System

Abstract

An object of a system according to an embodiment is to determine the priority of a user at the next reservation based on the frequency of use of a meeting room.SOLUTION: A system according to an embodiment includes a usage scoring unit, a credit score calculation unit, and a reservation priority determination unit. The usage scoring unit scores the conference room usage of the user. The credit score calculation unit calculates a credit score based on the use frequency scored by the use frequency scoring unit. A reservation priority determination part determines the priority of the user in the next conference room reservation on the basis of the credit score.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not have a mechanism for determining user priorities at the next reservation based on the utilization of the conference room, which makes it difficult to use the conference room efficiently.

[0005] The system according to the embodiment aims to determine the priority of users at the time of the next reservation based on the utilization rate of the conference room. [Means for solving the problem]

[0006] The system according to the embodiment includes a utilization scoring unit, a credit score calculation unit, and a reservation priority determination unit. The utilization scoring unit scores the utilization of a conference room by a user. The credit score calculation unit calculates a credit score based on the utilization scored by the utilization scoring unit. The reservation priority determination unit determines the priority of the user when reserving a conference room next based on the credit score. [Effects of the Invention]

[0007] The system according to the embodiment can determine the priority of users for the next reservation based on the utilization of the conference room. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The conference room reservation system according to an embodiment of the present invention is a system that scores the degree of conference room utilization of users and determines the priority of users at the next conference room reservation based on the score. As a result, the conference room reservation system determines the priority at the next reservation based on the degree of conference room utilization of users, thereby improving the efficiency of conference room utilization.

[0029] The conference room reservation system according to the embodiment includes a utilization scoring unit, a credit score calculation unit, and a reservation priority determination unit. The utilization scoring unit scores the utilization of a conference room by a user. For example, the utilization scoring unit records the utilization time of a conference room and assigns a higher score to a user who utilizes the conference room for a longer period of time. The utilization scoring unit also records the utilization frequency of a conference room and assigns a higher score to a user who utilizes the conference room frequently. The utilization scoring unit also records the cancellation rate of reservations and assigns a higher score to a user who utilizes the conference room less frequently. For example, the utilization scoring unit records the utilization time of a conference room in minutes and assigns a higher score to a user who utilizes the conference room less frequently. The utilization frequency is recorded by recording the number of times the conference room is utilized per week and assigns a higher score to a user who utilizes the conference room more frequently. The cancellation rate is recorded by recording the number of cancellations of reservations and assigns a higher score to a user who utilizes the conference room less frequently. The credit score calculation unit calculates a credit score based on the utilization score calculated by the utilization scoring unit. For example, the credit score calculation unit calculates a credit score for a user based on the utilization score, ranging from 0 to 100. The credit score calculation unit can also calculate the user's credit score as a rating from A to F based on the usage score. The credit score calculation unit can also calculate the user's credit score as a percentile based on the usage score. For example, the credit score calculation unit assigns a higher credit score to a higher usage score. Scoring in the range of 0 to 100 evaluates the usage score out of 100, with a higher score being assigned to a higher usage. The rating from A to F assigns a rating from A to F based on the usage score, with A being the highest and F being the lowest. Percentiles indicate a user's relative position within the whole, and high ratings are assigned to users in the top 10%. The reservation priority determination unit determines the priority of users when reserving a conference room next based on the credit score. For example, the reservation priority determination unit gives priority to users with high credit scores when reserving a conference room. The reservation priority determination unit can also give priority to providing reservations for earlier time slots to users with high credit scores. The reservation priority determination unit can also give priority to providing specific conference rooms to users with high credit scores. For example, the reservation priority determination unit allows users with high credit scores to reserve conference rooms earlier than other users.Early time slot reservations provide users with high credit scores with priority in popular time slots, such as mornings or lunch breaks. Prioritizing specific conference rooms provides users with high credit scores with priority in well-equipped or large conference rooms. As a result, the conference room reservation system according to the embodiment determines priorities for the next reservation based on the user's conference room usage, improving the efficiency of conference room usage. For example, the output unit displays the reservation results to the user via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending by email provides quick feedback by sending the results directly to the user.

[0030] The utilization scoring unit can analyze audio data when a conference room is in use and score the productivity of the meeting and the engagement of participants. For example, the utilization scoring unit collects audio data from within the conference room and analyzes it using AI. For example, it evaluates the frequency of comments and the quality of the content to score the productivity of the meeting. The utilization scoring unit also evaluates the engagement of participants based on the audio data from the meeting. For example, it scores the number of comments and the number of questions. The utilization scoring unit also analyzes the audio data from the meeting and scores the depth of the discussion and the progress of problem-solving. For example, a meeting that produces a concrete solution will be given a high score. In this way, the quality of the meeting can be evaluated by scoring the productivity of the meeting and the engagement of the participants.

[0031] The utilization scoring unit uses AI to analyze users' behavioral patterns when using conference rooms and can assign a score to the degree of adherence to meeting start and end times. For example, the utilization scoring unit records the start and end times of meetings and analyzes them with AI. For example, meetings that start and end on time are assigned a high score. The utilization scoring unit also assigns a score to the frequency of meeting delays and early departures. For example, users with few delays are assigned a high score. The utilization scoring unit also monitors the progress of meetings in real time and assigns a high score to meetings that proceed as planned. For example, it evaluates the allocation of time for each agenda item. This allows time management to be evaluated by assigning a score to the degree of adherence to meeting start and end times.

[0032] The utilization scoring unit can take into account the user's usage of other office equipment in the utilization score of a conference room. The utilization scoring unit, for example, records the frequency of use of a projector or whiteboard in the conference room and reflects this in the score. For example, a high score is given to a user who uses them frequently. The utilization scoring unit also monitors the usage of other office equipment and evaluates the quality of a meeting. For example, a meeting that makes effective use of a projector may receive a high score. The utilization scoring unit also integrates equipment usage data within the conference room to calculate an overall utilization score. For example, a meeting that makes balanced use of multiple pieces of equipment may receive a high score. In this way, by taking into account the usage of other office equipment in the utilization score of a conference room, it is possible to evaluate overall utilization.

[0033] The utilization scoring unit can score the amount of energy consumed when a conference room is in use and award high scores to eco-friendly users. The utilization scoring unit, for example, collects energy consumption data from a conference room and analyzes it using AI. For example, a meeting with low energy consumption is given a high score. The utilization scoring unit also evaluates energy-efficient meetings. For example, a meeting that proceeds efficiently in a short amount of time is given a high score. The utilization scoring unit also scores eco-friendly behavior. For example, it evaluates behavior such as turning off the power after a meeting ends. In this way, environmentally friendly usage can be promoted by scoring energy consumption and evaluating eco-friendly users.

[0034] The credit score calculation unit can integrate a user's past conference room usage history with other business performance data to calculate an overall credit score. For example, the credit score calculation unit integrates a user's conference room usage history with business performance data and analyzes it using AI. For example, it assigns a high score to users with high business performance. The credit score calculation unit also associates past conference room usage data with business results to calculate an overall credit score. For example, it assigns a high rating to users whose business performance is reflected in the results of meetings. The credit score calculation unit also evaluates the effectiveness of conference room usage based on the user's business performance data. For example, it assigns a high score to users whose business efficiency has improved after a meeting. In this way, the reliability of a user can be evaluated by calculating an overall credit score by integrating past conference room usage history with business performance data.

[0035] The credit score calculation unit can analyze a user's network activity when using a conference room and assign a score to determine whether the user is using the network appropriately. For example, the credit score calculation unit collects network activity data when using a conference room and analyzes it using AI. For example, it assigns a high score to a user who uses the Internet appropriately. The credit score calculation unit also evaluates the content of network activity and assigns a high score to a user who frequently uses the network for work-related purposes. For example, it evaluates users who use work-related sites and tools. The credit score calculation unit also assigns a score to the appropriateness of network activity. For example, it assigns a high score to a user who does not browse unrelated sites during a meeting. In this way, by analyzing network activity when using a conference room and assessing whether the user is using the network appropriately, the user's credibility is improved.

[0036] The credit score calculation unit can take into account the user's other in-company activities in the credit score. The credit score calculation unit, for example, collects data on the user's participation in in-company events and reflects this in the credit score. For example, a high score is given to a user who actively participates in in-company events. The credit score calculation unit also evaluates participation in in-company activities and gives a high score to a user who makes a high contribution to the company. For example, it evaluates volunteer activities and project participation. The credit score calculation unit also scores the frequency of participation in in-company events and gives a high score to a user who actively participates in in-company activities. For example, it evaluates a user who regularly participates in events. In this way, by taking in-company activities into account in the credit score, the user's overall trustworthiness can be evaluated.

[0037] The credit score calculation unit can analyze health data when a user uses a conference room and assign a score to indicate whether the user is using the conference room in a healthy manner. For example, the credit score calculation unit collects heart rate data during a conference room and analyzes it using AI. For example, it assigns a high score to a user who maintains a stable heart rate. The credit score calculation unit also monitors stress levels and assigns a high score to a user who conducts a conference room with low stress. For example, it evaluates users with low stress. The credit score calculation unit also assigns a score to healthy conference room use based on the health data. For example, it assigns a high score to a user who conducts a conference room in a relaxed state. This allows the analysis of health data when using a conference room and the evaluation of whether the user is using the conference room in a healthy manner to promote health management for users.

[0038] The reservation priority determination unit uses AI to analyze a user's past conference room reservation patterns and suggest the optimal reservation timing. The reservation priority determination unit, for example, analyzes the user's past conference room reservation data and suggests the optimal reservation timing. For example, it may make suggestions based on the time periods that the user frequently makes reservations for. The reservation priority determination unit also monitors the usage status of conference rooms in real time and suggests the optimal reservation timing based on availability. For example, it may suggest time periods with many available spaces. The reservation priority determination unit also integrates the user's schedule data and suggests the optimal reservation timing. For example, it may suggest time periods that do not overlap with other plans. In this way, by analyzing past conference room reservation patterns and suggesting the optimal reservation timing, the efficiency of conference room usage is improved.

[0039] The reservation priority determination unit can dynamically adjust the available time of the conference room based on the credit score and provide an optimal usage schedule. The reservation priority determination unit dynamically adjusts the available time of the conference room based on, for example, the user's credit score. For example, it provides available time preferentially to users with high scores. The reservation priority determination unit also monitors the usage status of the conference room in real time and adjusts the available time according to the credit score. For example, it allocates free time to users with high scores. The reservation priority determination unit also provides an optimal usage schedule based on the user's schedule data. For example, it adjusts the schedule so that it does not overlap with other appointments. In this way, the available time of the conference room is dynamically adjusted based on the credit score and an optimal usage schedule is provided, thereby improving user satisfaction.

[0040] The reservation priority determination unit can integrate reservation systems for other office facilities into the prioritized conference room reservation system. For example, the reservation priority determination unit can integrate reservation functions for cafeterias and break rooms into the conference room reservation system, allowing users to make reservations all at once. For example, ensuring break time after a meeting. The reservation priority determination unit also monitors the reservation status of other office facilities in real time and proposes the optimal reservation plan to the user. For example, reserving a meeting and a break consecutively. The reservation priority determination unit also provides the optimal usage schedule for conference rooms and other office facilities based on the user's schedule data. For example, it proposes a break room where users can refresh themselves after a meeting. In this way, integrating reservation systems for other office facilities into the conference room reservation system improves user convenience.

[0041] The reservation priority determination unit can provide an option to customize the layout and facilities of a conference room based on the credit score. The reservation priority determination unit provides an option to customize the layout and facilities of a conference room based on the user's credit score, for example. For example, a special layout is provided for users with a high score. The reservation priority determination unit also monitors the usage status of the conference room in real time and adjusts the layout and facilities according to the credit score. For example, it changes the placement of the projector or whiteboard. The reservation priority determination unit also suggests the optimal layout and facilities based on the user's schedule data. For example, it provides a layout according to the content of the conference. In this way, by providing an option to customize the layout and facilities of a conference room based on the credit score, user satisfaction is improved.

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

[0043] The usage scoring unit can score the amount of energy consumed when a conference room is in use and award high scores to eco-friendly users. For example, energy consumption data from a conference room can be collected and analyzed using AI. Meetings that consume less energy can be given a high score. Energy-efficient meetings can also be evaluated. For example, a meeting that proceeds efficiently in a short amount of time can be given a high score. Furthermore, eco-friendly behavior can be scored. For example, behavior such as turning off the power after a meeting is over can be evaluated. In this way, by scoring energy consumption and evaluating eco-friendly users, it is possible to promote environmentally conscious usage.

[0044] The usage scoring unit can analyze audio data when a conference room is in use and score the productivity of the meeting and the engagement of participants. For example, it can collect audio data from within the conference room and analyze it using AI. It evaluates the frequency of comments and the quality of the content to score the productivity of the meeting. It can also evaluate the engagement of participants based on the audio data from the meeting. For example, it can score the number of comments and the number of questions. It can also analyze the audio data from the meeting and score the depth of the discussion and the progress in problem-solving. For example, a meeting that produces concrete solutions can be given a high score. This makes it possible to evaluate the quality of a meeting by scoring its productivity and the engagement of participants.

[0045] The usage scoring unit uses AI to analyze users' behavioral patterns when using conference rooms, and can score their compliance with meeting start and end times. For example, the start and end times of meetings are recorded and analyzed using AI. Meetings that start and end on time are given a high score. The system also scores the frequency of meeting delays and early departures. High scores are awarded to users with minimal delays. Furthermore, the system monitors the progress of meetings in real time, and gives high scores to meetings that proceed as planned. Time allocation for each agenda item is evaluated. This allows time management to be evaluated by scoring compliance with meeting start and end times.

[0046] The utilization scoring unit can take into account the user's usage of other office equipment in the utilization score of a conference room. For example, it can record the frequency of use of the projector or whiteboard in the conference room and reflect this in the score. High scores are given to users who use them frequently. It also monitors the usage of other office equipment to evaluate the quality of meetings. Meetings that make effective use of the projector are given a high score. Furthermore, it integrates equipment usage data within the conference room to calculate an overall utilization score. Meetings that make balanced use of multiple pieces of equipment are given a high score. In this way, by taking into account the usage of other office equipment in the utilization score of a conference room, it is possible to evaluate overall utilization.

[0047] The usage scoring unit can score the amount of energy consumed when using a conference room and award high scores to eco-friendly users. For example, energy consumption data for a conference room can be collected and analyzed using AI. Meetings that consume less energy can be given a high score. Energy-efficient meetings can also be evaluated. Meetings that proceed efficiently in a short amount of time can be given a high score. Furthermore, eco-friendly behavior can be scored, such as turning off the power after a meeting has ended. In this way, by scoring energy consumption and evaluating eco-friendly users, it is possible to promote environmentally friendly usage.

[0048] The credit score calculation unit can integrate a user's past conference room usage history with other business performance data to calculate an overall credit score. For example, the user's conference room usage history is integrated with business performance data and analyzed using AI. A high score is awarded to users with high business performance. Additionally, past conference room usage data is associated with business results to calculate an overall credit score. Users whose meeting results are reflected in their work are highly rated. Furthermore, the effectiveness of conference room usage is evaluated based on business performance data. A high score is awarded to users whose work efficiency has improved after a meeting. In this way, a user's reliability can be evaluated by integrating past conference room usage history with business performance data to calculate an overall credit score.

[0049] The trust score calculation unit can analyze a user's network activity when using a conference room and assign a score to determine whether the user is using the internet appropriately. For example, network activity data when using a conference room is collected and analyzed using AI. A high score is assigned to users who use the internet appropriately. The content of network activity is also evaluated, and users who frequently use the internet for work-related purposes are assigned a high score. Users who use work-related sites and tools are evaluated. Furthermore, the appropriateness of network activity is scored. A high score is assigned to users who do not browse unrelated sites during a meeting. This improves the user's credibility by analyzing network activity when using a conference room and assessing whether the user is using the internet appropriately.

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

[0051] Step 1: The usage scoring unit scores the user's usage of the conference room. For example, it records the time spent using the conference room and gives a high score to users who spend a long time using the conference room. It also records the frequency of conference room use and gives a high score to users who use the conference room frequently. It also records the reservation cancellation rate and gives a high score to users who cancel less. Step 2: The credit score calculation unit calculates a credit score based on the usage scored by the usage scoring unit. For example, the credit score of the user is calculated in the range of 0 to 100 based on the usage score. The credit score of the user can also be calculated as a rating from A to F based on the usage score. Furthermore, the credit score of the user can also be calculated as a percentile based on the usage score. Step 3: The reservation priority determination unit determines the priority of users when reserving a conference room based on their credit scores. For example, users with high credit scores can be given priority in reserving conference rooms. It is also possible to give priority to users with high credit scores for reservations in earlier time slots. Furthermore, it is also possible to give priority to specific conference rooms for users with high credit scores.

[0052] (Example 2) The conference room reservation system according to an embodiment of the present invention is a system that scores the degree of conference room utilization of users and determines the priority of users at the next conference room reservation based on the score. As a result, the conference room reservation system determines the priority at the next reservation based on the degree of conference room utilization of users, thereby improving the efficiency of conference room utilization.

[0053] The conference room reservation system according to the embodiment includes a utilization scoring unit, a credit score calculation unit, and a reservation priority determination unit. The utilization scoring unit scores the utilization of a conference room by a user. For example, the utilization scoring unit records the utilization time of a conference room and assigns a higher score to a user who utilizes the conference room for a longer period of time. The utilization scoring unit also records the utilization frequency of a conference room and assigns a higher score to a user who utilizes the conference room frequently. The utilization scoring unit also records the cancellation rate of reservations and assigns a higher score to a user who utilizes the conference room less frequently. For example, the utilization scoring unit records the utilization time of a conference room in minutes and assigns a higher score to a user who utilizes the conference room less frequently. The utilization frequency is recorded by recording the number of times the conference room is utilized per week and assigns a higher score to a user who utilizes the conference room more frequently. The cancellation rate is recorded by recording the number of cancellations of reservations and assigns a higher score to a user who utilizes the conference room less frequently. The credit score calculation unit calculates a credit score based on the utilization score calculated by the utilization scoring unit. For example, the credit score calculation unit calculates a credit score for a user based on the utilization score, ranging from 0 to 100. The credit score calculation unit can also calculate the user's credit score as a rating from A to F based on the usage score. The credit score calculation unit can also calculate the user's credit score as a percentile based on the usage score. For example, the credit score calculation unit assigns a higher credit score to a higher usage score. Scoring in the range of 0 to 100 evaluates the usage score out of 100, with a higher score being assigned to a higher usage. The rating from A to F assigns a rating from A to F based on the usage score, with A being the highest and F being the lowest. Percentiles indicate a user's relative position within the whole, and high ratings are assigned to users in the top 10%. The reservation priority determination unit determines the priority of users when reserving a conference room next based on the credit score. For example, the reservation priority determination unit gives priority to users with high credit scores when reserving a conference room. The reservation priority determination unit can also give priority to providing reservations for earlier time slots to users with high credit scores. The reservation priority determination unit can also give priority to providing specific conference rooms to users with high credit scores. For example, the reservation priority determination unit allows users with high credit scores to reserve conference rooms earlier than other users.Early time slot reservations provide users with high credit scores with priority in popular time slots, such as mornings or lunch breaks. Prioritizing specific conference rooms provides users with high credit scores with priority in well-equipped or large conference rooms. As a result, the conference room reservation system according to the embodiment determines priorities for the next reservation based on the user's conference room usage, improving the efficiency of conference room usage. For example, the output unit displays the reservation results to the user via a web application or mobile application. If feedback on paper is desired, the results are printed using a printer. Sending by email provides quick feedback by sending the results directly to the user.

[0054] The utilization scoring unit can analyze audio data when a conference room is in use and score the productivity of the meeting and the engagement of participants. For example, the utilization scoring unit collects audio data from within the conference room and analyzes it using AI. For example, it evaluates the frequency of comments and the quality of the content to score the productivity of the meeting. The utilization scoring unit also evaluates the engagement of participants based on the audio data from the meeting. For example, it scores the number of comments and the number of questions. The utilization scoring unit also analyzes the audio data from the meeting and scores the depth of the discussion and the progress of problem-solving. For example, a meeting that produces a concrete solution will be given a high score. In this way, the quality of the meeting can be evaluated by scoring the productivity of the meeting and the engagement of the participants.

[0055] The utilization scoring unit uses AI to analyze users' behavioral patterns when using conference rooms and can assign a score to the degree of adherence to meeting start and end times. For example, the utilization scoring unit records the start and end times of meetings and analyzes them with AI. For example, meetings that start and end on time are assigned a high score. The utilization scoring unit also assigns a score to the frequency of meeting delays and early departures. For example, users with few delays are assigned a high score. The utilization scoring unit also monitors the progress of meetings in real time and assigns a high score to meetings that proceed as planned. For example, it evaluates the allocation of time for each agenda item. This allows time management to be evaluated by assigning a score to the degree of adherence to meeting start and end times.

[0056] The usage scoring unit uses the emotion estimation function to analyze the emotions of participants during a meeting, and can assign a high score to meetings where there are many positive emotions. For example, the usage scoring unit captures the facial expressions of participants during a meeting with a camera and analyzes their emotions using AI. For example, it assigns a high score to meetings where there are many smiling faces. The usage scoring unit also analyzes audio data and assigns a high score to meetings where there are many positive-toned comments. For example, it evaluates meetings where there are many words of praise and gratitude. The usage scoring unit also scores the atmosphere of the meeting based on the emotion estimation data. For example, it assigns a high score to meetings where positive emotions are sustained. In this way, the atmosphere of a meeting can be evaluated by analyzing the emotions of participants during a meeting and assigning a high score to meetings where there are many positive emotions.

[0057] The utilization scoring unit can take into account the user's usage of other office equipment in the utilization score of a conference room. The utilization scoring unit, for example, records the frequency of use of a projector or whiteboard in the conference room and reflects this in the score. For example, a high score is given to a user who uses them frequently. The utilization scoring unit also monitors the usage of other office equipment and evaluates the quality of a meeting. For example, a meeting that makes effective use of a projector may receive a high score. The utilization scoring unit also integrates equipment usage data within the conference room to calculate an overall utilization score. For example, a meeting that makes balanced use of multiple pieces of equipment may receive a high score. In this way, by taking into account the usage of other office equipment in the utilization score of a conference room, it is possible to evaluate overall utilization.

[0058] The utilization scoring unit can score the amount of energy consumed when a conference room is in use and award high scores to eco-friendly users. The utilization scoring unit, for example, collects energy consumption data from a conference room and analyzes it using AI. For example, a meeting with low energy consumption is given a high score. The utilization scoring unit also evaluates energy-efficient meetings. For example, a meeting that proceeds efficiently in a short amount of time is given a high score. The utilization scoring unit also scores eco-friendly behavior. For example, it evaluates behavior such as turning off the power after a meeting ends. In this way, environmentally friendly usage can be promoted by scoring energy consumption and evaluating eco-friendly users.

[0059] The usage scoring unit uses the emotion estimation function to collect feedback after conference room use and can assign high scores to users who provide a lot of positive feedback. For example, the usage scoring unit collects feedback from participants after a conference has ended and analyzes emotions using AI. For example, a conference with a lot of positive feedback is assigned a high score. The usage scoring unit also analyzes the content of the feedback and assigns high scores to users with a lot of positive opinions. For example, it evaluates praise for the progress and content of the conference. The usage scoring unit also scores the quality of the feedback based on the emotion estimation data. For example, a conference with few specific improvement suggestions is assigned a high score. In this way, user satisfaction is improved by collecting feedback after conference room use and evaluating users with a lot of positive feedback.

[0060] The credit score calculation unit can integrate a user's past conference room usage history with other business performance data to calculate an overall credit score. For example, the credit score calculation unit integrates a user's conference room usage history with business performance data and analyzes it using AI. For example, it assigns a high score to users with high business performance. The credit score calculation unit also associates past conference room usage data with business results to calculate an overall credit score. For example, it assigns a high rating to users whose business performance is reflected in the results of meetings. The credit score calculation unit also evaluates the effectiveness of conference room usage based on the user's business performance data. For example, it assigns a high score to users whose business efficiency has improved after a meeting. In this way, the reliability of a user can be evaluated by calculating an overall credit score by integrating past conference room usage history with business performance data.

[0061] The credit score calculation unit can analyze a user's network activity when using a conference room and assign a score to determine whether the user is using the network appropriately. For example, the credit score calculation unit collects network activity data when using a conference room and analyzes it using AI. For example, it assigns a high score to a user who uses the Internet appropriately. The credit score calculation unit also evaluates the content of network activity and assigns a high score to a user who frequently uses the network for work-related purposes. For example, it evaluates users who use work-related sites and tools. The credit score calculation unit also assigns a score to the appropriateness of network activity. For example, it assigns a high score to a user who does not browse unrelated sites during a meeting. In this way, by analyzing network activity when using a conference room and assessing whether the user is using the network appropriately, the user's credibility is improved.

[0062] The credit score calculation unit can use the emotion estimation function to analyze the emotional fluctuations of participants during a meeting and assign a high credit score to users who maintain stable emotions. The credit score calculation unit, for example, monitors the emotional fluctuations of participants during a meeting in real time and assigns a high score to users who maintain stable emotions. For example, it evaluates users with little emotional fluctuation. The credit score calculation unit also scores the stability of emotions during a meeting based on the emotion estimation data. For example, it assigns a high score to users who maintain positive emotions. The credit score calculation unit also analyzes emotional fluctuations during a meeting and assigns a high score to users who experience little stress or tension. For example, it evaluates users who conduct a meeting in a relaxed state. In this way, by analyzing the emotional fluctuations of participants during a meeting and evaluating users who maintain stable emotions, trustworthiness is improved.

[0063] The credit score calculation unit can take into account the user's other in-company activities in the credit score. The credit score calculation unit, for example, collects data on the user's participation in in-company events and reflects this in the credit score. For example, a high score is given to a user who actively participates in in-company events. The credit score calculation unit also evaluates participation in in-company activities and gives a high score to a user who makes a high contribution to the company. For example, it evaluates volunteer activities and project participation. The credit score calculation unit also scores the frequency of participation in in-company events and gives a high score to a user who actively participates in in-company activities. For example, it evaluates a user who regularly participates in events. In this way, by taking in-company activities into account in the credit score, the user's overall trustworthiness can be evaluated.

[0064] The credit score calculation unit can analyze health data when a user uses a conference room and assign a score to indicate whether the user is using the conference room in a healthy manner. For example, the credit score calculation unit collects heart rate data during a conference room and analyzes it using AI. For example, it assigns a high score to a user who maintains a stable heart rate. The credit score calculation unit also monitors stress levels and assigns a high score to a user who conducts a conference room with low stress. For example, it evaluates users with low stress. The credit score calculation unit also assigns a score to healthy conference room use based on the health data. For example, it assigns a high score to a user who conducts a conference room in a relaxed state. This allows the analysis of health data when using a conference room and the evaluation of whether the user is using the conference room in a healthy manner to promote health management for users.

[0065] The credit score calculation unit can use the emotion estimation function to analyze the emotions of participants after the meeting and assign a high credit score to users who elicit positive emotions. The credit score calculation unit, for example, monitors the emotions of participants after the meeting in real time and assigns a high score to users who elicit positive emotions. For example, it evaluates facial expressions of gratitude and satisfaction. The credit score calculation unit also scores emotional fluctuations after the meeting based on the emotion estimation data. For example, it assigns a high score to users who maintain positive emotions. The credit score calculation unit also analyzes feedback after the meeting and assigns a high score to users who elicit positive emotions. For example, it evaluates satisfaction with the results of the meeting. In this way, by analyzing the emotions of participants after the meeting and evaluating users who elicit positive emotions, the trustworthiness of users is improved.

[0066] The reservation priority determination unit uses AI to analyze a user's past conference room reservation patterns and suggest the optimal reservation timing. The reservation priority determination unit, for example, analyzes the user's past conference room reservation data and suggests the optimal reservation timing. For example, it may make suggestions based on the time periods that the user frequently makes reservations for. The reservation priority determination unit also monitors the usage status of conference rooms in real time and suggests the optimal reservation timing based on availability. For example, it may suggest time periods with many available spaces. The reservation priority determination unit also integrates the user's schedule data and suggests the optimal reservation timing. For example, it may suggest time periods that do not overlap with other plans. In this way, by analyzing past conference room reservation patterns and suggesting the optimal reservation timing, the efficiency of conference room usage is improved.

[0067] The reservation priority determination unit can dynamically adjust the available time of the conference room based on the credit score and provide an optimal usage schedule. The reservation priority determination unit dynamically adjusts the available time of the conference room based on, for example, the user's credit score. For example, it provides available time preferentially to users with high scores. The reservation priority determination unit also monitors the usage status of the conference room in real time and adjusts the available time according to the credit score. For example, it allocates free time to users with high scores. The reservation priority determination unit also provides an optimal usage schedule based on the user's schedule data. For example, it adjusts the schedule so that it does not overlap with other appointments. In this way, the available time of the conference room is dynamically adjusted based on the credit score and an optimal usage schedule is provided, thereby improving user satisfaction.

[0068] The reservation priority determination unit can use the emotion estimation function to suggest the most suitable conference room based on the user's emotional state. The reservation priority determination unit, for example, monitors the user's emotional state in real time and suggests the most suitable conference room. For example, it suggests a conference room that can be used in a relaxed state. The reservation priority determination unit also suggests a conference room according to the user's emotional state based on the emotion estimation data. For example, it suggests a large conference room for a user with strong positive emotions. The reservation priority determination unit also analyzes the user's emotional fluctuations and suggests the most suitable conference room. For example, it suggests a conference room that provides a low-stress environment. In this way, suggesting the most suitable conference room based on the user's emotional state improves user satisfaction.

[0069] The reservation priority determination unit can integrate reservation systems for other office facilities into the prioritized conference room reservation system. For example, the reservation priority determination unit can integrate reservation functions for cafeterias and break rooms into the conference room reservation system, allowing users to make reservations all at once. For example, ensuring break time after a meeting. The reservation priority determination unit also monitors the reservation status of other office facilities in real time and proposes the optimal reservation plan to the user. For example, reserving a meeting and a break consecutively. The reservation priority determination unit also provides the optimal usage schedule for conference rooms and other office facilities based on the user's schedule data. For example, it proposes a break room where users can refresh themselves after a meeting. In this way, integrating reservation systems for other office facilities into the conference room reservation system improves user convenience.

[0070] The reservation priority determination unit can provide an option to customize the layout and facilities of a conference room based on the credit score. The reservation priority determination unit provides an option to customize the layout and facilities of a conference room based on the user's credit score, for example. For example, a special layout is provided for users with a high score. The reservation priority determination unit also monitors the usage status of the conference room in real time and adjusts the layout and facilities according to the credit score. For example, it changes the placement of the projector or whiteboard. The reservation priority determination unit also suggests the optimal layout and facilities based on the user's schedule data. For example, it provides a layout according to the content of the conference. In this way, by providing an option to customize the layout and facilities of a conference room based on the credit score, user satisfaction is improved.

[0071] The reservation priority determination unit can use the emotion estimation function to propose optimal conference room layouts and facilities based on the user's emotional state. The reservation priority determination unit, for example, monitors the user's emotional state in real time and proposes optimal conference room layouts and facilities. For example, it proposes a layout that can be used in a relaxed state. The reservation priority determination unit also proposes layouts and facilities according to the user's emotional state based on the emotion estimation data. For example, it proposes a spacious layout for a user with strong positive emotions. The reservation priority determination unit also analyzes the user's emotional fluctuations and proposes optimal layouts and facilities. For example, it proposes a layout that provides a low-stress environment. In this way, by proposing optimal conference room layouts and facilities based on the user's emotional state, user satisfaction is improved.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The usage scoring unit can score the amount of energy consumed when a conference room is in use and award high scores to eco-friendly users. For example, energy consumption data from a conference room can be collected and analyzed using AI. Meetings that consume less energy can be given a high score. Energy-efficient meetings can also be evaluated. For example, a meeting that proceeds efficiently in a short amount of time can be given a high score. Furthermore, eco-friendly behavior can be scored. For example, behavior such as turning off the power after a meeting is over can be evaluated. In this way, by scoring energy consumption and evaluating eco-friendly users, it is possible to promote environmentally conscious usage.

[0074] The usage scoring unit can analyze audio data when a conference room is in use and score the productivity of the meeting and the engagement of participants. For example, it can collect audio data from within the conference room and analyze it using AI. It evaluates the frequency of comments and the quality of the content to score the productivity of the meeting. It can also evaluate the engagement of participants based on the audio data from the meeting. For example, it can score the number of comments and the number of questions. It can also analyze the audio data from the meeting and score the depth of the discussion and the progress in problem-solving. For example, a meeting that produces concrete solutions can be given a high score. This makes it possible to evaluate the quality of a meeting by scoring its productivity and the engagement of participants.

[0075] The usage scoring unit uses AI to analyze users' behavioral patterns when using conference rooms, and can score their compliance with meeting start and end times. For example, the start and end times of meetings are recorded and analyzed using AI. Meetings that start and end on time are given a high score. The system also scores the frequency of meeting delays and early departures. High scores are awarded to users with minimal delays. Furthermore, the system monitors the progress of meetings in real time, and gives high scores to meetings that proceed as planned. Time allocation for each agenda item is evaluated. This allows time management to be evaluated by scoring compliance with meeting start and end times.

[0076] The usage scoring unit uses the emotion estimation function to analyze the emotions of participants during a meeting, and can assign a high score to meetings where there are many positive emotions. For example, the facial expressions of participants during a meeting are captured by a camera and their emotions are analyzed using AI. Meetings with many smiling faces are assigned a high score. Audio data is also analyzed to assign a high score to meetings with many positive-toned comments. Meetings with many words of praise and gratitude are evaluated. Furthermore, the atmosphere of the meeting is scored based on the emotion estimation data. Meetings where positive emotions are sustained are assigned a high score. In this way, the atmosphere of a meeting can be evaluated by analyzing the emotions of participants during a meeting and assigning a high score to meetings where there are many positive emotions.

[0077] The utilization scoring unit can take into account the user's usage of other office equipment in the utilization score of a conference room. For example, it can record the frequency of use of the projector or whiteboard in the conference room and reflect this in the score. High scores are given to users who use them frequently. It also monitors the usage of other office equipment to evaluate the quality of meetings. Meetings that make effective use of the projector are given a high score. Furthermore, it integrates equipment usage data within the conference room to calculate an overall utilization score. Meetings that make balanced use of multiple pieces of equipment are given a high score. In this way, by taking into account the usage of other office equipment in the utilization score of a conference room, it is possible to evaluate overall utilization.

[0078] The usage scoring unit can score the amount of energy consumed when using a conference room and award high scores to eco-friendly users. For example, energy consumption data for a conference room can be collected and analyzed using AI. Meetings that consume less energy can be given a high score. Energy-efficient meetings can also be evaluated. Meetings that proceed efficiently in a short amount of time can be given a high score. Furthermore, eco-friendly behavior can be scored, such as turning off the power after a meeting has ended. In this way, by scoring energy consumption and evaluating eco-friendly users, it is possible to promote environmentally friendly usage.

[0079] The usage scoring unit uses the emotion estimation function to collect feedback after conference room use and can assign high scores to users who provide a lot of positive feedback. For example, feedback is collected from participants after a conference has ended and emotions are analyzed using AI. Conferences with a lot of positive feedback are assigned a high score. The content of the feedback is also analyzed and high scores are assigned to users with a lot of positive opinions. Praise for the progress and content of the conference is evaluated. Furthermore, the quality of the feedback is scored based on the emotion estimation data. Conferences with few specific suggestions for improvement are assigned a high score. In this way, user satisfaction can be improved by collecting feedback after conference room use and evaluating users with a lot of positive feedback.

[0080] The credit score calculation unit can integrate a user's past conference room usage history with other business performance data to calculate an overall credit score. For example, the user's conference room usage history is integrated with business performance data and analyzed using AI. A high score is awarded to users with high business performance. Additionally, past conference room usage data is associated with business results to calculate an overall credit score. Users whose meeting results are reflected in their work are highly rated. Furthermore, the effectiveness of conference room usage is evaluated based on business performance data. A high score is awarded to users whose work efficiency has improved after a meeting. In this way, a user's reliability can be evaluated by integrating past conference room usage history with business performance data to calculate an overall credit score.

[0081] The trust score calculation unit can analyze a user's network activity when using a conference room and assign a score to determine whether the user is using the internet appropriately. For example, network activity data when using a conference room is collected and analyzed using AI. A high score is assigned to users who use the internet appropriately. The content of network activity is also evaluated, and users who frequently use the internet for work-related purposes are assigned a high score. Users who use work-related sites and tools are evaluated. Furthermore, the appropriateness of network activity is scored. A high score is assigned to users who do not browse unrelated sites during a meeting. This improves the user's credibility by analyzing network activity when using a conference room and assessing whether the user is using the internet appropriately.

[0082] The credit score calculation unit uses the emotion estimation function to analyze the emotional fluctuations of participants during a meeting and assign a high credit score to users who maintain stable emotions. For example, it monitors the emotional fluctuations of participants during a meeting in real time and assigns a high score to users who maintain stable emotions. Users with little emotional fluctuation are evaluated. In addition, based on the emotion estimation data, the stability of emotions during a meeting is scored. High scores are assigned to users who maintain positive emotions. Furthermore, it analyzes emotional fluctuations during a meeting and assigns high scores to users who experience little stress or tension. Users who conduct the meeting in a relaxed state are evaluated. In this way, trust is improved by analyzing the emotional fluctuations of participants during a meeting and evaluating users who maintain stable emotions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The usage scoring unit scores the user's usage of the conference room. For example, it records the time spent using the conference room and gives a high score to users who spend a long time using the conference room. It also records the frequency of conference room use and gives a high score to users who use the conference room frequently. It also records the reservation cancellation rate and gives a high score to users who cancel less. Step 2: The credit score calculation unit calculates a credit score based on the usage scored by the usage scoring unit. For example, the credit score of the user is calculated in the range of 0 to 100 based on the usage score. The credit score of the user can also be calculated as a rating from A to F based on the usage score. Furthermore, the credit score of the user can also be calculated as a percentile based on the usage score. Step 3: The reservation priority determination unit determines the priority of users when reserving a conference room based on their credit scores. For example, users with high credit scores can be given priority in reserving conference rooms. It is also possible to give priority to users with high credit scores for reservations in earlier time slots. Furthermore, it is also possible to give priority to specific conference rooms for users with high credit scores.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a utilization score calculation unit that calculates a score for the utilization of the conference room by the user; a credit score calculation unit that calculates a credit score based on the usage score calculated by the usage scoring unit; a reservation priority determination unit that determines the priority of users when reserving a conference room next time based on the credit score. A system characterized by:

2. The utilization scoring unit Analyzes audio data from conference rooms to score meeting productivity and participant engagement 2. The system of claim 1.

3. The utilization scoring unit AI is used to analyze the user's behavioral patterns when using the conference room, and scores are generated based on the user's adherence to meeting start and end times.

2. The system of claim 1.

4. The utilization scoring unit Analyze the emotions of participants during the meeting and give a high score to meetings with a high percentage of positive emotions.

2. The system of claim 1.

5. The utilization scoring unit The utilization score of the conference room is calculated by taking into account the utilization status of other office facilities of the user.

2. The system of claim 1.

6. The utilization scoring unit Energy consumption during conference room use is scored, and eco-friendly users are given a high score.

2. The system of claim 1.

7. The utilization scoring unit Feedback is collected after using the conference room, and a high score is given to the user who provides a lot of positive feedback.

2. The system of claim 1.

8. The credit score calculation unit The user's past conference room usage history and other business performance data are integrated to calculate the overall credit score.

2. The system of claim 1.

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