit can also set the method for measuring congestion and the display format of the heat map to create a heat map of congestion levels. Furthermore, the data collection unit can perform cost analysis of headquarters and facility operations, collecting detailed data such as operating costs and equipment costs. For example, the data collection unit can collect CSV data on entry and exit in real time and generate a heat map of congestion levels. The data collection unit can also perform sentiment analysis by performing natural language processing on customer reviews. By collecting diverse data in this way, it is possible to understand office usage patterns and customer behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input CSV data on entry and exit into a generating AI and have the generating AI perform the data analysis.

[0034] The analysis unit can analyze the collected data to understand office usage patterns and customer behavior. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. For example, the analysis unit can analyze office usage patterns to understand usage frequency and time of day. The analysis unit can also analyze customer behavior to identify behavioral history and patterns. For example, the analysis unit can use the collected data to analyze office usage patterns in detail to understand customer behavior. In this way, data analysis can help understand office usage patterns and customer behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0035] The proposal department can, based on the analysis results, provide a comprehensive visualization of office usage, including headquarters, teleworking, and co-working spaces, for the general affairs department of a company, and propose solutions that identify surpluses or shortages of meeting rooms and phone booths using heatmap analysis. For example, the proposal department can make proposals to the general affairs department of a company to improve the efficiency of office use and optimize costs. For example, the proposal department can use heatmap analysis to identify surpluses or shortages of meeting rooms and phone booths. For example, the proposal department can make specific proposals to the general affairs department of a company to improve the efficiency of office use and optimize costs. This allows the general affairs department of a company to visualize usage and identify surpluses or shortages of meeting rooms and phone booths. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate the proposal content.

[0036] The proposal department can make proposals to coworking space operators, advising them on investment plans and operational policies such as congestion reduction and vacancy reduction, based on the analysis results. For example, the proposal department can make specific proposals to coworking space operators to reduce congestion and vacancy. For example, the proposal department can propose the introduction of a reservation system or adjustment of usage hours to reduce congestion. In addition, the proposal department can propose usage promotion campaigns or revisions to pricing to reduce vacancy. For example, the proposal department can make specific proposals to coworking space operators to reduce congestion and vacancy. This allows the proposal department to make proposals to coworking space operators to reduce congestion and vacancy. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate the proposal content.

[0037] The data collection unit can focus on specific time periods or days of the week when collecting CSV data on entry and exit. For example, the unit can focus on collecting data during peak hours in the morning and evening on weekdays. It can also focus on collecting data on Saturdays and Sundays to understand weekend usage. Furthermore, the unit can collect data on days when specific events are held and analyze the impact of those events. This allows for a detailed understanding of usage patterns by focusing data collection on specific time periods or days of the week. 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 input data for specific time periods or days of the week into a generating AI and have the generating AI perform the data analysis.

[0038] The data collection unit can collect detailed data by focusing on specific areas or rooms when collecting heatmaps of congestion. For example, the data collection unit can collect detailed congestion data for each meeting room to understand the usage of meeting rooms. It can also collect congestion data for specific areas to understand the usage of shared spaces. Furthermore, it can collect detailed congestion data for each phone booth to understand the usage of phone booths. This makes it possible to understand usage in detail by collecting detailed data by focusing on specific areas or rooms. 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 input data for specific areas or rooms into a generating AI and have the generating AI perform data analysis.

[0039] The data collection unit can collect detailed data by focusing on specific cost items when collecting cost analysis data related to headquarters and facility operations. For example, the data collection unit can collect detailed data on utility costs and analyze energy efficiency. It can also collect detailed data on cleaning costs and analyze cleaning efficiency. Furthermore, it can collect detailed data on maintenance costs and analyze maintenance efficiency. This enables efficient cost management by collecting detailed data by focusing on specific cost items. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on specific cost items into a generating AI and have the generating AI perform the data analysis.

[0040] The data collection unit can collect detailed data by focusing on specific keywords or phrases when collecting sentiment analysis of customer reviews using natural language processing. For example, the data collection unit can focus on the keyword "satisfied" to collect detailed positive reviews. It can also focus on the keyword "dissatisfied" to collect detailed negative reviews. Furthermore, it can focus on the keyword "improvement" to collect detailed reviews regarding areas for improvement. This allows for a detailed understanding of customer sentiment by collecting detailed data by focusing on specific keywords or phrases. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on specific keywords or phrases into a generating AI and have the generating AI perform the data analysis.

[0041] The analysis unit can focus its analysis on specific time periods or days of the week when analyzing the collected data. For example, it can focus its analysis on data during peak hours in the morning and evening on weekdays. It can also focus its analysis on Saturday and Sunday data to understand weekend usage patterns. Furthermore, it can analyze data on days when specific events are held to analyze the impact of those events. This allows for a detailed understanding of usage patterns by focusing the analysis on specific time periods or days of the week. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For instance, the analysis unit can input data for specific time periods or days of the week into a generating AI and have the generating AI perform the data analysis.

[0042] The analysis unit can perform detailed analyses by focusing on specific areas or rooms when analyzing office usage patterns. For example, the analysis unit can perform detailed analyses of meeting room usage patterns. It can also perform detailed analyses of shared space usage patterns. Furthermore, it can perform detailed analyses of phone booth usage patterns. This allows for a detailed understanding of usage patterns by focusing on specific areas or rooms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from specific areas or rooms into a generating AI and have the generating AI perform the data analysis.

[0043] The analysis unit can perform detailed analyses by focusing on specific behavioral patterns when analyzing customer behavior. For example, the analysis unit can perform detailed analyses of customer entry and exit patterns. It can also perform detailed analyses of customer reactions to congestion levels. Furthermore, the analysis unit can perform detailed analyses of customer reviews. In this way, by focusing on specific behavioral patterns and performing detailed analyses, customer behavior can be understood in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on specific behavioral patterns into a generating AI and have the generating AI perform the data analysis.

[0044] The analysis unit can perform detailed analyses by focusing on specific data sources when analyzing collected data. For example, the analysis unit can perform detailed analyses of CSV data on entry and exit. It can also perform detailed analyses of congestion heatmaps. Furthermore, the analysis unit can perform detailed cost analyses related to headquarters and facility operations. This allows for a detailed understanding of the data by focusing on specific data sources and performing detailed analyses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from a specific data source into a generating AI and have the generating AI perform the data analysis.

[0045] The proposal department can focus its proposals on specific time slots or days of the week when making suggestions to the general affairs departments of companies. For example, the proposal department can focus its suggestions on peak hours in the morning and evening on weekdays. It can also make suggestions for Saturdays and Sundays to understand weekend usage. Furthermore, the proposal department can make suggestions for days when specific events are held. This allows for a detailed understanding of usage patterns by focusing suggestions on specific time slots or days of the week. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific time slots or days of the week into a generating AI and have the generating AI generate the proposal content.

[0046] The proposal department can make detailed proposals to coworking space operators by focusing on specific areas or rooms. For example, the proposal department can propose adding or removing meeting rooms based on their usage. It can also propose optimizing shared spaces based on their usage. Furthermore, it can propose adding or removing phone booths based on their usage. This allows for a detailed understanding of usage patterns by focusing on specific areas or rooms and making detailed proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific areas or rooms into a generating AI and have the generating AI generate proposals.

[0047] The proposal department can focus on specific cost items and provide detailed proposals when making suggestions to the general affairs departments of companies. For example, the proposal department can focus on reducing utility costs. It can also focus on reducing cleaning costs. Furthermore, it can focus on reducing maintenance costs. This allows for efficient cost management by focusing on specific cost items and providing detailed proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific cost items into a generating AI and have the generating AI generate the proposal content.

[0048] The proposal department can focus on specific customer segments and provide detailed proposals when making suggestions to coworking space operators. For example, the proposal department can focus on freelance users and propose flexible plans. It can also focus on startup companies and propose cost-effective plans. Furthermore, it can focus on satellite office users of large corporations and propose high-performance equipment. In this way, by focusing on specific customer segments and providing detailed proposals, it becomes possible to make proposals that meet customer needs. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on a specific customer segment into a generating AI and have the generating AI generate proposal content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can perform detailed analyses by focusing on specific data sources when analyzing collected data. For example, it can perform detailed analyses of CSV data on entry and exit. It can also perform detailed analyses of congestion heatmaps. Furthermore, it can perform detailed cost analyses related to headquarters and facility operations. This allows for a detailed understanding of the data by focusing on specific data sources and performing detailed analyses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data from a specific data source into a generating AI and have the generating AI perform the data analysis.

[0051] The proposal department can focus on specific cost items when making proposals to the general affairs departments of companies, providing detailed suggestions. For example, it can focus on reducing utility costs, cleaning costs, and maintenance costs. By focusing on specific cost items and providing detailed suggestions, efficient cost management becomes possible. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific cost items into a generating AI and have the generating AI generate the proposal content.

[0052] The data collection unit can collect detailed data by focusing on specific keywords or phrases when collecting sentiment analysis of customer reviews using natural language processing. For example, it can focus on the keyword "satisfied" to collect detailed positive reviews. It can also focus on the keyword "dissatisfied" to collect detailed negative reviews. Furthermore, it can focus on the keyword "improvement" to collect detailed reviews regarding areas for improvement. In this way, by collecting detailed data by focusing on specific keywords or phrases, it is possible to gain a detailed understanding of customer sentiment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data of specific keywords or phrases into a generating AI and have the generating AI perform the data analysis.

[0053] The proposal department can focus on specific customer segments and provide detailed proposals when making suggestions to coworking space operators. For example, it can focus on freelance users and propose flexible plans. It can also focus on startup companies and propose cost-effective plans. Furthermore, it can focus on satellite office users of large corporations and propose high-performance equipment. By focusing on specific customer segments and providing detailed proposals, it becomes possible to make proposals that meet customer needs. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on a specific customer segment into a generating AI and have the generating AI generate proposal content.

[0054] The proposal department can make detailed proposals to coworking space operators by focusing on specific areas or rooms. For example, it can propose adding or removing meeting rooms based on their usage. It can also propose optimizing shared spaces based on their usage. Furthermore, it can propose adding or removing phone booths based on their usage. This allows for a detailed understanding of usage patterns by focusing on specific areas or rooms and making detailed proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific areas or rooms into a generating AI and have the generating AI generate the proposal content.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The data collection unit collects data. The data collection unit collects data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. The data collection unit collects CSV data on entry and exit, obtaining the date and time of entry and exit, as well as identification information of individuals. It can also set the method for measuring congestion and the display format of the heat map in order to create a heat map of congestion levels. Furthermore, it can perform cost analysis of headquarters and facility operations, collecting detailed data such as operating costs and equipment costs. The data collection unit collects CSV data on entry and exit in real time and generates a heat map of congestion levels. Furthermore, it can perform sentiment analysis by performing natural language processing on customer reviews. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. The analysis unit analyzes office usage patterns to understand usage frequency and time of day. It can also analyze customer behavior to identify behavioral history and patterns. Furthermore, based on the collected data, it can make suggestions for improving the efficiency of office use and enhancing customer satisfaction. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department proposes to the general affairs departments of companies to comprehensively visualize the usage of headquarters, teleworking, co-working spaces, etc., and to point out the surplus or shortage of meeting rooms and phone booths using heat map analysis. Furthermore, it makes proposals for improving the efficiency of office use and optimizing costs. The proposal department can also make proposals to co-working space operators, advising them on investment plans and operational policies such as reducing congestion and vacancies.

[0057] (Example of form 2) The Workplace Insights AI system according to an embodiment of the present invention is a system that utilizes data analysis to provide workplace managers and coworking space operators with efficient office use and improved customer satisfaction. This system uses an AI agent to collect and analyze data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing, thereby understanding office usage patterns and customer behavior. Based on this, the AI ​​makes suggestions for efficient office use and improved customer satisfaction. For example, for a company's general affairs department, the AI ​​agent comprehensively visualizes the use of headquarters, teleworking, and coworking spaces, and uses heat map analysis to identify surpluses or shortages of meeting rooms and phone booths. For coworking space operators, it provides advice on investment plans and operational policies, such as congestion reduction and vacancy reduction, based on real-time analysis. This system allows a company's general affairs department to understand usage patterns and optimize costs and maximize employee satisfaction. Furthermore, coworking space operators can conduct precise customer analysis and optimize operations. As a result, the Workplace Insights AI system can achieve efficient office use and improved customer satisfaction.

[0058] The Workplace Insights AI system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects data such as CSV data of entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. For example, the data collection unit collects CSV data of entry and exit, obtaining the date and time of entry and exit and identification information of individuals. The data collection unit can also set the method for measuring congestion and the display format of the heat map in order to create a heat map of congestion levels. Furthermore, the data collection unit can perform cost analysis of headquarters and facility operations and collect detailed data such as operating costs and equipment costs. For example, the data collection unit collects CSV data of entry and exit in real time and generates a heat map of congestion levels. The data collection unit can also perform natural language processing of customer reviews and conduct sentiment analysis. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. The analysis department analyzes office usage patterns, for example, to understand usage frequency and time of day. It can also analyze customer behavior to identify behavioral history and patterns. Furthermore, based on the collected data, the analysis department can make suggestions for improving office efficiency and customer satisfaction. For example, the analysis department uses collected data to analyze office usage patterns in detail and understand customer behavior. The proposal department makes suggestions based on the analysis results obtained by the analysis department. For example, the proposal department might propose to a company's general affairs department a comprehensive visualization of usage in the head office, remote work, and co-working spaces, and use heatmap analysis to identify surpluses or shortages of meeting rooms and phone booths. The proposal department might also make suggestions to a company's general affairs department for improving office efficiency and optimizing costs. Additionally, the proposal department can advise co-working space operators on investment plans and operational policies for reducing congestion and vacancies. For example, the proposal department might make specific suggestions to co-working space operators for reducing congestion and vacancies.As a result, the Workplace Insights AI system according to this embodiment can achieve efficient office use and improved customer satisfaction.

[0059] The data collection unit collects data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. Specifically, when collecting CSV data on entry and exit, the unit obtains the date and time of entry and exit for each employee and visitor, as well as identification information of the person. This allows for an accurate understanding of office usage and the frequency of entry and exit. In addition, to create heat maps of congestion levels, data is collected from sensors and cameras installed in various areas of the office to measure congestion levels in real time. This allows for a visual display of congestion levels in specific time periods or areas. Furthermore, the data collection unit collects detailed data such as operating costs and equipment costs in order to conduct cost analysis of headquarters and facility operations. For example, it collects operating costs such as electricity bills, water bills, and cleaning costs, as well as equipment purchase costs and maintenance costs, enabling a comprehensive cost analysis. Finally, to perform sentiment analysis using natural language processing of customer reviews, the unit collects reviews from online review sites and social media and performs sentiment analysis using natural language processing technology. This allows for understanding customer satisfaction and dissatisfaction, which can then be used to improve services. The data collection department centrally manages this data and can link it with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses can be made to specific situations and conditions. As a result, the data collection department can collect data efficiently and effectively, improving the overall performance of the system.

[0060] The Analysis Department analyzes the data collected by the Data Collection Department. For example, the Analysis Department analyzes the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. Specifically, it analyzes CSV data on entry and exit to understand the frequency and time of office use by employees and visitors. This allows for a detailed understanding of office usage, which can be used to improve efficient office management. It also analyzes heat maps of congestion to understand the degree of congestion at specific times and in specific areas. This allows for the development of measures to alleviate congestion. Furthermore, it analyzes cost analysis data to understand the details of costs incurred in operating the headquarters and facilities. This allows for the development of concrete measures to reduce costs. In addition, it performs natural language analysis on customer reviews to conduct sentiment analysis. This allows for an understanding of customer satisfaction and dissatisfaction, which can be used to improve services. Based on this data, the Analysis Department can make proposals to improve the efficiency of office use and enhance customer satisfaction. For example, by analyzing office usage patterns in detail and understanding the frequency and time of use, it can propose optimizing the office layout and equipment placement. Furthermore, by analyzing customer behavior and identifying behavioral history and patterns, it becomes possible to propose services tailored to customer needs. In addition, the analytics department can perform long-term trend analysis and predictions based on the collected data. This allows for the development of strategies to respond to future fluctuations in office usage and changes in customer needs.

[0061] The Proposal Department makes proposals based on the analysis results obtained by the Analysis Department. For example, the Proposal Department might propose to a company's general affairs department a comprehensive visualization of the use of the head office, teleworking, and coworking spaces, and use heatmap analysis to identify surpluses or shortages of meeting rooms and phone booths. Specifically, by visualizing office usage and understanding the frequency and time of use of meeting rooms and phone booths, they can identify surpluses or shortages and propose optimal placement and operation methods. They can also comprehensively visualize the usage of teleworking and coworking spaces and propose efficient work styles. Furthermore, the Proposal Department can advise coworking space operators on investment plans and operational policies such as congestion reduction and vacancy reduction. For example, based on a heatmap of congestion, they can understand the level of congestion at specific times or in specific areas and propose concrete measures to alleviate congestion. They can also propose operational policies to reduce vacancies and support efficient space management. Through these proposals, the Proposal Department can help companies and coworking space operators achieve efficient office use and improved customer satisfaction. Furthermore, the Proposal Department can continuously monitor the effectiveness of the proposals and revise them as needed. This allows the proposal department to always provide optimal proposals based on the latest information and to respond flexibly to the needs of companies and coworking space operators.

[0062] The data collection unit can collect data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. For example, the data collection unit can collect CSV data on entry and exit, obtaining the date and time of entry and exit, as well as identification information of individuals. The data collection unit can also set the method for measuring congestion and the display format of the heat map to create a heat map of congestion levels. Furthermore, the data collection unit can perform cost analysis of headquarters and facility operations, collecting detailed data such as operating costs and equipment costs. For example, the data collection unit can collect CSV data on entry and exit in real time and generate a heat map of congestion levels. The data collection unit can also perform sentiment analysis by performing natural language processing on customer reviews. By collecting diverse data in this way, it is possible to understand office usage patterns and customer behavior. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input CSV data on entry and exit into a generating AI and have the generating AI perform the data analysis.

[0063] The analysis unit can analyze the collected data to understand office usage patterns and customer behavior. For example, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. For example, the analysis unit can analyze office usage patterns to understand usage frequency and time of day. The analysis unit can also analyze customer behavior to identify behavioral history and patterns. For example, the analysis unit can use the collected data to analyze office usage patterns in detail to understand customer behavior. In this way, data analysis can help understand office usage patterns and customer behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the data analysis.

[0064] The proposal department can, based on the analysis results, provide a comprehensive visualization of office usage, including headquarters, teleworking, and co-working spaces, for the general affairs department of a company, and propose solutions that identify surpluses or shortages of meeting rooms and phone booths using heatmap analysis. For example, the proposal department can make proposals to the general affairs department of a company to improve the efficiency of office use and optimize costs. For example, the proposal department can use heatmap analysis to identify surpluses or shortages of meeting rooms and phone booths. For example, the proposal department can make specific proposals to the general affairs department of a company to improve the efficiency of office use and optimize costs. This allows the general affairs department of a company to visualize usage and identify surpluses or shortages of meeting rooms and phone booths. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate the proposal content.

[0065] The proposal department can make proposals to coworking space operators, advising them on investment plans and operational policies such as congestion reduction and vacancy reduction, based on the analysis results. For example, the proposal department can make specific proposals to coworking space operators to reduce congestion and vacancy. For example, the proposal department can propose the introduction of a reservation system or adjustment of usage hours to reduce congestion. In addition, the proposal department can propose usage promotion campaigns or revisions to pricing to reduce vacancy. For example, the proposal department can make specific proposals to coworking space operators to reduce congestion and vacancy. This allows the proposal department to make proposals to coworking space operators to reduce congestion and vacancy. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analysis results into a generating AI and have the generating AI generate the proposal content.

[0066] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. Conversely, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0067] The data collection unit can focus on specific time periods or days of the week when collecting CSV data on entry and exit. For example, the unit can focus on collecting data during peak hours in the morning and evening on weekdays. It can also focus on collecting data on Saturdays and Sundays to understand weekend usage. Furthermore, the unit can collect data on days when specific events are held and analyze the impact of those events. This allows for a detailed understanding of usage patterns by focusing data collection on specific time periods or days of the week. 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 input data for specific time periods or days of the week into a generating AI and have the generating AI perform the data analysis.

[0068] The data collection unit can collect detailed data by focusing on specific areas or rooms when collecting heatmaps of congestion. For example, the data collection unit can collect detailed congestion data for each meeting room to understand the usage of meeting rooms. It can also collect congestion data for specific areas to understand the usage of shared spaces. Furthermore, it can collect detailed congestion data for each phone booth to understand the usage of phone booths. This makes it possible to understand usage in detail by collecting detailed data by focusing on specific areas or rooms. 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 input data for specific areas or rooms into a generating AI and have the generating AI perform data analysis.

[0069] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, important data can be collected preferentially by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0070] The data collection unit can collect detailed data by focusing on specific cost items when collecting cost analysis data related to headquarters and facility operations. For example, the data collection unit can collect detailed data on utility costs and analyze energy efficiency. It can also collect detailed data on cleaning costs and analyze cleaning efficiency. Furthermore, it can collect detailed data on maintenance costs and analyze maintenance efficiency. This enables efficient cost management by collecting detailed data by focusing on specific cost items. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on specific cost items into a generating AI and have the generating AI perform the data analysis.

[0071] The data collection unit can collect detailed data by focusing on specific keywords or phrases when collecting sentiment analysis of customer reviews using natural language processing. For example, the data collection unit can focus on the keyword "satisfied" to collect detailed positive reviews. It can also focus on the keyword "dissatisfied" to collect detailed negative reviews. Furthermore, it can focus on the keyword "improvement" to collect detailed reviews regarding areas for improvement. This allows for a detailed understanding of customer sentiment by collecting detailed data by focusing on specific keywords or phrases. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data on specific keywords or phrases into a generating AI and have the generating AI perform the data analysis.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a rapid analysis result. In this way, by adjusting the analysis method according to the user's emotions, the analysis unit can provide analysis results that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the analysis method.

[0073] The analysis unit can focus its analysis on specific time periods or days of the week when analyzing the collected data. For example, it can focus its analysis on data during peak hours in the morning and evening on weekdays. It can also focus its analysis on Saturday and Sunday data to understand weekend usage patterns. Furthermore, it can analyze data on days when specific events are held to analyze the impact of those events. This allows for a detailed understanding of usage patterns by focusing the analysis on specific time periods or days of the week. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For instance, the analysis unit can input data for specific time periods or days of the week into a generating AI and have the generating AI perform the data analysis.

[0074] The analysis unit can perform detailed analyses by focusing on specific areas or rooms when analyzing office usage patterns. For example, the analysis unit can perform detailed analyses of meeting room usage patterns. It can also perform detailed analyses of shared space usage patterns. Furthermore, it can perform detailed analyses of phone booth usage patterns. This allows for a detailed understanding of usage patterns by focusing on specific areas or rooms. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from specific areas or rooms into a generating AI and have the generating AI perform the data analysis.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display method suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0076] The analysis unit can perform detailed analyses by focusing on specific behavioral patterns when analyzing customer behavior. For example, the analysis unit can perform detailed analyses of customer entry and exit patterns. It can also perform detailed analyses of customer reactions to congestion levels. Furthermore, the analysis unit can perform detailed analyses of customer reviews. In this way, by focusing on specific behavioral patterns and performing detailed analyses, customer behavior can be understood in detail. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on specific behavioral patterns into a generating AI and have the generating AI perform the data analysis.

[0077] The analysis unit can perform detailed analyses by focusing on specific data sources when analyzing collected data. For example, the analysis unit can perform detailed analyses of CSV data on entry and exit. It can also perform detailed analyses of congestion heatmaps. Furthermore, the analysis unit can perform detailed cost analyses related to headquarters and facility operations. This allows for a detailed understanding of the data by focusing on specific data sources and performing detailed analyses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data from a specific data source into a generating AI and have the generating AI perform the data analysis.

[0078] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide concise and easy-to-implement suggestions. If the user is relaxed, it can provide more detailed suggestions. Furthermore, if the user is in a hurry, it can provide suggestions that can be implemented quickly. By adjusting the content of suggestions according to the user's emotions, the suggestion unit can provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the content of its suggestions.

[0079] The proposal department can focus its proposals on specific time slots or days of the week when making suggestions to the general affairs departments of companies. For example, the proposal department can focus its suggestions on peak hours in the morning and evening on weekdays. It can also make suggestions for Saturdays and Sundays to understand weekend usage. Furthermore, the proposal department can make suggestions for days when specific events are held. This allows for a detailed understanding of usage patterns by focusing suggestions on specific time slots or days of the week. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific time slots or days of the week into a generating AI and have the generating AI generate the proposal content.

[0080] The proposal department can make detailed proposals to coworking space operators by focusing on specific areas or rooms. For example, the proposal department can propose adding or removing meeting rooms based on their usage. It can also propose optimizing shared spaces based on their usage. Furthermore, it can propose adding or removing phone booths based on their usage. This allows for a detailed understanding of usage patterns by focusing on specific areas or rooms and making detailed proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific areas or rooms into a generating AI and have the generating AI generate proposals.

[0081] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will prioritize high-priority suggestions. If the user is relaxed, the suggestion unit will prioritize detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit will prioritize suggestions that can be acted upon quickly. In this way, by prioritizing suggestions according to the user's emotions, important suggestions can be prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0082] The proposal department can focus on specific cost items and provide detailed proposals when making suggestions to the general affairs departments of companies. For example, the proposal department can focus on reducing utility costs. It can also focus on reducing cleaning costs. Furthermore, it can focus on reducing maintenance costs. This allows for efficient cost management by focusing on specific cost items and providing detailed proposals. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific cost items into a generating AI and have the generating AI generate the proposal content.

[0083] The proposal department can focus on specific customer segments and provide detailed proposals when making suggestions to coworking space operators. For example, the proposal department can focus on freelance users and propose flexible plans. It can also focus on startup companies and propose cost-effective plans. Furthermore, it can focus on satellite office users of large corporations and propose high-performance equipment. In this way, by focusing on specific customer segments and providing detailed proposals, it becomes possible to make proposals that meet customer needs. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input data on a specific customer segment into a generating AI and have the generating AI generate proposal content.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of high-importance data. If the user is relaxed, it can prioritize the analysis of detailed data. Furthermore, if the user is in a hurry, it can prioritize the analysis of data that can be analyzed quickly. In this way, by determining the priority of analysis according to the user's emotions, important data can be analyzed preferentially. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0086] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion timing can be delayed. Conversely, if the user is relaxed, the suggestion timing can be advanced. Furthermore, if the user is in a hurry, suggestions can be made quickly. In this way, by adjusting the timing of suggestions according to the user's emotions, suggestions can be made at a time that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into the generative AI and have the generative AI adjust the timing of suggestions.

[0087] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, a simple data collection method can be adopted. If the user is relaxed, a detailed data collection method can be adopted. Furthermore, if the user is in a hurry, a method for rapid data collection can be adopted. In this way, by adjusting the data collection method according to the user's emotions, a data collection method suitable for the user can be provided. 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 input the user's emotion data into the generative AI and have the generative AI adjust the data collection method.

[0088] The analysis unit can perform detailed analyses by focusing on specific data sources when analyzing collected data. For example, it can perform detailed analyses of CSV data on entry and exit. It can also perform detailed analyses of congestion heatmaps. Furthermore, it can perform detailed cost analyses related to headquarters and facility operations. This allows for a detailed understanding of the data by focusing on specific data sources and performing detailed analyses. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input data from a specific data source into a generating AI and have the generating AI perform the data analysis.

[0089] The proposal department can focus on specific cost items when making proposals to the general affairs departments of companies, providing detailed suggestions. For example, it can focus on reducing utility costs, cleaning costs, and maintenance costs. By focusing on specific cost items and providing detailed suggestions, efficient cost management becomes possible. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific cost items into a generating AI and have the generating AI generate the proposal content.

[0090] The data collection unit can collect detailed data by focusing on specific keywords or phrases when collecting sentiment analysis of customer reviews using natural language processing. For example, it can focus on the keyword "satisfied" to collect detailed positive reviews. It can also focus on the keyword "dissatisfied" to collect detailed negative reviews. Furthermore, it can focus on the keyword "improvement" to collect detailed reviews regarding areas for improvement. In this way, by collecting detailed data by focusing on specific keywords or phrases, it is possible to gain a detailed understanding of customer sentiment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data of specific keywords or phrases into a generating AI and have the generating AI perform the data analysis.

[0091] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display method suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results.

[0092] The proposal department can focus on specific customer segments and provide detailed proposals when making suggestions to coworking space operators. For example, it can focus on freelance users and propose flexible plans. It can also focus on startup companies and propose cost-effective plans. Furthermore, it can focus on satellite office users of large corporations and propose high-performance equipment. By focusing on specific customer segments and providing detailed proposals, it becomes possible to make proposals that meet customer needs. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on a specific customer segment into a generating AI and have the generating AI generate proposal content.

[0093] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, it can prioritize the collection of high-priority data. If the user is relaxed, it can prioritize the collection of detailed data. Furthermore, if the user is in a hurry, it can prioritize the collection of data that can be collected quickly. In this way, by prioritizing the data to be collected according to the user's emotions, important data can be collected preferentially. 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, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0094] The proposal department can make detailed proposals to coworking space operators by focusing on specific areas or rooms. For example, it can propose adding or removing meeting rooms based on their usage. It can also propose optimizing shared spaces based on their usage. Furthermore, it can propose adding or removing phone booths based on their usage. This allows for a detailed understanding of usage patterns by focusing on specific areas or rooms and making detailed proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on specific areas or rooms into a generating AI and have the generating AI generate the proposal content.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit collects data. The data collection unit collects data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. The data collection unit collects CSV data on entry and exit, obtaining the date and time of entry and exit, as well as identification information of individuals. It can also set the method for measuring congestion and the display format of the heat map in order to create a heat map of congestion levels. Furthermore, it can perform cost analysis of headquarters and facility operations, collecting detailed data such as operating costs and equipment costs. The data collection unit collects CSV data on entry and exit in real time and generates a heat map of congestion levels. Furthermore, it can perform sentiment analysis by performing natural language processing on customer reviews. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data using statistical analysis and machine learning algorithms to understand office usage patterns and customer behavior. The analysis unit analyzes office usage patterns to understand usage frequency and time of day. It can also analyze customer behavior to identify behavioral history and patterns. Furthermore, based on the collected data, it can make suggestions for improving the efficiency of office use and enhancing customer satisfaction. Step 3: The proposal department makes proposals based on the analysis results obtained by the analysis department. The proposal department proposes to the general affairs departments of companies to comprehensively visualize the usage of headquarters, teleworking, co-working spaces, etc., and to point out the surplus or shortage of meeting rooms and phone booths using heat map analysis. Furthermore, it makes proposals for improving the efficiency of office use and optimizing costs. The proposal department can also make proposals to co-working space operators, advising them on investment plans and operational policies such as reducing congestion and vacancies.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects CSV data on entry and exit and a heat map of congestion using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions based on the analysis results to improve the efficiency of office use and enhance customer satisfaction. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects CSV data of entry and exit and a heat map of congestion using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using statistical analysis and machine learning algorithms. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and makes suggestions based on the analysis results for improving the efficiency of office use and enhancing customer satisfaction. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects CSV data on entry and exit and a heat map of congestion using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using statistical analysis and machine learning algorithms. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions based on the analysis results to improve the efficiency of office use and enhance customer satisfaction. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects CSV data of entry and exit and a heat map of congestion using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using statistical analysis and machine learning algorithms. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and makes suggestions based on the analysis results for improving the efficiency of office use and enhancing customer satisfaction. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that makes proposals based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the collected data, we can understand office usage patterns and customer behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose a solution for corporate general affairs departments that provides an integrated visualization of usage patterns for headquarters, teleworking, co-working spaces, etc., and uses heatmap analysis to identify any surpluses or shortages of meeting rooms and phone booths. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the analysis results, we will propose investment plans and operational policies to coworking space operators, advising them on measures such as reducing congestion and vacancies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting CSV data on building entry and exit, focus on collecting data for specific time periods or days of the week. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting heatmaps of congestion levels, focus on specific areas or rooms to gather detailed data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting cost analysis data for headquarters and facility operations, focus on specific cost items to gather detailed data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting sentiment analysis data from customer reviews using natural language processing, we focus on specific keywords and phrases to gather detailed data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing collected data, we focus the analysis on specific time periods or days of the week. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing office usage patterns, we focus on specific areas or rooms to conduct detailed analyses. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing customer behavior, we focus on specific behavioral patterns and conduct detailed analyses. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing collected data, we focus on specific data sources to perform detailed analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the content of the suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making proposals to the general affairs departments of companies, focus your proposals on specific time slots or days of the week. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals to coworking space operators, focus on specific areas or rooms and provide detailed suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making proposals to the general affairs department of a company, focus on specific cost items and provide detailed proposals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals to coworking space operators, focus on specific customer segments and provide detailed proposals. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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 data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a proposal unit that makes proposals based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as CSV data on entry and exit, heat maps of congestion levels, cost analysis of headquarters and facility operations, and sentiment analysis of customer reviews using natural language processing. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the collected data, we can understand office usage patterns and customer behavior. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose a solution for corporate general affairs departments that provides an integrated visualization of usage patterns for headquarters, teleworking, co-working spaces, etc., and uses heatmap analysis to identify any surpluses or shortages of meeting rooms and phone booths. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we will propose investment plans and operational policies to coworking space operators, advising them on measures such as reducing congestion and vacancies. The system according to feature 1.

6. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

7. The aforementioned collection unit is When collecting CSV data on building entry and exit, focus on collecting data for specific time periods or days of the week. The system according to feature 1.

8. The aforementioned collection unit is When collecting heatmaps of congestion levels, focus on specific areas or rooms to gather detailed data. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting cost analysis data for headquarters and facility operations, focus on specific cost items to gather detailed data. The system according to feature 1.

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