system

Abstract

A system is provided.SOLUTION: This system is provided with a means for acquiring the schedule information of an employee, a means for acquiring the using situation of an office, a means for selecting optimal BGM based on a time zone, the using situation of a space, the attribute of a participant and the purpose of an event, and a means for distributing the selected BGM to each space in the office.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With the spread of telecommuting, office work environments are being reassessed, evolving from mere workplaces to spaces that foster employee communication and creativity. For this reason, the impact that the office sound environment has on employees' concentration and relaxation is becoming increasingly important. However, setting up background music in the office today is time-consuming, and it is difficult to select optimal background music and adjust the volume based on factors such as the time of day, space usage, and user attributes. Furthermore, there is a lack of methods for incorporating employee feedback to optimize background music. It is necessary to solve these issues and provide a more comfortable office environment. [Means for solving the problem]

[0005] This invention provides a system that includes a means for acquiring employee schedule information, a means for acquiring office usage status, a means for selecting optimal background music based on the time of day, space usage status, participant attributes, and the purpose of the event, and a means for distributing the selected background music to each space in the office. Furthermore, by including a means for automatically adjusting the volume of the selected background music and a means for collecting user feedback and optimizing the background music selection algorithm based on that feedback, the system can optimize the office environment in real time, improving employee concentration and relaxation.

[0006] "Employee schedule information" is information including schedules and schedule details recorded on calendars and schedule management tools used by employees to manage their schedules.

[0007] "Office usage status" refers to information about the current usage status of each space in the office, the degree of congestion, and the users who are using that space.

[0008] A "time period" refers to a specific time or range of times within a day, including time segments such as morning, afternoon, and after work.

[0009] "Space usage status" is information that indicates how a particular space in the office is being used, for example, whether it is in a meeting, on a break, or by how many people are using it.

[0010] "Participant attributes" is information including characteristics such as department, job title, and age of users participating in a particular office space or event.

[0011] "Event purpose" is information indicating the purpose of an activity that will take place during a specific time period or space, such as a meeting, work, or break.

[0012] "Optimal background music" is music or sounds that are determined to be most suitable for the particular situation in the office based on the time of day, space usage, participant attributes, and the purpose of the event.

[0013] "Means for distributing background music" refers to the technical means or methods for playing the selected background music through the audio systems installed in each space within the office.

[0014] "Means for automatically adjusting the volume" refers to technical means for automatically setting and adjusting the volume of selected background music based on real-time data.

[0015] "Feedback" refers to information such as opinions, ratings, and impressions provided by users regarding background music, its volume, and song selection.

[0016] "BGM selection algorithm" refers to a logical procedure or calculation method that uses real-time data and feedback to select the most suitable background music for an office environment. [Brief explanation of the drawings]

[0017] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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, a 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), and an APU (Accelerated Processing Unit).

[0021] 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.

[0022] 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.

[0023] 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), Bluetooth (registered trademark), etc.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0029] 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.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The system of the present invention, "Comfortable Office BGM Maker," collects employee schedule information and office usage status in real time, selects the most appropriate background music based on the time of day, space usage status, participant attributes, and the purpose of the event, and distributes it to each space, providing a comfortable office environment.

[0039] System Overview

[0040] The system mainly consists of the following elements:

[0041] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[0042] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[0043] 3. BGM selection method: The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, usage, participant attributes, and the purpose of the event.

[0044] 4. BGM distribution method: The server distributes the selected background music to the audio system in each space in the office and begins playback.

[0045] 5. Volume adjustment method: The server automatically adjusts the volume based on the environment and user feedback.

[0046] 6. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[0047] Program processing flow

[0048] Data collection

[0049] The server obtains employee schedule information via the company's internal calendar API and collects real-time usage data from sensors and IoT devices within the office.

[0050] Data analysis

[0051] The server determines the time period based on the acquired data and analyzes the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and purpose of the event (e.g., meeting, break, work).

[0052] BGM selection

[0053] The server runs an algorithm to select the optimal background music based on the analysis results, and selects background music that is appropriate for the space in use.

[0054] BGM playback

[0055] The selected background music is distributed from the server to the audio system in each space and played automatically, with the server automatically adjusting the volume based on the data to set it to the optimal level.

[0056] Feedback collection and optimization

[0057] Users can submit feedback on the background music from their devices, and the server collects this feedback and feeds the analysis results back into the algorithm for optimization.

[0058] Specific examples

[0059] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0060] 1. Data Collection

[0061] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[0062] The server checks the usage sensor of conference room A to see if the conference room is reserved.

[0063] 2. Data Analysis

[0064] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[0065] 3. BGM selection

[0066] The server determines that quiet jazz music is appropriate for this situation and selects it.

[0067] 4. BGM playback

[0068] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to a level that allows people to concentrate on the meeting.

[0069] 5. Gather feedback and optimize

[0070] After the meeting, users (conference participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[0071] In this way, the Comfortable Office BGM Maker provides optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[0072] The processing flow will be explained below.

[0073] Step 1:

[0074] The server retrieves each employee's calendar information through the API. This information includes details of each employee's schedule and individual schedule. For example, it confirms that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[0075] Step 2:

[0076] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that the room has been reserved.

[0077] Step 3:

[0078] The server analyzes the retrieved calendar information and usage data to determine the current time zone, for example, confirming that it is "3:00 PM."

[0079] Step 4:

[0080] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "it is being used by a member of the sales department."

[0081] Step 5:

[0082] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[0083] Step 6:

[0084] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[0085] Step 7:

[0086] The server runs an algorithm to select the best background music based on the time of day, space availability, participant attributes, and the purpose of the event. For example, it might select "quiet jazz music" as the best choice.

[0087] Step 8:

[0088] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[0089] Step 9:

[0090] The server automatically adjusts the volume of background music based on the environment and user feedback, for example, setting it to a volume level that makes it easy to concentrate in a meeting environment.

[0091] Step 10:

[0092] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[0093] Step 11:

[0094] The server collects user feedback and optimizes the background music selection algorithm based on it, for example, adjusting the list of music candidates for the next selection based on the feedback results.

[0095] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules and office usage, optimizing the office environment in real time.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] In conventional office environments, there was no system that could select optimal background music (BGM) in real time based on employees' schedules and office usage, and deliver it at an appropriate volume. This made it difficult to improve employees' concentration and achieve a relaxing effect. Another issue was that the selected BGM was not evaluated to see if it was optimal for employees, and the system was not optimized to reflect that feedback.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage information, a means for analyzing the information acquired by the data acquisition means, a means for selecting optimal music based on the analysis results, and a means for distributing the selected music to each space in the office. This makes it possible to automatically select optimal background music in real time based on employee schedules and office usage and distribute it at an appropriate volume. Furthermore, by collecting feedback from users and optimizing the algorithm based on that feedback, a system can be realized that can continuously provide optimal background music.

[0101] "Means for obtaining employee schedule information" refers to a function that automatically collects employee schedule data using the API of a planning management tool.

[0102] "Means of obtaining office usage status" refers to a function that uses sensors and IoT devices to monitor and obtain the usage status of each space in real time.

[0103] The "means for analyzing information obtained by the data obtaining means" is a function for analyzing the obtained schedule information and usage status data and performing data processing corresponding to a specific time period or situation.

[0104] "Means for selecting optimal music based on analysis results" refers to an algorithm that automatically selects the background music that is most suitable for specific conditions based on the results of data analysis.

[0105] "Means for distributing selected music to each space within the office" refers to the function of transmitting selected background music to the audio systems installed in each space in the office and playing it back.

[0106] "Means for automatically adjusting volume" refers to a function that automatically adjusts the volume of selected background music to an optimal level based on the environment and user feedback.

[0107] "Means for collecting feedback from users and optimizing the music selection algorithm based on that feedback" refers to a function that collects opinions and impressions from users, updates the music selection algorithm based on that data, and improves its accuracy.

[0108] The system of this invention, "Comfortable Office BGM Maker," is a system that provides a comfortable office environment by acquiring employee schedule information and office usage status in real time, and selecting and distributing the most suitable background music.

[0109] System configuration

[0110] The system mainly consists of the following components:

[0111] 1. A way to obtain employee schedule information

[0112] 2. How to obtain office usage information

[0113] 3. Means for analyzing information obtained by data acquisition means

[0114] 4. A method for selecting optimal music based on analysis results

[0115] 5. A means of distributing selected music to each space in the office

[0116] 6. Automatic volume control

[0117] 7. A means of collecting user feedback and optimizing the music selection algorithm based on that feedback

[0118] Hardware and software used

[0119] Hardware:

[0120] Sensors and IoT devices installed in the office

[0121] Audio systems installed in each space (e.g., smart speakers)

[0122] software:

[0123] API for obtaining employee schedule information (e.g., calendar API)

[0124] A platform for acquiring data from IoT devices (e.g. IoT Hub)

[0125] Data analysis programs (e.g., Python, pandas library)

[0126] BGM selection algorithm (e.g., scikit-learn)

[0127] Music streaming service API (e.g., music service API)

[0128] Programs for collecting and analyzing feedback (e.g., TensorFlow, PyTorch)

[0129] Processing flow

[0130] Data collection

[0131] The server accesses an external scheduling tool (e.g., a calendar API) to retrieve employee schedule data. This information is returned in JSON format, which the server stores in a database. The server also collects real-time usage data from sensors and IoT devices (e.g., IoT Hub) installed in the office.

[0132] Data analysis

[0133] The server analyzes the acquired schedule information and usage data using data science tools (e.g., Python's pandas library). Based on the analysis results, data processing is performed according to specific time periods and situations.

[0134] BGM selection

[0135] The server then runs an algorithm to select the optimal background music based on the analysis results. This selection uses machine learning libraries (e.g., scikit-learn) and takes into account past feedback data. The algorithm selects the optimal music based on conditions (e.g., time of day, usage, participant attributes, and event purpose).

[0136] BGM playback

[0137] The server distributes the selected background music to each space in the office. For example, a music service API is used to distribute music played through a smart speaker. The server automatically adjusts the volume to the optimal level.

[0138] Gathering feedback and optimizing the algorithm

[0139] Users can submit feedback about the background music via a dedicated app or web portal from their device (smartphone or PC). This feedback data is collected and analyzed by the server, and the background music selection algorithm is optimized using a machine learning model (e.g., TensorFlow or PyTorch).

[0140] Specific examples

[0141] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0142] 1. Data Collection

[0143] The server uses the calendar API to retrieve information that a new product planning meeting is scheduled for 3:00 PM to 4:00 PM in conference room A.

[0144] The server checks the usage status of conference room A through the IoT device.

[0145] 2. Data Analysis

[0146] The server analyzes the time of day, participant attributes, purpose of the event, etc.

[0147] 3. BGM selection

[0148] Based on the analysis results, the server determines that quiet jazz music is appropriate and selects it.

[0149] 4. BGM playback

[0150] At 3:00 PM, the server will stream jazz music to the audio system in conference room A and automatically start playing it, adjusting the volume to a level appropriate for the meeting.

[0151] 5. Gathering feedback and optimizing the algorithm

[0152] After the meeting, users (conference participants) send feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[0153] Prompt Sentence Examples

[0154] "The sales department will hold a new product planning meeting in Conference Room A from 3:00 PM to 4:00 PM. Please select background music appropriate for this situation and develop an algorithm to optimally adjust the volume when playing it."

[0155] As described above, the Comfortable Office BGM Maker is a system that selects and distributes optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[0156] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0157] Step 1: Obtain employee schedule information

[0158] Input: The server takes as input an authentication token to retrieve employee schedule information via the calendar API.

[0159] How it works: The server sends an HTTP GET request to an API endpoint to retrieve event information. Specifically, it uses the requests library to send a request to https: / / api.calendarservice.com / v3 / calendars / primary / events.

[0160] Output: The retrieved schedule information is returned in JSON format, which is parsed and stored in an internal database.

[0161] Step 2: Get Office usage

[0162] Input: The server takes the IoT device's connection string and authentication information as input.

[0163] How it works: The server collects data from Microsoft Azure IoT Hub and other IoT platforms, specifically by asynchronously fetching data from devices using the asyncio and azure-iot-device libraries.

[0164] Output: The captured usage data is stored in an internal database as real-time space utilization.

[0165] Step 3: Data analysis

[0166] Input: The server inputs the employee schedule information and usage data obtained in steps 1 and 2.

[0167] How it works: The server uses the pandas library to store information in a data frame and analyzes things like time of day, usage, attendee demographics, and event purpose.

[0168] Output: The analysis results are stored in a database as information for a specific time period and situation.

[0169] Step 4: Select background music

[0170] Input: The server takes the result of the analysis in step 3 as input.

[0171] How it works: The server uses a machine learning library (e.g., scikit-learn) to run an algorithm to select the optimal background music, taking into account past feedback data.

[0172] Output: The selected background music is output as a music URL obtained using a music service API (e.g., Spotify's API).

[0173] Step 5: Play background music

[0174] Input: The server takes as input the music URL of the background music selected in step 4.

[0175] How it works: The server sends a music URL via HTTP POST request to each space's audio system (e.g., smart speaker), and also adjusts the volume to set an appropriate volume level for the environment.

[0176] Output: Music is played in the designated space, with the volume adjusted to an optimal level.

[0177] Step 6: Gather feedback and optimize

[0178] Input: Users enter feedback from their devices through a dedicated app or web portal.

[0179] How it works: The server receives the feedback data in JSON format and stores it in a database. It then optimizes the algorithm using a machine learning model (e.g., TensorFlow or PyTorch).

[0180] Output: The updated algorithm will be reflected in the next BGM selection.

[0181] (Application example 1)

[0182] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0183] In conventional automobiles, there was no mechanism to provide optimal background music according to the passenger's mood or in-car environment. Furthermore, there was a lack of systems that could meet the specific needs of passengers, such as relaxation and improved concentration. As a result, it was difficult to ensure a comfortable ride. The present invention aims to provide passengers with a comfortable riding experience by selecting optimal background music based on passenger schedule information and in-car environment information.

[0184] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0185] In this invention, the server includes means for acquiring employee schedule information, means for acquiring the usage status of the work space, means for selecting optimal background music based on the time of day, the usage status of the space, the attributes of the participants, and the purpose of the event, means for distributing the selected background music to each area in the work space, means for acquiring in-vehicle environment information, means for selecting optimal background music based on the time of day, the in-vehicle environment information, and the passengers' schedule information and mood, and means for distributing the selected background music to the in-vehicle sound system. This allows the server to always provide optimal background music based on various information, making it possible to provide passengers with a comfortable riding experience and a comfortable working environment in the work space.

[0186] The "means for acquiring employee schedule information" is a means for acquiring schedule information from a schedule management system for an office or a vehicle's passengers.

[0187] "Means for obtaining information on the usage status of business spaces" refers to means for obtaining information on the usage status of each area within a business space in real time using sensors and IoT devices.

[0188] "Means for selecting optimal background music based on the time of day, space usage, participant attributes, and event purpose" refers to a means for analyzing specified data and selecting the most suitable background music using an algorithm.

[0189] The "means for distributing the selected background music to each area within the business space" refers to a means for distributing the selected background music to the sound system in each area within the business space and starting playback.

[0190] The "means for acquiring in-vehicle environmental information" is a means for acquiring environmental data such as temperature, humidity, and number of passengers using sensors installed inside the vehicle.

[0191] "Means for selecting optimal background music based on the time of day, in-car environment information, passenger schedule information, and mood" refers to a means for analyzing this information and selecting the most suitable background music using an algorithm.

[0192] The "means for distributing the selected background music to the sound system in the vehicle" refers to a means for distributing the selected background music to the sound system in the vehicle and starting playback.

[0193] The "means for automatically adjusting the volume of selected background music" is a means for automatically adjusting the volume of background music to an appropriate level based on ambient environmental sounds and feedback.

[0194] "Means for collecting feedback from users and optimizing the background music selection algorithm based on that feedback" refers to means for collecting feedback from users and optimizing the algorithm by reflecting the analysis results in the algorithm.

[0195] This invention is a system for providing a comfortable environment in autonomous vehicles and work spaces, and aims to select optimal background music based on employee schedule information, usage status, and in-vehicle environmental information, and distribute it at the appropriate time. This system is mainly composed of a server, sensors, an audio system, and a feedback terminal.

[0196] Overall system overview

[0197] 1. How to obtain employee schedule information

[0198] The server obtains schedule information from the employee or passenger calendar API. For example, it uses the Google Calendar API to obtain schedule information for employees and passengers.

[0199] 2. Means of obtaining information on the usage status of work spaces or vehicles

[0200] The server uses sensors (temperature sensors, humidity sensors, motion sensors, etc.) installed in the work space and inside the vehicle to obtain real-time usage data for each area or the vehicle environment, using IoT devices and sensor APIs.

[0201] 3. BGM selection method

[0202] The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. For example, if passengers are on their way to work in the morning, it will select relaxing background music.

[0203] 4. Means of distributing the selected background music

[0204] The server distributes the selected background music to the sound systems (e.g., Bluetooth speakers) in each area of ​​the work space or vehicle, and issues instructions to automatically start playback. The selected background music is distributed using a music streaming API (e.g., Spotify API).

[0205] 5. Automatic BGM volume adjustment method

[0206] The server automatically adjusts the volume of the background music to an appropriate level based on the noise level in the vehicle or work space and user feedback.

[0207] 6. Feedback collection and optimization measures

[0208] Users submit feedback on the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the algorithm for optimization.

[0209] Specific application examples

[0210] Commuting time (9:00 AM): With the temperature inside the car at 25°C, relaxing background music is selected based on the passenger's schedule and played on the car's sound system. If the feedback indicates that the music was relaxing, it will be reflected in future background music selections.

[0211] Prompt Sentence Examples

[0212] Based on in-car environmental data (temperature, humidity, number of passengers) while driving and passenger calendar information (commencement time, important meetings, etc.), a program is created to select the most suitable background music and play it in the car.

[0213] This system will allow passengers and employees to always enjoy the optimal music environment while riding or working, improving comfort and allowing for an even more personalized music environment by improving the background music selection algorithm based on collected feedback.

[0214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0215] Step 1:

[0216] Data collection

[0217] The server obtains schedule information using the employee or passenger calendar API. Specifically, it uses the Google Calendar API to obtain each user's schedule information and stores it internally in the system. The server also obtains real-time environmental data (temperature, humidity, number of passengers) through the work space or in-vehicle sensor API. The input for this data collection is calendar information and sensor information, and the output is a dataset that compiles this data.

[0218] Step 2:

[0219] Data analysis

[0220] The server analyzes the acquired schedule information and environmental data. The analyzed data identifies the time of day, space usage, participant attributes, and event purpose. For example, if the current time is 9:00 AM, it determines that the person is "on their way to work" and checks the environmental data to see that the temperature inside the car is 25 degrees. The input to this analysis process is the dataset obtained from data collection, and the output is the analysis result (e.g., "on their way to work, temperature 25 degrees").

[0221] Step 3:

[0222] BGM selection

[0223] The server runs an algorithm to select optimal background music based on the analysis results. For example, if you are on your way to work and the temperature is 25 degrees, it will select relaxing background music. The input to this selection process is the analysis results, and the output is information about the selected background music (e.g., a relaxing playlist).

[0224] Step 4:

[0225] BGM distribution

[0226] The server distributes the selected background music to the sound system in the work space or vehicle. Specifically, it uses a music streaming API (e.g., Spotify API) to distribute the selected background music to the sound system (Bluetooth speaker, etc.) in each area, and playback begins automatically. The input for this distribution is the selected background music information, and the output is the background music played on each sound system.

[0227] Step 5:

[0228] Volume adjustment

[0229] The server automatically adjusts the volume of the background music being played. It sets the optimal volume based on the noise level in the car or work space and user feedback. Specifically, it obtains environmental noise data from sensors and automatically adjusts the volume to an appropriate level based on that data. The inputs for this volume adjustment are the noise level and feedback information, and the output is the adjusted volume.

[0230] Step 6:

[0231] Feedback collection and optimization

[0232] Users submit feedback about the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the background music selection algorithm for optimization. Specifically, the feedback data is analyzed and reflected in future background music selections. The input for this feedback collection and optimization is user feedback, and the output is an optimized algorithm.

[0233] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0234] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[0235] System Overview

[0236] The system mainly consists of the following elements:

[0237] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[0238] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[0239] 3. Emotion engine: The server analyzes the user's facial expressions and voice from sensors installed on the terminal and in each space, and recognizes their emotions.

[0240] 4. BGM Selection: The server runs an algorithm to select the optimal BGM based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions as recognized by the emotion engine.

[0241] 5. BGM distribution method: The server distributes the selected background music to the audio systems installed in each space within the office and begins playback.

[0242] 6. Volume adjustment method: The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine.

[0243] 7. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[0244] Program processing flow

[0245] Data collection

[0246] The server obtains employee schedule information via the company calendar API, acquires real-time usage data from sensors and IoT devices in the office, and uses an emotion engine to recognize emotions from users' facial expressions and voices and collects them as data.

[0247] Data analysis

[0248] The server analyzes the various data it acquires to determine the current time period, including the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and event purpose (e.g., meeting, break, work). It also analyzes user emotion data recognized by an emotion engine.

[0249] BGM selection

[0250] The server runs an algorithm to select the optimal background music based on the analysis results and the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, music with a high relaxing effect will be selected.

[0251] BGM playback

[0252] The selected background music is distributed from the server to the audio system in each space and played automatically. The server automatically adjusts the volume based on real-time data to provide the optimal sound environment.

[0253] Feedback collection and optimization

[0254] Users can submit feedback about the background music from their devices (smartphones, PCs, etc.) The server collects this feedback and combines it with data from the emotion engine to optimize the algorithm.

[0255] Specific examples

[0256] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0257] 1. Data Collection

[0258] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[0259] The occupancy sensor in conference room A confirms that the conference room is reserved.

[0260] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[0261] 2. Data Analysis

[0262] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[0263] The emotion engine recognizes that the user is a little nervous.

[0264] 3. BGM selection

[0265] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[0266] 4. BGM playback

[0267] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[0268] 5. Gather feedback and optimize

[0269] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[0270] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[0271] The processing flow will be explained below.

[0272] Step 1:

[0273] The server retrieves each employee's calendar information via the API, including details of the employee's schedule and individual schedule, and confirms, for example, that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[0274] Step 2:

[0275] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that the room has been reserved.

[0276] Step 3:

[0277] The server collects the user's facial expressions and voice from sensors such as cameras and microphones installed on the device and in each space, and uses an emotion engine to recognize the user's emotions. For example, it can recognize a "tense facial expression" from camera footage and analyze the user's voice to determine that the user has a "tense tone of voice."

[0278] Step 4:

[0279] The server analyzes the acquired calendar information, usage data, and emotion data to identify the current time zone. For example, it verifies that it is "3:00 PM" and that it is the start time of the meeting.

[0280] Step 5:

[0281] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "the user is a member of the sales department."

[0282] Step 6:

[0283] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[0284] Step 7:

[0285] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[0286] Step 8:

[0287] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as emotional data recognized by the emotion engine. For example, it might determine that "quiet jazz music that enhances concentration and relaxation" is optimal.

[0288] Step 9:

[0289] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[0290] Step 10:

[0291] The server automatically adjusts the volume of background music based on real-time data, for example, adjusting the volume to a level that allows for concentration during meetings and setting an optimal volume level to "relieve tense emotions."

[0292] Step 11:

[0293] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[0294] Step 12:

[0295] The server collects user feedback, analyzes it together with the emotion engine data, and optimizes the background music selection algorithm. For example, it adjusts the list of music candidates for the next selection based on feedback such as "whether the user felt relieved."

[0296] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules, office usage, and user emotions, optimizing the office environment in real time.

[0297] Example 2

[0298] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0299] In modern office environments, optimizing the work environment is important to increase employee productivity and comfort. However, conventional background music systems are limited in their ability to create an effective environment because they are unable to select and play optimal music based on the user's schedule or emotional state. Furthermore, optimization through BGM volume adjustment and feedback collection is also insufficient. Therefore, there is a need for a system that can provide optimal BGM and adjust it in real time based on the office situation and the user's emotional state.

[0300] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage status, a means for recognizing user emotions, a means for selecting optimal background music based on the time period, space usage status, participant attributes, event purpose, and user emotions, a means for distributing the selected background music to each space in the office, a means for automatically adjusting the volume of the selected background music, and a means for collecting user feedback and optimizing the background music selection algorithm based on the feedback and user emotion data. This makes it possible to provide optimal background music based on various data in the office environment and continuously adjust the volume in real time.

[0301] "Employee schedule information" is data about the time and location of meetings, tasks, events, etc. scheduled by employees.

[0302] "Office usage status" refers to data such as the utilization rate, occupancy status, and availability of each space within the office.

[0303] "Means for recognizing user emotions" refers to technology that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice and identify their emotional state.

[0304] "Time zone" refers to a specific time period of the day that the system is classified into based on the current time.

[0305] "Space usage" is data that shows the current usage of each space within the office, such as conference rooms and work areas.

[0306] "Participant attributes" refers to information such as the department and position of employees who participate in events or work in the office.

[0307] "Purpose of the event" refers to the purpose of various activities that take place in the office, such as meetings, work, and breaks.

[0308] "Means for selecting optimal background music" refers to algorithms or mechanisms that select the most appropriate background music for each situation based on collected data and analysis results.

[0309] "Means for distributing selected background music to each space within the office" refers to technology that allows the server to send the selected background music to the audio system in each space and begin playback.

[0310] The "means for automatically adjusting the volume of the selected background music" is a mechanism for automatically adjusting the volume of the background music being played based on environmental noise and user feedback.

[0311] "Means for collecting feedback from users" refers to an interface or system that allows users to send their opinions and ratings about background music from their devices.

[0312] "Means to optimize the BGM selection algorithm" refers to a technology that analyzes collected user feedback and emotional data and improves the algorithm to increase the accuracy of future BGM selection.

[0313] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[0314] System Overview

[0315] The system mainly consists of the following elements:

[0316] 1. How to obtain employee schedule information

[0317] The server retrieves schedule information from employee calendars and schedule management tools via APIs, such as Google Calendar API.

[0318] 2. Means of obtaining office usage status

[0319] The server collects usage data for each space from sensors and IoT devices installed in the office, including conference room reservation systems and seat occupancy sensors.

[0320] 3. Emotion Engine

[0321] The server analyzes the user's facial expressions and voice from sensors installed on the device and in each space to recognize emotions. A specific implementation example would be to use an emotion recognition API such as Microsoft Azure Emotion API.

[0322] 4. BGM selection method

[0323] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions, as determined by an emotion engine. For example, if the user is feeling stressed, music with a relaxing effect will be selected.

[0324] 5. BGM distribution methods

[0325] The server distributes the selected background music to the audio systems installed in each space in the office and starts playback, sending a signal to the audio systems via the network.

[0326] 6. Volume adjustment means

[0327] The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine. It uses data from sound environment sensors to optimize the volume in real time.

[0328] 7. Feedback Collection Methods

[0329] Users submit feedback about the background music from their devices, and the server analyzes this feedback to optimize the algorithm. This is done using a feedback interface via devices such as smartphones and PCs.

[0330] Specific examples

[0331] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0332] 1. Data Collection

[0333] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[0334] The occupancy sensor in conference room A confirms that the conference room is reserved.

[0335] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[0336] 2. Data Analysis

[0337] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[0338] The emotion engine recognizes that the user is a little nervous.

[0339] 3. BGM selection

[0340] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[0341] 4. BGM playback

[0342] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[0343] 5. Gather feedback and optimize

[0344] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[0345] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[0346] Examples of prompt statements

[0347] "Please explain in detail how the system recognizes the emotions of individual users based on their schedules and office usage, and selects and plays the most appropriate background music."

[0348] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0349] Step 1: Data collection

[0350] Obtaining employee schedule information

[0351] The server calls a calendar API (e.g., Google Calendar API) to retrieve employee schedule information. The input data includes employee schedule information (e.g., meeting dates and times, attendee list) from the calendar API. The output data is stored in the system. This data is used in subsequent processing steps.

[0352] Acquisition of office usage status

[0353] The server collects usage data from sensors and IoT devices in the office.,Input data includes real-time data from sensors (e.g.,,conference room occupancy, seat usage).,Output data is stored in the system.

[0354] Obtaining user emotion data

[0355] The server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's facial and voice data and recognize their emotions. Input data includes facial and voice data captured by a camera and microphone. The output is the analyzed emotional data stored within the system.

[0356] Step 2: Data analysis

[0357] Identifying the time period

[0358] The server checks the current system time and determines the current time zone. The system time is used as input. The output is the determined time zone (e.g., 3:00 PM) and is stored in the system.

[0359] Analyzing space usage

[0360] The server analyzes the collected sensor data and identifies the usage status of each space. Office usage data is used as input data. Information on occupied and vacant spaces is stored in the system as output.

[0361] Analysis of attendee demographics and event objectives

[0362] The server analyzes the acquired schedule information to determine the attributes of participants and the purpose of the event. The schedule information is used as input data. As output, the attributes of participants (e.g., sales department, engineering department) and the purpose of the event (e.g., meeting, work) are saved in the system.

[0363] Emotional Data Analysis

[0364] The server analyzes the recognized emotion data and identifies the user's emotional state. The acquired emotion data is used as input data. As output, the analyzed user's emotional state (e.g., tension, relaxation) is stored in the system.

[0365] Step 3: Select background music

[0366] Execution of the BGM selection algorithm

[0367] The server runs an algorithm to select the optimal background music based on the analysis results and emotional data. Input data includes the time of day, space usage, participant attributes, event purpose, and emotional data. The selected background music is saved in the system as output. For example, music with a high relaxing effect is selected for a user who is feeling stressed.

[0368] Step 4: Play background music

[0369] BGM distribution

[0370] The server distributes the selected background music to the audio system in each space. The selected background music is used as input data. The background music sent to the audio system is played as output. Specifically, jazz music is distributed to conference room A at 3:00 PM.

[0371] Volume adjustment

[0372] The server automatically adjusts the volume based on real-time data. Environmental noise data and user feedback are used as input data. The output is background music adjusted to an appropriate volume. For example, the server detects the environmental noise in conference room A and adjusts the volume accordingly.

[0373] Step 5: Gather feedback and optimize

[0374] Collecting feedback

[0375] Users submit feedback about the background music from their devices (smartphones or PCs). The user's feedback information is used as input data. The collected feedback information is saved in the system as output. Specifically, the user submits feedback such as "The background music is a little loud."

[0376] Algorithm optimization

[0377] The server analyzes the collected feedback and emotion data and optimizes the background music selection algorithm. The feedback information and emotion data are used as input data. The optimized algorithm is reflected in the system as output. This will result in more appropriate background music selection from the next time onwards.

[0378] (Application example 2)

[0379] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0380] Conventional background music systems in factories were unable to provide appropriate background music that matched the emotions and working conditions of workers. As a result, effects such as improved work efficiency and reduced employee stress could not be expected. Furthermore, they lacked a mechanism for collecting feedback and optimizing the background music selection algorithm. There was a need to solve these problems and provide a more comfortable and efficient working environment.

[0381] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0382] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office (factory) usage status, a means for selecting optimal background music based on the time period, space usage status, participant attributes, and event purpose, a means for distributing the selected background music to each space in the office (factory), a means for acquiring and analyzing worker emotion data, and a means for selecting and distributing optimal background music for each work area in real time. This makes it possible to select and distribute background music suitable for each work area and worker in real time.

[0383] "Employee schedule information" refers to information recorded in calendars or schedule management tools that employees use to record their daily work and schedules.

[0384] "Office usage status" is data that indicates the usage status and occupancy information of each space during a specific time period.

[0385] "Time period" refers to a specific time period within a day, and is one of the criteria the system uses to select background music.

[0386] "Space usage status" refers to information that indicates the current usage status of physical locations such as conference rooms and work areas.

[0387] "Participant attributes" refers to basic information such as job title, department, and age of people attending a particular event.

[0388] The "purpose of the event" is information indicating the intention of an activity or event that takes place during a specific time period, such as a meeting, training session, or break.

[0389] "Optimal background music" is music selected to maximize work efficiency and relaxation in a specific environment or situation.

[0390] "Worker emotion data" is information that indicates the emotional state of a worker, obtained using facial expression recognition, voice analysis, etc.

[0391] "Means for selecting and delivering optimal background music for each work area in real time" refers to methods and technologies for instantly providing optimal background music to each work area based on fluctuations in the work environment and data on workers' emotions.

[0392] MODE FOR CARRYING OUT THE INVENTION

[0393] The system of this invention, "Comfortable Factory BGM Maker," selects and distributes optimal background music in real time based on worker schedule information, factory usage status, and worker emotional data in order to optimize the work environment within a factory.

[0394] System Overview

[0395] The system mainly consists of the following elements:

[0396] 1. Schedule information acquisition means: The server acquires the schedule information of workers through a schedule management tool or calendar API.

[0397] 2. Factory usage status acquisition method: The server acquires usage status data for each work area in real time from sensors and IoT devices within the factory.

[0398] 3. Means for acquiring emotional data: The server uses sensors such as cameras and microphones installed in each work area to analyze the facial expressions and voices of workers and acquire emotional data.

[0399] 4. BGM selection method: The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as the acquired emotional data.

[0400] 5. BGM distribution means: The server distributes the selected background music to the audio systems installed in each work area within the factory and starts playing it in real time.

[0401] 6. Volume adjustment method: The server automatically adjusts the volume based on environmental sounds and feedback from workers.

[0402] 7. Feedback collection method: Users provide feedback on the background music from their devices (smartphones, PCs, etc.), and the server analyzes this feedback to optimize the algorithm.

[0403] Example of operation

[0404] For example, if the emotion engine recognizes that a worker on a factory assembly line is a little tired at 2pm, the "Comfortable Factory BGM Maker" will do the following:

[0405] 1. Data Collection:

[0406] The server retrieves from the scheduling tool that assembly line workers are performing their regular tasks at 2:00 PM.

[0407] Assembly line usage data is collected from sensors within the factory and workers' emotional data is analyzed.

[0408] 2. Data Analysis:

[0409] The server analyzes that it is 2:00 PM and that the worker is working on the assembly line.

[0410] The emotion engine recognizes that the worker is a little tired.

[0411] 3. BGM selection:

[0412] The server determines that upbeat pop music, which has a relaxing effect while maintaining work efficiency, is optimal and selects it.

[0413] 4. BGM distribution:

[0414] The server broadcasts the selected pop music to the assembly line's audio system and begins playing the music.

[0415] Adjust the volume appropriately to play at the optimum volume for your work environment.

[0416] 5. Feedback Collection:

[0417] After completing the task, the user can submit feedback on the background music from their device.

[0418] The server collects feedback data and optimizes the algorithm so that it will be reflected in future BGM selections.

[0419] Prompt Sentence Examples

[0420] It's 2 PM in the factory, and the emotion engine recognizes that the assembly line workers are feeling a bit tired. The Comfortable Factory BGM maker selects upbeat pop music that brings positive energy and streams it to the break area.

[0421] Example prompt for a generative AI model:

[0422] What algorithm should be used to select and deliver the optimal background music when workers on a factory assembly line are feeling a little tired at 2 p.m.? Please propose an optimization method based on emotion data and schedule data.

[0423] In this way, the "Comfortable Factory BGM Maker" can provide optimal background music based on workers' emotions and real-time data, creating an effective working environment.

[0424] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0425] Step 1:

[0426] The server obtains worker schedule information from a schedule management tool or calendar API. The input is the API endpoint of the schedule management tool. The output is the worker schedule data, which includes work hours and work content. This process sends an API request and analyzes the obtained data.

[0427] Step 2:

[0428] The server collects real-time usage data for each work area from sensors and IoT devices in the factory. The input is data collected from the sensors and IoT devices. The output is usage data for each work area, and work progress is understood based on this data. This process involves collecting and analyzing raw data from the sensors.

[0429] Step 3:

[0430] The server uses cameras and microphones installed in the work area to capture and analyze emotion data from the worker's facial expressions and voice. The input is real-time video and audio data obtained from the cameras and microphones. The output is data indicating the worker's emotional state, and an emotion engine is used to recognize facial expressions and analyze voice.

[0431] Step 4:

[0432] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, event purpose, and acquired emotional data. The input is all the data acquired in the previous step. The output is a list of selected background music, and the optimal music is identified using the background music selection algorithm.

[0433] Step 5:

[0434] The server distributes the selected background music to the audio systems installed in each work area in the factory and starts playing it in real time. The input is the background music music file or streaming URL. The output is music playback in the work area, controlling the audio system to distribute the music.

[0435] Step 6:

[0436] The server automatically adjusts the volume based on environmental noise and feedback from workers. The input is real-time environmental sound data and feedback data. The output is an adjusted volume setting, optimizing the volume using a microphone and feedback collection tools.

[0437] Step 7:

[0438] The user provides feedback on the background music from a device (smartphone or PC). The input is the feedback data entered by the user. The output is the feedback data sent to the server, and the feedback is submitted through the device application.

[0439] Step 8:

[0440] The server analyzes the collected feedback and optimizes the algorithm to reflect it in future BGM selections. The input is the feedback data from users. The output is an updated BGM selection algorithm, which is adjusted using a feedback analysis tool.

[0441] 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.

[0442] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0443] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0444] [Second embodiment]

[0445] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0446] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0447] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0448] 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.

[0449] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0450] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0451] 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.

[0452] 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.

[0453] 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 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.

[0454] 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.

[0455] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0456] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0457] The system of the present invention, "Comfortable Office BGM Maker," collects employee schedule information and office usage status in real time, selects the most appropriate background music based on the time of day, space usage status, participant attributes, and the purpose of the event, and distributes it to each space, providing a comfortable office environment.

[0458] System Overview

[0459] The system mainly consists of the following elements:

[0460] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[0461] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[0462] 3. BGM selection method: The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, usage, participant attributes, and the purpose of the event.

[0463] 4. BGM distribution method: The server distributes the selected background music to the audio system in each space in the office and begins playback.

[0464] 5. Volume adjustment method: The server automatically adjusts the volume based on the environment and user feedback.

[0465] 6. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[0466] Program processing flow

[0467] Data collection

[0468] The server obtains employee schedule information via the company's internal calendar API and collects real-time usage data from sensors and IoT devices within the office.

[0469] Data analysis

[0470] The server determines the time period based on the acquired data and analyzes the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and purpose of the event (e.g., meeting, break, work).

[0471] BGM selection

[0472] The server runs an algorithm to select the optimal background music based on the analysis results, and selects background music that is appropriate for the space in use.

[0473] BGM playback

[0474] The selected background music is distributed from the server to the audio system in each space and played automatically, with the server automatically adjusting the volume based on the data to set it to the optimal level.

[0475] Feedback collection and optimization

[0476] Users can submit feedback on the background music from their devices, and the server collects this feedback and feeds the analysis results back into the algorithm for optimization.

[0477] Specific examples

[0478] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0479] 1. Data Collection

[0480] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[0481] The server checks the usage sensor of conference room A to see if the conference room is reserved.

[0482] 2. Data Analysis

[0483] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[0484] 3. BGM selection

[0485] The server determines that quiet jazz music is appropriate for this situation and selects it.

[0486] 4. BGM playback

[0487] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to a level that allows people to concentrate on the meeting.

[0488] 5. Gather feedback and optimize

[0489] After the meeting, users (conference participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[0490] In this way, the Comfortable Office BGM Maker provides optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[0491] The processing flow will be explained below.

[0492] Step 1:

[0493] The server retrieves each employee's calendar information through the API. This information includes details of each employee's schedule and individual schedule. For example, it confirms that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[0494] Step 2:

[0495] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that the room has been reserved.

[0496] Step 3:

[0497] The server analyzes the retrieved calendar information and usage data to determine the current time zone, for example, confirming that it is "3:00 PM."

[0498] Step 4:

[0499] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "it is being used by a member of the sales department."

[0500] Step 5:

[0501] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[0502] Step 6:

[0503] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[0504] Step 7:

[0505] The server runs an algorithm to select the best background music based on the time of day, space availability, participant attributes, and the purpose of the event. For example, it might select "quiet jazz music" as the best choice.

[0506] Step 8:

[0507] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[0508] Step 9:

[0509] The server automatically adjusts the volume of background music based on the environment and user feedback, for example, setting it to a volume level that makes it easy to concentrate in a meeting environment.

[0510] Step 10:

[0511] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[0512] Step 11:

[0513] The server collects user feedback and optimizes the background music selection algorithm based on it, for example, adjusting the list of music candidates for the next selection based on the feedback results.

[0514] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules and office usage, optimizing the office environment in real time.

[0515] Example 1

[0516] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0517] In conventional office environments, there was no system that could select optimal background music (BGM) in real time based on employees' schedules and office usage, and deliver it at an appropriate volume. This made it difficult to improve employees' concentration and achieve a relaxing effect. Another issue was that the selected BGM was not evaluated to see if it was optimal for employees, and the system was not optimized to reflect that feedback.

[0518] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0519] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage information, a means for analyzing the information acquired by the data acquisition means, a means for selecting optimal music based on the analysis results, and a means for distributing the selected music to each space in the office. This makes it possible to automatically select optimal background music in real time based on employee schedules and office usage and distribute it at an appropriate volume. Furthermore, by collecting feedback from users and optimizing the algorithm based on that feedback, a system can be realized that can continuously provide optimal background music.

[0520] "Means for obtaining employee schedule information" refers to a function that automatically collects employee schedule data using the API of a planning management tool.

[0521] "Means of obtaining office usage status" refers to a function that uses sensors and IoT devices to monitor and obtain the usage status of each space in real time.

[0522] The "means for analyzing information obtained by the data obtaining means" is a function for analyzing the obtained schedule information and usage status data and performing data processing corresponding to a specific time period or situation.

[0523] "Means for selecting optimal music based on analysis results" refers to an algorithm that automatically selects the background music that is most suitable for specific conditions based on the results of data analysis.

[0524] "Means for distributing selected music to each space within the office" refers to the function of transmitting selected background music to the audio systems installed in each space in the office and playing it back.

[0525] "Means for automatically adjusting volume" refers to a function that automatically adjusts the volume of selected background music to an optimal level based on the environment and user feedback.

[0526] "Means for collecting feedback from users and optimizing the music selection algorithm based on that feedback" refers to a function that collects opinions and impressions from users, updates the music selection algorithm based on that data, and improves its accuracy.

[0527] The system of this invention, "Comfortable Office BGM Maker," is a system that provides a comfortable office environment by acquiring employee schedule information and office usage status in real time, and selecting and distributing the most suitable background music.

[0528] System configuration

[0529] The system mainly consists of the following components:

[0530] 1. A way to obtain employee schedule information

[0531] 2. How to obtain office usage information

[0532] 3. Means for analyzing information obtained by data acquisition means

[0533] 4. A method for selecting optimal music based on analysis results

[0534] 5. A means of distributing selected music to each space in the office

[0535] 6. Automatic volume control

[0536] 7. A means of collecting user feedback and optimizing the music selection algorithm based on that feedback

[0537] Hardware and software used

[0538] Hardware:

[0539] Sensors and IoT devices installed in the office

[0540] Audio systems installed in each space (e.g., smart speakers)

[0541] software:

[0542] API for obtaining employee schedule information (e.g., calendar API)

[0543] A platform for acquiring data from IoT devices (e.g. IoT Hub)

[0544] Data analysis programs (e.g., Python, pandas library)

[0545] BGM selection algorithm (e.g., scikit-learn)

[0546] Music streaming service API (e.g., music service API)

[0547] Programs for collecting and analyzing feedback (e.g., TensorFlow, PyTorch)

[0548] Processing flow

[0549] Data collection

[0550] The server accesses an external scheduling tool (e.g., a calendar API) to retrieve employee schedule data. This information is returned in JSON format, which the server stores in a database. The server also collects real-time usage data from sensors and IoT devices (e.g., IoT Hub) installed in the office.

[0551] Data analysis

[0552] The server analyzes the acquired schedule information and usage data using data science tools (e.g., Python's pandas library). Based on the analysis results, data processing is performed according to specific time periods and situations.

[0553] BGM selection

[0554] The server then runs an algorithm to select the optimal background music based on the analysis results. This selection uses machine learning libraries (e.g., scikit-learn) and takes into account past feedback data. The algorithm selects the optimal music based on conditions (e.g., time of day, usage, participant attributes, and event purpose).

[0555] BGM playback

[0556] The server distributes the selected background music to each space in the office. For example, a music service API is used to distribute music played through a smart speaker. The server automatically adjusts the volume to the optimal level.

[0557] Gathering feedback and optimizing the algorithm

[0558] Users can submit feedback about the background music via a dedicated app or web portal from their device (smartphone or PC). This feedback data is collected and analyzed by the server, and the background music selection algorithm is optimized using a machine learning model (e.g., TensorFlow or PyTorch).

[0559] Specific examples

[0560] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0561] 1. Data Collection

[0562] The server uses the calendar API to retrieve information that a new product planning meeting is scheduled for 3:00 PM to 4:00 PM in conference room A.

[0563] The server checks the usage status of conference room A through the IoT device.

[0564] 2. Data Analysis

[0565] The server analyzes the time of day, participant attributes, purpose of the event, etc.

[0566] 3. BGM selection

[0567] Based on the analysis results, the server determines that quiet jazz music is appropriate and selects it.

[0568] 4. BGM playback

[0569] At 3:00 PM, the server will stream jazz music to the audio system in conference room A and automatically start playing it, adjusting the volume to a level appropriate for the meeting.

[0570] 5. Gathering feedback and optimizing the algorithm

[0571] After the meeting, users (conference participants) send feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[0572] Prompt Sentence Examples

[0573] "The sales department will hold a new product planning meeting in Conference Room A from 3:00 PM to 4:00 PM. Please select background music appropriate for this situation and develop an algorithm to optimally adjust the volume when playing it."

[0574] As described above, the Comfortable Office BGM Maker is a system that selects and distributes optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[0575] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0576] Step 1: Obtain employee schedule information

[0577] Input: The server takes as input an authentication token to retrieve employee schedule information via the calendar API.

[0578] How it works: The server sends an HTTP GET request to an API endpoint to retrieve event information. Specifically, it uses the requests library to send a request to https: / / api.calendarservice.com / v3 / calendars / primary / events.

[0579] Output: The retrieved schedule information is returned in JSON format, which is parsed and stored in an internal database.

[0580] Step 2: Get Office usage

[0581] Input: The server takes the IoT device's connection string and authentication information as input.

[0582] How it works: The server collects data from Microsoft Azure IoT Hub and other IoT platforms, specifically by asynchronously fetching data from devices using the asyncio and azure-iot-device libraries.

[0583] Output: The captured usage data is stored in an internal database as real-time space utilization.

[0584] Step 3: Data analysis

[0585] Input: The server inputs the employee schedule information and usage data obtained in steps 1 and 2.

[0586] How it works: The server uses the pandas library to store information in a data frame and analyzes things like time of day, usage, attendee demographics, and event purpose.

[0587] Output: The analysis results are stored in a database as information for a specific time period and situation.

[0588] Step 4: Select background music

[0589] Input: The server takes the result of the analysis in step 3 as input.

[0590] How it works: The server uses a machine learning library (e.g., scikit-learn) to run an algorithm to select the optimal background music, taking into account past feedback data.

[0591] Output: The selected background music is output as a music URL obtained using a music service API (e.g., Spotify's API).

[0592] Step 5: Play background music

[0593] Input: The server takes as input the music URL of the background music selected in step 4.

[0594] How it works: The server sends a music URL via HTTP POST request to each space's audio system (e.g., smart speaker), and also adjusts the volume to set an appropriate volume level for the environment.

[0595] Output: Music is played in the designated space, with the volume adjusted to an optimal level.

[0596] Step 6: Gather feedback and optimize

[0597] Input: Users enter feedback from their devices through a dedicated app or web portal.

[0598] How it works: The server receives the feedback data in JSON format and stores it in a database. It then optimizes the algorithm using a machine learning model (e.g., TensorFlow or PyTorch).

[0599] Output: The updated algorithm will be reflected in the next BGM selection.

[0600] (Application example 1)

[0601] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0602] In conventional automobiles, there was no mechanism to provide optimal background music according to the passenger's mood or in-car environment. Furthermore, there was a lack of systems that could meet the specific needs of passengers, such as relaxation and improved concentration. As a result, it was difficult to ensure a comfortable ride. The present invention aims to provide passengers with a comfortable riding experience by selecting optimal background music based on passenger schedule information and in-car environment information.

[0603] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0604] In this invention, the server includes means for acquiring employee schedule information, means for acquiring the usage status of the work space, means for selecting optimal background music based on the time of day, the usage status of the space, the attributes of the participants, and the purpose of the event, means for distributing the selected background music to each area in the work space, means for acquiring in-vehicle environment information, means for selecting optimal background music based on the time of day, the in-vehicle environment information, and the passengers' schedule information and mood, and means for distributing the selected background music to the in-vehicle sound system. This allows the server to always provide optimal background music based on various information, making it possible to provide passengers with a comfortable riding experience and a comfortable working environment in the work space.

[0605] The "means for acquiring employee schedule information" is a means for acquiring schedule information from a schedule management system for an office or a vehicle's passengers.

[0606] "Means for obtaining information on the usage status of business spaces" refers to means for obtaining information on the usage status of each area within a business space in real time using sensors and IoT devices.

[0607] "Means for selecting optimal background music based on the time of day, space usage, participant attributes, and event purpose" refers to a means for analyzing specified data and selecting the most suitable background music using an algorithm.

[0608] The "means for distributing the selected background music to each area within the business space" refers to a means for distributing the selected background music to the sound system in each area within the business space and starting playback.

[0609] The "means for acquiring in-vehicle environmental information" is a means for acquiring environmental data such as temperature, humidity, and number of passengers using sensors installed inside the vehicle.

[0610] "Means for selecting optimal background music based on the time of day, in-car environment information, passenger schedule information, and mood" refers to a means for analyzing this information and selecting the most suitable background music using an algorithm.

[0611] The "means for distributing the selected background music to the sound system in the vehicle" refers to a means for distributing the selected background music to the sound system in the vehicle and starting playback.

[0612] The "means for automatically adjusting the volume of selected background music" is a means for automatically adjusting the volume of background music to an appropriate level based on ambient environmental sounds and feedback.

[0613] "Means for collecting feedback from users and optimizing the background music selection algorithm based on that feedback" refers to means for collecting feedback from users and optimizing the algorithm by reflecting the analysis results in the algorithm.

[0614] This invention is a system for providing a comfortable environment in autonomous vehicles and work spaces, and aims to select optimal background music based on employee schedule information, usage status, and in-vehicle environmental information, and distribute it at the appropriate time. This system is mainly composed of a server, sensors, an audio system, and a feedback terminal.

[0615] Overall system overview

[0616] 1. How to obtain employee schedule information

[0617] The server obtains schedule information from the employee or passenger calendar API. For example, it uses the Google Calendar API to obtain schedule information for employees and passengers.

[0618] 2. Means of obtaining information on the usage status of work spaces or vehicles

[0619] The server uses sensors (temperature, humidity, motion sensors, etc.) installed in the work space and inside the vehicle to obtain real-time usage data for each area or the vehicle environment, using IoT devices and sensor APIs.

[0620] 3. BGM selection method

[0621] The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. For example, if passengers are on their way to work in the morning, it will select relaxing background music.

[0622] 4. Means of distributing the selected background music

[0623] The server distributes the selected background music to the sound systems (e.g., Bluetooth speakers) in each area of ​​the work space or vehicle, and issues instructions to automatically start playback. The selected background music is distributed using a music streaming API (e.g., Spotify API).

[0624] 5. Automatic BGM volume adjustment method

[0625] The server automatically adjusts the volume of the background music to an appropriate level based on the noise level in the vehicle or work space and user feedback.

[0626] 6. Feedback collection and optimization measures

[0627] Users submit feedback on the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the algorithm for optimization.

[0628] Specific application examples

[0629] Commuting time (9:00 AM): With the temperature inside the car at 25°C, relaxing background music is selected based on the passenger's schedule and played on the car's sound system. If the feedback indicates that the music was relaxing, it will be reflected in future background music selections.

[0630] Prompt Sentence Examples

[0631] Based on in-car environmental data (temperature, humidity, number of passengers) while driving and passenger calendar information (commencement time, important meetings, etc.), a program is created to select the most suitable background music and play it in the car.

[0632] This system will allow passengers and employees to always enjoy the optimal music environment while riding or working, improving comfort and allowing for an even more personalized music environment by improving the background music selection algorithm based on collected feedback.

[0633] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0634] Step 1:

[0635] Data collection

[0636] The server obtains schedule information using the employee or passenger calendar API. Specifically, it uses the Google Calendar API to obtain each user's schedule information and stores it internally in the system. The server also obtains real-time environmental data (temperature, humidity, number of passengers) through the work space or in-vehicle sensor API. The input for this data collection is calendar information and sensor information, and the output is a dataset that compiles this data.

[0637] Step 2:

[0638] Data analysis

[0639] The server analyzes the acquired schedule information and environmental data. The analyzed data identifies the time of day, space usage, participant attributes, and event purpose. For example, if the current time is 9:00 AM, it determines that the person is "on their way to work" and checks the environmental data to see that the temperature inside the car is 25 degrees. The input to this analysis process is the dataset obtained from data collection, and the output is the analysis result (e.g., "on their way to work, temperature 25 degrees").

[0640] Step 3:

[0641] BGM selection

[0642] The server runs an algorithm to select optimal background music based on the analysis results. For example, if you are on your way to work and the temperature is 25 degrees, it will select relaxing background music. The input to this selection process is the analysis results, and the output is information about the selected background music (e.g., a relaxing playlist).

[0643] Step 4:

[0644] BGM distribution

[0645] The server distributes the selected background music to the sound system in the work space or vehicle. Specifically, it uses a music streaming API (e.g., Spotify API) to distribute the selected background music to the sound system (Bluetooth speaker, etc.) in each area, and playback begins automatically. The input for this distribution is the selected background music information, and the output is the background music played on each sound system.

[0646] Step 5:

[0647] Volume adjustment

[0648] The server automatically adjusts the volume of the background music being played. It sets the optimal volume based on the noise level in the car or work space and user feedback. Specifically, it obtains environmental noise data from sensors and automatically adjusts the volume to an appropriate level based on that data. The inputs for this volume adjustment are the noise level and feedback information, and the output is the adjusted volume.

[0649] Step 6:

[0650] Feedback collection and optimization

[0651] Users submit feedback about the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the background music selection algorithm for optimization. Specifically, the feedback data is analyzed and reflected in future background music selections. The input for this feedback collection and optimization is user feedback, and the output is an optimized algorithm.

[0652] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0653] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[0654] System Overview

[0655] The system mainly consists of the following elements:

[0656] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[0657] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[0658] 3. Emotion engine: The server analyzes the user's facial expressions and voice from sensors installed on the terminal and in each space, and recognizes their emotions.

[0659] 4. BGM Selection: The server runs an algorithm to select the optimal BGM based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions as recognized by the emotion engine.

[0660] 5. BGM distribution method: The server distributes the selected background music to the audio systems installed in each space within the office and begins playback.

[0661] 6. Volume adjustment method: The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine.

[0662] 7. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[0663] Program processing flow

[0664] Data collection

[0665] The server obtains employee schedule information via the company calendar API, acquires real-time usage data from sensors and IoT devices in the office, and uses an emotion engine to recognize emotions from users' facial expressions and voices and collects them as data.

[0666] Data analysis

[0667] The server analyzes the various data it acquires to determine the current time period, including the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and event purpose (e.g., meeting, break, work). It also analyzes user emotion data recognized by an emotion engine.

[0668] BGM selection

[0669] The server runs an algorithm to select the optimal background music based on the analysis results and the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, music with a high relaxing effect will be selected.

[0670] BGM playback

[0671] The selected background music is distributed from the server to the audio system in each space and played automatically. The server automatically adjusts the volume based on real-time data to provide the optimal sound environment.

[0672] Feedback collection and optimization

[0673] Users can submit feedback about the background music from their devices (smartphones, PCs, etc.) The server collects this feedback and combines it with data from the emotion engine to optimize the algorithm.

[0674] Specific examples

[0675] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0676] 1. Data Collection

[0677] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[0678] The occupancy sensor in conference room A confirms that the conference room is reserved.

[0679] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[0680] 2. Data Analysis

[0681] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[0682] The emotion engine recognizes that the user is a little nervous.

[0683] 3. BGM selection

[0684] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[0685] 4. BGM playback

[0686] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[0687] 5. Gather feedback and optimize

[0688] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[0689] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[0690] The processing flow will be explained below.

[0691] Step 1:

[0692] The server retrieves each employee's calendar information via the API, including details of the employee's schedule and individual schedule, and confirms, for example, that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[0693] Step 2:

[0694] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that the room has been reserved.

[0695] Step 3:

[0696] The server collects the user's facial expressions and voice from sensors such as cameras and microphones installed on the device and in each space, and uses an emotion engine to recognize the user's emotions. For example, it can recognize a "tense facial expression" from camera footage and analyze the user's voice to determine that the user has a "tense tone of voice."

[0697] Step 4:

[0698] The server analyzes the acquired calendar information, usage data, and emotion data to identify the current time zone. For example, it verifies that it is "3:00 PM" and that it is the start time of the meeting.

[0699] Step 5:

[0700] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "the user is a member of the sales department."

[0701] Step 6:

[0702] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[0703] Step 7:

[0704] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[0705] Step 8:

[0706] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as emotional data recognized by the emotion engine. For example, it might determine that "quiet jazz music that enhances concentration and relaxation" is optimal.

[0707] Step 9:

[0708] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[0709] Step 10:

[0710] The server automatically adjusts the volume of background music based on real-time data, for example, adjusting the volume to a level that allows for concentration during meetings and setting an optimal volume level to "relieve tense emotions."

[0711] Step 11:

[0712] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[0713] Step 12:

[0714] The server collects user feedback, analyzes it together with the emotion engine data, and optimizes the background music selection algorithm. For example, it adjusts the list of music candidates for the next selection based on feedback such as "whether the user felt relieved."

[0715] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules, office usage, and user emotions, optimizing the office environment in real time.

[0716] Example 2

[0717] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0718] In modern office environments, optimizing the work environment is important to increase employee productivity and comfort. However, conventional background music systems are limited in their ability to create an effective environment because they are unable to select and play optimal music based on the user's schedule or emotional state. Furthermore, optimization through BGM volume adjustment and feedback collection is also insufficient. Therefore, there is a need for a system that can provide optimal BGM and adjust it in real time based on the office situation and the user's emotional state.

[0719] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage status, a means for recognizing user emotions, a means for selecting optimal background music based on the time period, space usage status, participant attributes, event purpose, and user emotions, a means for distributing the selected background music to each space in the office, a means for automatically adjusting the volume of the selected background music, and a means for collecting user feedback and optimizing the background music selection algorithm based on the feedback and user emotion data. This makes it possible to provide optimal background music based on various data in the office environment and continuously adjust the volume in real time.

[0720] "Employee schedule information" is data about the time and location of meetings, tasks, events, etc. scheduled by employees.

[0721] "Office usage status" refers to data such as the utilization rate, occupancy status, and availability of each space within the office.

[0722] "Means for recognizing user emotions" refers to technology that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice and identify their emotional state.

[0723] "Time zone" refers to a specific time period of the day that the system is classified into based on the current time.

[0724] "Space usage" is data that shows the current usage of each space within the office, such as conference rooms and work areas.

[0725] "Participant attributes" refers to information such as the department and position of employees who participate in events or work in the office.

[0726] "Purpose of the event" refers to the purpose of various activities that take place in the office, such as meetings, work, and breaks.

[0727] "Means for selecting optimal background music" refers to algorithms or mechanisms that select the most appropriate background music for each situation based on collected data and analysis results.

[0728] "Means for distributing selected background music to each space within the office" refers to technology that allows the server to send the selected background music to the audio system in each space and begin playback.

[0729] The "means for automatically adjusting the volume of the selected background music" is a mechanism for automatically adjusting the volume of the background music being played based on environmental noise and user feedback.

[0730] "Means for collecting feedback from users" refers to an interface or system that allows users to send their opinions and ratings about background music from their devices.

[0731] "Means to optimize the BGM selection algorithm" refers to a technology that analyzes collected user feedback and emotional data and improves the algorithm to increase the accuracy of future BGM selection.

[0732] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[0733] System Overview

[0734] The system mainly consists of the following elements:

[0735] 1. How to obtain employee schedule information

[0736] The server retrieves schedule information from employee calendars and schedule management tools via APIs, such as Google Calendar API.

[0737] 2. Means of obtaining office usage status

[0738] The server collects usage data for each space from sensors and IoT devices installed in the office, including conference room reservation systems and seat occupancy sensors.

[0739] 3. Emotion Engine

[0740] The server analyzes the user's facial expressions and voice from sensors installed on the device and in each space to recognize emotions. A specific implementation example would be to use an emotion recognition API such as Microsoft Azure Emotion API.

[0741] 4. BGM selection method

[0742] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions, as determined by an emotion engine. For example, if the user is feeling stressed, music with a relaxing effect will be selected.

[0743] 5. BGM distribution methods

[0744] The server distributes the selected background music to the audio systems installed in each space in the office and starts playback, sending a signal to the audio systems via the network.

[0745] 6. Volume adjustment means

[0746] The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine. It uses data from sound environment sensors to optimize the volume in real time.

[0747] 7. Feedback Collection Methods

[0748] Users submit feedback about the background music from their devices, and the server analyzes this feedback to optimize the algorithm. This is done using a feedback interface via devices such as smartphones and PCs.

[0749] Specific examples

[0750] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0751] 1. Data Collection

[0752] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[0753] The occupancy sensor in conference room A confirms that the conference room is reserved.

[0754] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[0755] 2. Data Analysis

[0756] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[0757] The emotion engine recognizes that the user is a little nervous.

[0758] 3. BGM selection

[0759] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[0760] 4. BGM playback

[0761] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[0762] 5. Gather feedback and optimize

[0763] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[0764] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[0765] Examples of prompt statements

[0766] "Please explain in detail how the system recognizes the emotions of individual users based on their schedules and office usage, and selects and plays the most appropriate background music."

[0767] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0768] Step 1: Data collection

[0769] Obtaining employee schedule information

[0770] The server calls a calendar API (e.g., Google Calendar API) to retrieve employee schedule information. The input data includes employee schedule information (e.g., meeting dates and times, attendee list) from the calendar API. The output data is stored in the system. This data is used in subsequent processing steps.

[0771] Acquisition of office usage status

[0772] The server collects usage data from sensors and IoT devices in the office.,Input data includes real-time data from sensors (e.g.,,conference room occupancy, seat usage).,Output data is stored in the system.

[0773] Obtaining user emotion data

[0774] The server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's facial and voice data and recognize their emotions. Input data includes facial and voice data captured by a camera and microphone. The output is the analyzed emotional data stored within the system.

[0775] Step 2: Data analysis

[0776] Identifying the time period

[0777] The server checks the current system time and determines the current time zone. The system time is used as input. The output is the determined time zone (e.g., 3:00 PM) and is stored in the system.

[0778] Analyzing space usage

[0779] The server analyzes the collected sensor data and identifies the usage status of each space. Office usage data is used as input data. Information on occupied and vacant spaces is stored in the system as output.

[0780] Analysis of attendee demographics and event objectives

[0781] The server analyzes the acquired schedule information to determine the attributes of participants and the purpose of the event. The schedule information is used as input data. As output, the attributes of participants (e.g., sales department, engineering department) and the purpose of the event (e.g., meeting, work) are saved in the system.

[0782] Emotional Data Analysis

[0783] The server analyzes the recognized emotion data and identifies the user's emotional state. The acquired emotion data is used as input data. As output, the analyzed user's emotional state (e.g., tension, relaxation) is stored in the system.

[0784] Step 3: Select background music

[0785] Execution of the BGM selection algorithm

[0786] The server runs an algorithm to select the optimal background music based on the analysis results and emotional data. Input data includes the time of day, space usage, participant attributes, event purpose, and emotional data. The selected background music is saved in the system as output. For example, music with a high relaxing effect is selected for a user who is feeling stressed.

[0787] Step 4: Play background music

[0788] BGM distribution

[0789] The server distributes the selected background music to the audio system in each space. The selected background music is used as input data. The background music sent to the audio system is played as output. Specifically, jazz music is distributed to conference room A at 3:00 PM.

[0790] Volume adjustment

[0791] The server automatically adjusts the volume based on real-time data. Environmental noise data and user feedback are used as input data. The output is background music adjusted to an appropriate volume. For example, the server detects the environmental noise in conference room A and adjusts the volume accordingly.

[0792] Step 5: Gather feedback and optimize

[0793] Collecting feedback

[0794] Users submit feedback about the background music from their devices (smartphones or PCs). The user's feedback information is used as input data. The collected feedback information is saved in the system as output. Specifically, the user submits feedback such as "The background music is a little loud."

[0795] Algorithm optimization

[0796] The server analyzes the collected feedback and emotion data and optimizes the background music selection algorithm. The feedback information and emotion data are used as input data. The optimized algorithm is reflected in the system as output. This will result in more appropriate background music selection from the next time onwards.

[0797] (Application example 2)

[0798] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0799] Conventional background music systems in factories were unable to provide appropriate background music that matched the emotions and working conditions of workers. As a result, effects such as improved work efficiency and reduced employee stress could not be expected. Furthermore, they lacked a mechanism for collecting feedback and optimizing the background music selection algorithm. There was a need to solve these problems and provide a more comfortable and efficient working environment.

[0800] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0801] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office (factory) usage status, a means for selecting optimal background music based on the time period, space usage status, participant attributes, and event purpose, a means for distributing the selected background music to each space in the office (factory), a means for acquiring and analyzing worker emotion data, and a means for selecting and distributing optimal background music for each work area in real time. This makes it possible to select and distribute background music suitable for each work area and worker in real time.

[0802] "Employee schedule information" refers to information recorded in calendars or schedule management tools that employees use to record their daily work and schedules.

[0803] "Office usage status" is data that indicates the usage status and occupancy information of each space during a specific time period.

[0804] "Time period" refers to a specific time period within a day, and is one of the criteria the system uses to select background music.

[0805] "Space usage status" refers to information that indicates the current usage status of physical locations such as conference rooms and work areas.

[0806] "Participant attributes" refers to basic information such as job title, department, and age of people attending a particular event.

[0807] The "purpose of the event" is information indicating the intention of an activity or event that takes place during a specific time period, such as a meeting, training session, or break.

[0808] "Optimal background music" is music selected to maximize work efficiency and relaxation in a specific environment or situation.

[0809] "Worker emotion data" is information that indicates the emotional state of a worker, obtained using facial expression recognition, voice analysis, etc.

[0810] "Means for selecting and delivering optimal background music for each work area in real time" refers to methods and technologies for instantly providing optimal background music to each work area based on fluctuations in the work environment and data on workers' emotions.

[0811] MODE FOR CARRYING OUT THE INVENTION

[0812] The system of this invention, "Comfortable Factory BGM Maker," selects and distributes optimal background music in real time based on worker schedule information, factory usage status, and worker emotional data in order to optimize the work environment within a factory.

[0813] System Overview

[0814] The system mainly consists of the following elements:

[0815] 1. Schedule information acquisition means: The server acquires the schedule information of workers through a schedule management tool or calendar API.

[0816] 2. Factory usage status acquisition method: The server acquires usage status data for each work area in real time from sensors and IoT devices within the factory.

[0817] 3. Means for acquiring emotional data: The server uses sensors such as cameras and microphones installed in each work area to analyze the facial expressions and voices of workers and acquire emotional data.

[0818] 4. BGM selection method: The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as the acquired emotional data.

[0819] 5. BGM distribution means: The server distributes the selected background music to the audio systems installed in each work area within the factory and starts playing it in real time.

[0820] 6. Volume adjustment method: The server automatically adjusts the volume based on environmental sounds and feedback from workers.

[0821] 7. Feedback collection method: Users provide feedback on the background music from their devices (smartphones, PCs, etc.), and the server analyzes this feedback to optimize the algorithm.

[0822] Example of operation

[0823] For example, if the emotion engine recognizes that a worker on a factory assembly line is a little tired at 2pm, the "Comfortable Factory BGM Maker" will do the following:

[0824] 1. Data Collection:

[0825] The server retrieves from the scheduling tool that assembly line workers are performing their regular tasks at 2:00 PM.

[0826] Assembly line usage data is collected from sensors within the factory and workers' emotional data is analyzed.

[0827] 2. Data Analysis:

[0828] The server analyzes that it is 2:00 PM and that the worker is working on the assembly line.

[0829] The emotion engine recognizes that the worker is a little tired.

[0830] 3. BGM selection:

[0831] The server determines that upbeat pop music, which has a relaxing effect while maintaining work efficiency, is optimal and selects it.

[0832] 4. BGM distribution:

[0833] The server broadcasts the selected pop music to the assembly line's audio system and begins playing the music.

[0834] Adjust the volume appropriately to play at the optimum volume for your work environment.

[0835] 5. Feedback Collection:

[0836] After completing the task, the user can submit feedback on the background music from their device.

[0837] The server collects feedback data and optimizes the algorithm so that it will be reflected in future BGM selections.

[0838] Prompt Sentence Examples

[0839] It's 2 PM in the factory, and the emotion engine recognizes that the assembly line workers are feeling a bit tired. The Comfortable Factory BGM maker selects upbeat pop music that brings positive energy and streams it to the break area.

[0840] Example prompt for a generative AI model:

[0841] What algorithm should be used to select and deliver the optimal background music when workers on a factory assembly line are feeling a little tired at 2 p.m.? Please propose an optimization method based on emotion data and schedule data.

[0842] In this way, the "Comfortable Factory BGM Maker" can provide optimal background music based on workers' emotions and real-time data, creating an effective working environment.

[0843] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0844] Step 1:

[0845] The server obtains worker schedule information from a schedule management tool or calendar API. The input is the API endpoint of the schedule management tool. The output is the worker schedule data, which includes work hours and work content. This process sends an API request and analyzes the obtained data.

[0846] Step 2:

[0847] The server collects real-time usage data for each work area from sensors and IoT devices in the factory. The input is data collected from the sensors and IoT devices. The output is usage data for each work area, and work progress is understood based on this data. This process involves collecting and analyzing raw data from the sensors.

[0848] Step 3:

[0849] The server uses cameras and microphones installed in the work area to capture and analyze emotion data from the worker's facial expressions and voice. The input is real-time video and audio data obtained from the cameras and microphones. The output is data indicating the worker's emotional state, and an emotion engine is used to recognize facial expressions and analyze voice.

[0850] Step 4:

[0851] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, event purpose, and acquired emotional data. The input is all the data acquired in the previous step. The output is a list of selected background music, and the optimal music is identified using the background music selection algorithm.

[0852] Step 5:

[0853] The server distributes the selected background music to the audio systems installed in each work area in the factory and starts playing it in real time. The input is the background music music file or streaming URL. The output is music playback in the work area, controlling the audio system to distribute the music.

[0854] Step 6:

[0855] The server automatically adjusts the volume based on environmental noise and feedback from workers. The input is real-time environmental sound data and feedback data. The output is an adjusted volume setting, optimizing the volume using a microphone and feedback collection tools.

[0856] Step 7:

[0857] The user provides feedback on the background music from a device (smartphone or PC). The input is the feedback data entered by the user. The output is the feedback data sent to the server, and the feedback is submitted through the device application.

[0858] Step 8:

[0859] The server analyzes the collected feedback and optimizes the algorithm to reflect it in future BGM selections. The input is the feedback data from users. The output is an updated BGM selection algorithm, which is adjusted using a feedback analysis tool.

[0860] 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.

[0861] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0862] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0863] [Third embodiment]

[0864] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0865] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0866] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0867] 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.

[0868] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0869] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0870] 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.

[0871] 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.

[0872] 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 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.

[0873] 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.

[0874] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0875] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0876] The system of the present invention, "Comfortable Office BGM Maker," collects employee schedule information and office usage status in real time, selects the most appropriate background music based on the time of day, space usage status, participant attributes, and the purpose of the event, and distributes it to each space, providing a comfortable office environment.

[0877] System Overview

[0878] The system mainly consists of the following elements:

[0879] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[0880] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[0881] 3. BGM selection method: The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, usage, participant attributes, and the purpose of the event.

[0882] 4. BGM distribution method: The server distributes the selected background music to the audio system in each space in the office and begins playback.

[0883] 5. Volume adjustment method: The server automatically adjusts the volume based on the environment and user feedback.

[0884] 6. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[0885] Program processing flow

[0886] Data collection

[0887] The server obtains employee schedule information via the company's internal calendar API and collects real-time usage data from sensors and IoT devices within the office.

[0888] Data analysis

[0889] The server determines the time period based on the acquired data and analyzes the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and purpose of the event (e.g., meeting, break, work).

[0890] BGM selection

[0891] The server runs an algorithm to select the optimal background music based on the analysis results, and selects background music that is appropriate for the space in use.

[0892] BGM playback

[0893] The selected background music is distributed from the server to the audio system in each space and played automatically, with the server automatically adjusting the volume based on the data to set it to the optimal level.

[0894] Feedback collection and optimization

[0895] Users can submit feedback on the background music from their devices, and the server collects this feedback and feeds the analysis results back into the algorithm for optimization.

[0896] Specific examples

[0897] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0898] 1. Data Collection

[0899] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[0900] The server checks the usage sensor of conference room A to see if the conference room is reserved.

[0901] 2. Data Analysis

[0902] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[0903] 3. BGM selection

[0904] The server determines that quiet jazz music is appropriate for this situation and selects it.

[0905] 4. BGM playback

[0906] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to a level that allows people to concentrate on the meeting.

[0907] 5. Gather feedback and optimize

[0908] After the meeting, users (conference participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[0909] In this way, the Comfortable Office BGM Maker provides optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[0910] The processing flow will be explained below.

[0911] Step 1:

[0912] The server retrieves each employee's calendar information through the API. This information includes details of each employee's schedule and individual schedule. For example, it confirms that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[0913] Step 2:

[0914] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that it has been reserved.

[0915] Step 3:

[0916] The server analyzes the retrieved calendar information and usage data to determine the current time zone, for example, confirming that it is "3:00 PM."

[0917] Step 4:

[0918] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "it is being used by a member of the sales department."

[0919] Step 5:

[0920] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[0921] Step 6:

[0922] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[0923] Step 7:

[0924] The server runs an algorithm to select the best background music based on the time of day, space availability, participant attributes, and the purpose of the event. For example, it might select "quiet jazz music" as the best choice.

[0925] Step 8:

[0926] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[0927] Step 9:

[0928] The server automatically adjusts the volume of background music based on the environment and user feedback, for example, setting it to a volume level that makes it easy to concentrate in a meeting environment.

[0929] Step 10:

[0930] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[0931] Step 11:

[0932] The server collects user feedback and optimizes the background music selection algorithm based on it, for example, adjusting the list of music candidates for the next selection based on the feedback results.

[0933] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules and office usage, optimizing the office environment in real time.

[0934] Example 1

[0935] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0936] In conventional office environments, there was no system that could select optimal background music (BGM) in real time based on employees' schedules and office usage, and deliver it at an appropriate volume. This made it difficult to improve employees' concentration and achieve a relaxing effect. Another issue was that the selected BGM was not evaluated to see if it was optimal for employees, and the system was not optimized to reflect that feedback.

[0937] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0938] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage information, a means for analyzing the information acquired by the data acquisition means, a means for selecting optimal music based on the analysis results, and a means for distributing the selected music to each space in the office. This makes it possible to automatically select optimal background music in real time based on employee schedules and office usage and distribute it at an appropriate volume. Furthermore, by collecting feedback from users and optimizing the algorithm based on that feedback, a system can be realized that can continuously provide optimal background music.

[0939] "Means for obtaining employee schedule information" refers to a function that automatically collects employee schedule data using the API of a planning management tool.

[0940] "Means of obtaining office usage status" refers to a function that uses sensors and IoT devices to monitor and obtain the usage status of each space in real time.

[0941] The "means for analyzing information obtained by the data obtaining means" is a function for analyzing the obtained schedule information and usage status data and performing data processing corresponding to a specific time period or situation.

[0942] "Means for selecting optimal music based on analysis results" refers to an algorithm that automatically selects the background music that is most suitable for specific conditions based on the results of data analysis.

[0943] "Means for distributing selected music to each space within the office" refers to the function of transmitting selected background music to the audio systems installed in each space in the office and playing it back.

[0944] "Means for automatically adjusting volume" refers to a function that automatically adjusts the volume of selected background music to an optimal level based on the environment and user feedback.

[0945] "Means for collecting feedback from users and optimizing the music selection algorithm based on that feedback" refers to a function that collects opinions and impressions from users, updates the music selection algorithm based on that data, and improves its accuracy.

[0946] The system of this invention, "Comfortable Office BGM Maker," is a system that provides a comfortable office environment by acquiring employee schedule information and office usage status in real time, and selecting and distributing the most suitable background music.

[0947] System configuration

[0948] The system mainly consists of the following components:

[0949] 1. A way to obtain employee schedule information

[0950] 2. How to obtain office usage information

[0951] 3. Means for analyzing information obtained by data acquisition means

[0952] 4. A method for selecting optimal music based on analysis results

[0953] 5. A means of distributing selected music to each space in the office

[0954] 6. Automatic volume control

[0955] 7. A means of collecting user feedback and optimizing the music selection algorithm based on that feedback

[0956] Hardware and software used

[0957] Hardware:

[0958] Sensors and IoT devices installed in the office

[0959] Audio systems installed in each space (e.g., smart speakers)

[0960] software:

[0961] API for obtaining employee schedule information (e.g., calendar API)

[0962] A platform for acquiring data from IoT devices (e.g. IoT Hub)

[0963] Data analysis programs (e.g., Python, pandas library)

[0964] BGM selection algorithm (e.g., scikit-learn)

[0965] Music streaming service API (e.g., music service API)

[0966] Programs for collecting and analyzing feedback (e.g., TensorFlow, PyTorch)

[0967] Processing flow

[0968] Data collection

[0969] The server accesses an external scheduling tool (e.g., a calendar API) to retrieve employee schedule data. This information is returned in JSON format, which the server stores in a database. The server also collects real-time usage data from sensors and IoT devices (e.g., IoT Hub) installed in the office.

[0970] Data analysis

[0971] The server analyzes the acquired schedule information and usage data using data science tools (e.g., Python's pandas library). Based on the analysis results, data processing is performed according to specific time periods and situations.

[0972] BGM selection

[0973] The server then runs an algorithm to select the optimal background music based on the analysis results. This selection uses machine learning libraries (e.g., scikit-learn) and takes into account past feedback data. The algorithm selects the optimal music based on conditions (e.g., time of day, usage, participant attributes, and event purpose).

[0974] BGM playback

[0975] The server distributes the selected background music to each space in the office. For example, a music service API is used to distribute music played through a smart speaker. The server automatically adjusts the volume to the optimal level.

[0976] Gathering feedback and optimizing the algorithm

[0977] Users can submit feedback about the background music via a dedicated app or web portal from their device (smartphone or PC). This feedback data is collected and analyzed by the server, and the background music selection algorithm is optimized using a machine learning model (e.g., TensorFlow or PyTorch).

[0978] Specific examples

[0979] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[0980] 1. Data Collection

[0981] The server uses the calendar API to retrieve information that a new product planning meeting is scheduled for 3:00 PM to 4:00 PM in conference room A.

[0982] The server checks the usage status of conference room A through the IoT device.

[0983] 2. Data Analysis

[0984] The server analyzes the time of day, participant attributes, purpose of the event, etc.

[0985] 3. BGM selection

[0986] Based on the analysis results, the server determines that quiet jazz music is appropriate and selects it.

[0987] 4. BGM playback

[0988] At 3:00 PM, the server will stream jazz music to the audio system in conference room A and automatically start playing it, adjusting the volume to a level appropriate for the meeting.

[0989] 5. Gathering feedback and optimizing the algorithm

[0990] After the meeting, users (conference participants) send feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[0991] Prompt Sentence Examples

[0992] "The sales department will hold a new product planning meeting in Conference Room A from 3:00 PM to 4:00 PM. Please select background music appropriate for this situation and develop an algorithm to optimally adjust the volume when playing it."

[0993] As described above, the Comfortable Office BGM Maker is a system that selects and distributes optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[0994] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0995] Step 1: Obtain employee schedule information

[0996] Input: The server takes as input an authentication token to retrieve employee schedule information via the calendar API.

[0997] How it works: The server sends an HTTP GET request to an API endpoint to retrieve event information. Specifically, it uses the requests library to send a request to https: / / api.calendarservice.com / v3 / calendars / primary / events.

[0998] Output: The retrieved schedule information is returned in JSON format, which is parsed and stored in an internal database.

[0999] Step 2: Get Office usage

[1000] Input: The server takes the IoT device's connection string and authentication information as input.

[1001] How it works: The server collects data from Microsoft Azure IoT Hub and other IoT platforms, specifically by asynchronously fetching data from devices using the asyncio and azure-iot-device libraries.

[1002] Output: The captured usage data is stored in an internal database as real-time space utilization.

[1003] Step 3: Data analysis

[1004] Input: The server inputs the employee schedule information and usage data obtained in steps 1 and 2.

[1005] How it works: The server uses the pandas library to store information in a data frame and analyzes things like time of day, usage, attendee demographics, and event purpose.

[1006] Output: The analysis results are stored in a database as information for a specific time period and situation.

[1007] Step 4: Select background music

[1008] Input: The server takes the result of the analysis in step 3 as input.

[1009] How it works: The server uses a machine learning library (e.g., scikit-learn) to run an algorithm to select the optimal background music, taking into account past feedback data.

[1010] Output: The selected background music is output as a music URL obtained using a music service API (e.g., Spotify's API).

[1011] Step 5: Play background music

[1012] Input: The server takes as input the music URL of the background music selected in step 4.

[1013] How it works: The server sends a music URL via HTTP POST request to each space's audio system (e.g., smart speaker), and also adjusts the volume to set an appropriate volume level for the environment.

[1014] Output: Music is played in the designated space, with the volume adjusted to an optimal level.

[1015] Step 6: Gather feedback and optimize

[1016] Input: Users enter feedback from their devices through a dedicated app or web portal.

[1017] How it works: The server receives the feedback data in JSON format and stores it in a database. It then optimizes the algorithm using a machine learning model (e.g., TensorFlow or PyTorch).

[1018] Output: The updated algorithm will be reflected in the next BGM selection.

[1019] (Application example 1)

[1020] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1021] In conventional automobiles, there was no mechanism to provide optimal background music according to the passenger's mood or in-car environment. Furthermore, there was a lack of systems that could meet the specific needs of passengers, such as relaxation and improved concentration. As a result, it was difficult to ensure a comfortable ride. The present invention aims to provide passengers with a comfortable riding experience by selecting optimal background music based on passenger schedule information and in-car environment information.

[1022] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1023] In this invention, the server includes means for acquiring employee schedule information, means for acquiring the usage status of the work space, means for selecting optimal background music based on the time of day, the usage status of the space, the attributes of the participants, and the purpose of the event, means for distributing the selected background music to each area in the work space, means for acquiring in-vehicle environment information, means for selecting optimal background music based on the time of day, the in-vehicle environment information, and the passengers' schedule information and mood, and means for distributing the selected background music to the in-vehicle sound system. This allows the server to always provide optimal background music based on various information, making it possible to provide passengers with a comfortable riding experience and a comfortable working environment in the work space.

[1024] The "means for acquiring employee schedule information" is a means for acquiring schedule information from a schedule management system for an office or a vehicle's passengers.

[1025] "Means for obtaining information on the usage status of business spaces" refers to means for obtaining information on the usage status of each area within a business space in real time using sensors and IoT devices.

[1026] "Means for selecting optimal background music based on the time of day, space usage, participant attributes, and event purpose" refers to a means for analyzing specified data and selecting the most suitable background music using an algorithm.

[1027] The "means for distributing the selected background music to each area within the business space" refers to a means for distributing the selected background music to the sound system in each area within the business space and starting playback.

[1028] The "means for acquiring in-vehicle environmental information" is a means for acquiring environmental data such as temperature, humidity, and number of passengers using sensors installed inside the vehicle.

[1029] "Means for selecting optimal background music based on the time of day, in-car environment information, passenger schedule information, and mood" refers to a means for analyzing this information and selecting the most suitable background music using an algorithm.

[1030] The "means for distributing the selected background music to the sound system in the vehicle" refers to a means for distributing the selected background music to the sound system in the vehicle and starting playback.

[1031] The "means for automatically adjusting the volume of selected background music" is a means for automatically adjusting the volume of background music to an appropriate level based on ambient environmental sounds and feedback.

[1032] "Means for collecting feedback from users and optimizing the background music selection algorithm based on that feedback" refers to means for collecting feedback from users and optimizing the algorithm by reflecting the analysis results in the algorithm.

[1033] This invention is a system for providing a comfortable environment in autonomous vehicles and work spaces, and aims to select optimal background music based on employee schedule information, usage status, and in-vehicle environmental information, and distribute it at the appropriate time. This system is mainly composed of a server, sensors, an audio system, and a feedback terminal.

[1034] Overall system overview

[1035] 1. How to obtain employee schedule information

[1036] The server obtains schedule information from the employee or passenger calendar API. For example, it uses the Google Calendar API to obtain schedule information for employees and passengers.

[1037] 2. Means of obtaining information on the usage status of work spaces or vehicles

[1038] The server uses sensors (temperature sensors, humidity sensors, motion sensors, etc.) installed in the work space and inside the vehicle to obtain real-time usage data for each area or the vehicle environment, using IoT devices and sensor APIs.

[1039] 3. BGM selection method

[1040] The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. For example, if passengers are on their way to work in the morning, it will select relaxing background music.

[1041] 4. Means of distributing the selected background music

[1042] The server distributes the selected background music to the sound systems (e.g., Bluetooth speakers) in each area of ​​the work space or vehicle, and issues instructions to automatically start playback. The selected background music is distributed using a music streaming API (e.g., Spotify API).

[1043] 5. Automatic BGM volume adjustment method

[1044] The server automatically adjusts the volume of the background music to an appropriate level based on the noise level in the vehicle or work space and user feedback.

[1045] 6. Feedback collection and optimization measures

[1046] Users submit feedback on the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the algorithm for optimization.

[1047] Specific application examples

[1048] Commuting time (9:00 AM): With the temperature inside the car at 25°C, relaxing background music is selected based on the passenger's schedule and played on the car's sound system. If the feedback indicates that the music was relaxing, it will be reflected in future background music selections.

[1049] Prompt Sentence Examples

[1050] Based on in-car environmental data (temperature, humidity, number of passengers) while driving and passenger calendar information (commencement time, important meetings, etc.), a program is created to select the most suitable background music and play it in the car.

[1051] This system will allow passengers and employees to always enjoy the optimal music environment while riding or working, improving comfort and allowing for an even more personalized music environment by improving the background music selection algorithm based on collected feedback.

[1052] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1053] Step 1:

[1054] Data collection

[1055] The server obtains schedule information using the employee or passenger calendar API. Specifically, it uses the Google Calendar API to obtain each user's schedule information and stores it internally in the system. The server also obtains real-time environmental data (temperature, humidity, number of passengers) through the work space or in-vehicle sensor API. The input for this data collection is calendar information and sensor information, and the output is a dataset that compiles this data.

[1056] Step 2:

[1057] Data analysis

[1058] The server analyzes the acquired schedule information and environmental data. The analyzed data identifies the time of day, space usage, participant attributes, and event purpose. For example, if the current time is 9:00 AM, it determines that the person is "on their way to work" and checks the environmental data to see that the temperature inside the car is 25 degrees. The input to this analysis process is the dataset obtained from data collection, and the output is the analysis result (e.g., "on their way to work, temperature 25 degrees").

[1059] Step 3:

[1060] BGM selection

[1061] The server runs an algorithm to select optimal background music based on the analysis results. For example, if you are on your way to work and the temperature is 25 degrees, it will select relaxing background music. The input to this selection process is the analysis results, and the output is information about the selected background music (e.g., a relaxing playlist).

[1062] Step 4:

[1063] BGM distribution

[1064] The server distributes the selected background music to the sound system in the work space or vehicle. Specifically, it uses a music streaming API (e.g., Spotify API) to distribute the selected background music to the sound system (Bluetooth speaker, etc.) in each area, and playback begins automatically. The input for this distribution is the selected background music information, and the output is the background music played on each sound system.

[1065] Step 5:

[1066] Volume adjustment

[1067] The server automatically adjusts the volume of the background music being played. It sets the optimal volume based on the noise level in the car or work space and user feedback. Specifically, it obtains environmental noise data from sensors and automatically adjusts the volume to an appropriate level based on that data. The inputs for this volume adjustment are the noise level and feedback information, and the output is the adjusted volume.

[1068] Step 6:

[1069] Feedback collection and optimization

[1070] Users submit feedback about the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the background music selection algorithm for optimization. Specifically, the feedback data is analyzed and reflected in future background music selections. The input for this feedback collection and optimization is user feedback, and the output is an optimized algorithm.

[1071] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1072] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[1073] System Overview

[1074] The system mainly consists of the following elements:

[1075] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[1076] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[1077] 3. Emotion engine: The server analyzes the user's facial expressions and voice from sensors installed on the terminal and in each space, and recognizes their emotions.

[1078] 4. BGM Selection: The server runs an algorithm to select the optimal BGM based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions as recognized by the emotion engine.

[1079] 5. BGM distribution method: The server distributes the selected background music to the audio systems installed in each space within the office and begins playback.

[1080] 6. Volume adjustment method: The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine.

[1081] 7. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[1082] Program processing flow

[1083] Data collection

[1084] The server obtains employee schedule information via the company calendar API, acquires real-time usage data from sensors and IoT devices in the office, and uses an emotion engine to recognize emotions from users' facial expressions and voices and collects them as data.

[1085] Data analysis

[1086] The server analyzes the various data it acquires to determine the current time period, including the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and event purpose (e.g., meeting, break, work). It also analyzes user emotion data recognized by an emotion engine.

[1087] BGM selection

[1088] The server runs an algorithm to select the optimal background music based on the analysis results and the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, music with a high relaxing effect will be selected.

[1089] BGM playback

[1090] The selected background music is distributed from the server to the audio system in each space and played automatically. The server automatically adjusts the volume based on real-time data to provide the optimal sound environment.

[1091] Feedback collection and optimization

[1092] Users can submit feedback about the background music from their devices (smartphones, PCs, etc.) The server collects this feedback and combines it with data from the emotion engine to optimize the algorithm.

[1093] Specific examples

[1094] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[1095] 1. Data Collection

[1096] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[1097] The occupancy sensor in conference room A confirms that the conference room is reserved.

[1098] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[1099] 2. Data Analysis

[1100] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[1101] The emotion engine recognizes that the user is a little nervous.

[1102] 3. BGM selection

[1103] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[1104] 4. BGM playback

[1105] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[1106] 5. Gather feedback and optimize

[1107] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[1108] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[1109] The processing flow will be explained below.

[1110] Step 1:

[1111] The server retrieves each employee's calendar information via the API, including details of the employee's schedule and individual schedule, and confirms, for example, that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[1112] Step 2:

[1113] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that the room has been reserved.

[1114] Step 3:

[1115] The server collects the user's facial expressions and voice from sensors such as cameras and microphones installed on the device and in each space, and uses an emotion engine to recognize the user's emotions. For example, it can recognize a "tense facial expression" from camera footage and analyze the user's voice to determine that the user has a "tense tone of voice."

[1116] Step 4:

[1117] The server analyzes the acquired calendar information, usage data, and emotion data to identify the current time zone. For example, it verifies that it is "3:00 PM" and that it is the start time of the meeting.

[1118] Step 5:

[1119] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "the user is a member of the sales department."

[1120] Step 6:

[1121] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[1122] Step 7:

[1123] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[1124] Step 8:

[1125] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as emotional data recognized by the emotion engine. For example, it might determine that "quiet jazz music that enhances concentration and relaxation" is optimal.

[1126] Step 9:

[1127] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[1128] Step 10:

[1129] The server automatically adjusts the volume of background music based on real-time data, for example, adjusting the volume to a level that allows for concentration during meetings and setting an optimal volume level to "relieve tense emotions."

[1130] Step 11:

[1131] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[1132] Step 12:

[1133] The server collects user feedback, analyzes it together with the emotion engine data, and optimizes the background music selection algorithm. For example, it adjusts the list of music candidates for the next selection based on feedback such as "whether the user felt relieved."

[1134] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules, office usage, and user emotions, optimizing the office environment in real time.

[1135] Example 2

[1136] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1137] In modern office environments, optimizing the work environment is important to increase employee productivity and comfort. However, conventional background music systems are limited in their ability to create an effective environment because they are unable to select and play optimal music based on the user's schedule or emotional state. Furthermore, optimization through BGM volume adjustment and feedback collection is also insufficient. Therefore, there is a need for a system that can provide optimal BGM and adjust it in real time based on the office situation and the user's emotional state.

[1138] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage status, a means for recognizing user emotions, a means for selecting optimal background music based on the time period, space usage status, participant attributes, event purpose, and user emotions, a means for distributing the selected background music to each space in the office, a means for automatically adjusting the volume of the selected background music, and a means for collecting user feedback and optimizing the background music selection algorithm based on the feedback and user emotion data. This makes it possible to provide optimal background music based on various data in the office environment and continuously adjust the volume in real time.

[1139] "Employee schedule information" is data about the time and location of meetings, tasks, events, etc. scheduled by employees.

[1140] "Office usage status" refers to data such as the utilization rate, occupancy status, and availability of each space within the office.

[1141] "Means for recognizing user emotions" refers to technology that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice and identify their emotional state.

[1142] "Time zone" refers to a specific time period of the day that the system is classified into based on the current time.

[1143] "Space usage" is data that shows the current usage of each space within the office, such as conference rooms and work areas.

[1144] "Participant attributes" refers to information such as the department and position of employees who participate in events or work in the office.

[1145] "Purpose of the event" refers to the purpose of various activities that take place in the office, such as meetings, work, and breaks.

[1146] "Means for selecting optimal background music" refers to algorithms or mechanisms that select the most appropriate background music for each situation based on collected data and analysis results.

[1147] "Means for distributing selected background music to each space within the office" refers to technology that allows the server to send the selected background music to the audio system in each space and begin playback.

[1148] The "means for automatically adjusting the volume of the selected background music" is a mechanism for automatically adjusting the volume of the background music being played based on environmental noise and user feedback.

[1149] "Means for collecting feedback from users" refers to an interface or system that allows users to send their opinions and ratings about background music from their devices.

[1150] "Means to optimize the BGM selection algorithm" refers to a technology that analyzes collected user feedback and emotional data and improves the algorithm to increase the accuracy of future BGM selection.

[1151] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[1152] System Overview

[1153] The system mainly consists of the following elements:

[1154] 1. How to obtain employee schedule information

[1155] The server retrieves schedule information from employee calendars and schedule management tools via APIs, such as Google Calendar API.

[1156] 2. Means of obtaining office usage status

[1157] The server collects usage data for each space from sensors and IoT devices installed in the office, including conference room reservation systems and seat occupancy sensors.

[1158] 3. Emotion Engine

[1159] The server analyzes the user's facial expressions and voice from sensors installed on the device and in each space to recognize emotions. A specific implementation example would be to use an emotion recognition API such as Microsoft Azure Emotion API.

[1160] 4. BGM selection method

[1161] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions, as determined by an emotion engine. For example, if the user is feeling stressed, music with a relaxing effect will be selected.

[1162] 5. BGM distribution methods

[1163] The server distributes the selected background music to the audio systems installed in each space in the office and starts playback, sending a signal to the audio systems via the network.

[1164] 6. Volume adjustment means

[1165] The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine. It uses data from sound environment sensors to optimize the volume in real time.

[1166] 7. Feedback Collection Methods

[1167] Users submit feedback about the background music from their devices, and the server analyzes this feedback to optimize the algorithm. This is done using a feedback interface via devices such as smartphones and PCs.

[1168] Specific examples

[1169] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[1170] 1. Data Collection

[1171] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[1172] The occupancy sensor in conference room A confirms that the conference room is reserved.

[1173] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[1174] 2. Data Analysis

[1175] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[1176] The emotion engine recognizes that the user is a little nervous.

[1177] 3. BGM selection

[1178] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[1179] 4. BGM playback

[1180] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[1181] 5. Gather feedback and optimize

[1182] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[1183] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[1184] Examples of prompt statements

[1185] "Please explain in detail how the system recognizes the emotions of individual users based on their schedules and office usage, and selects and plays the most appropriate background music."

[1186] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1187] Step 1: Data collection

[1188] Obtaining employee schedule information

[1189] The server calls a calendar API (e.g., Google Calendar API) to retrieve employee schedule information. The input data includes employee schedule information (e.g., meeting dates and times, attendee list) from the calendar API. The output data is stored in the system. This data is used in subsequent processing steps.

[1190] Acquisition of office usage status

[1191] The server collects usage data from sensors and IoT devices in the office.,Input data includes real-time data from sensors (e.g.,,conference room occupancy, seat usage).,Output data is stored in the system.

[1192] Obtaining user emotion data

[1193] The server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's facial and voice data and recognize their emotions. Input data includes facial and voice data captured by a camera and microphone. The output is the analyzed emotional data stored within the system.

[1194] Step 2: Data analysis

[1195] Identifying the time period

[1196] The server checks the current system time and determines the current time zone. The system time is used as input. The output is the determined time zone (e.g., 3:00 PM) and is stored in the system.

[1197] Analyzing space usage

[1198] The server analyzes the collected sensor data and identifies the usage status of each space. Office usage data is used as input data. Information on occupied and vacant spaces is stored in the system as output.

[1199] Analysis of attendee demographics and event objectives

[1200] The server analyzes the acquired schedule information to determine the attributes of participants and the purpose of the event. The schedule information is used as input data. As output, the attributes of participants (e.g., sales department, engineering department) and the purpose of the event (e.g., meeting, work) are saved in the system.

[1201] Emotional Data Analysis

[1202] The server analyzes the recognized emotion data and identifies the user's emotional state. The acquired emotion data is used as input data. As output, the analyzed user's emotional state (e.g., tension, relaxation) is stored in the system.

[1203] Step 3: Select background music

[1204] Execution of the BGM selection algorithm

[1205] The server runs an algorithm to select the optimal background music based on the analysis results and emotional data. Input data includes the time of day, space usage, participant attributes, event purpose, and emotional data. The selected background music is saved in the system as output. For example, music with a high relaxing effect is selected for a user who is feeling stressed.

[1206] Step 4: Play background music

[1207] BGM distribution

[1208] The server distributes the selected background music to the audio system in each space. The selected background music is used as input data. The background music sent to the audio system is played as output. Specifically, jazz music is distributed to conference room A at 3:00 PM.

[1209] Volume adjustment

[1210] The server automatically adjusts the volume based on real-time data. Environmental noise data and user feedback are used as input data. The output is background music adjusted to an appropriate volume. For example, the server detects the environmental noise in conference room A and adjusts the volume accordingly.

[1211] Step 5: Gather feedback and optimize

[1212] Collecting feedback

[1213] Users submit feedback about the background music from their devices (smartphones or PCs). The user's feedback information is used as input data. The collected feedback information is saved in the system as output. Specifically, the user submits feedback such as "The background music is a little loud."

[1214] Algorithm optimization

[1215] The server analyzes the collected feedback and emotion data and optimizes the background music selection algorithm. The feedback information and emotion data are used as input data. The optimized algorithm is reflected in the system as output. This will result in more appropriate background music selection from the next time onwards.

[1216] (Application example 2)

[1217] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1218] Conventional background music systems in factories were unable to provide appropriate background music that matched the emotions and working conditions of workers. As a result, effects such as improved work efficiency and reduced employee stress could not be expected. Furthermore, they lacked a mechanism for collecting feedback and optimizing the background music selection algorithm. There was a need to solve these problems and provide a more comfortable and efficient working environment.

[1219] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1220] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office (factory) usage status, a means for selecting optimal background music based on the time period, space usage status, participant attributes, and event purpose, a means for distributing the selected background music to each space in the office (factory), a means for acquiring and analyzing worker emotion data, and a means for selecting and distributing optimal background music for each work area in real time. This makes it possible to select and distribute background music suitable for each work area and worker in real time.

[1221] "Employee schedule information" refers to information recorded in calendars or schedule management tools that employees use to record their daily work and schedules.

[1222] "Office usage status" is data that indicates the usage status and occupancy information of each space during a specific time period.

[1223] "Time period" refers to a specific time period within a day, and is one of the criteria the system uses to select background music.

[1224] "Space usage status" refers to information that indicates the current usage status of physical locations such as conference rooms and work areas.

[1225] "Participant attributes" refers to basic information such as job title, department, and age of people attending a particular event.

[1226] The "purpose of the event" is information indicating the intention of an activity or event that takes place during a specific time period, such as a meeting, training session, or break.

[1227] "Optimal background music" is music selected to maximize work efficiency and relaxation in a specific environment or situation.

[1228] "Worker emotion data" is information that indicates the emotional state of a worker, obtained using facial expression recognition, voice analysis, etc.

[1229] "Means for selecting and delivering optimal background music for each work area in real time" refers to methods and technologies for instantly providing optimal background music to each work area based on fluctuations in the work environment and data on workers' emotions.

[1230] MODE FOR CARRYING OUT THE INVENTION

[1231] The system of this invention, "Comfortable Factory BGM Maker," selects and distributes optimal background music in real time based on worker schedule information, factory usage status, and worker emotional data in order to optimize the work environment within a factory.

[1232] System Overview

[1233] The system mainly consists of the following elements:

[1234] 1. Schedule information acquisition means: The server acquires the schedule information of workers through a schedule management tool or calendar API.

[1235] 2. Factory usage status acquisition method: The server acquires usage status data for each work area in real time from sensors and IoT devices within the factory.

[1236] 3. Means for acquiring emotional data: The server uses sensors such as cameras and microphones installed in each work area to analyze the facial expressions and voices of workers and acquire emotional data.

[1237] 4. BGM selection method: The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as the acquired emotional data.

[1238] 5. BGM distribution means: The server distributes the selected background music to the audio systems installed in each work area within the factory and starts playing it in real time.

[1239] 6. Volume adjustment method: The server automatically adjusts the volume based on environmental sounds and feedback from workers.

[1240] 7. Feedback collection method: Users provide feedback on the background music from their devices (smartphones, PCs, etc.), and the server analyzes this feedback to optimize the algorithm.

[1241] Example of operation

[1242] For example, if the emotion engine recognizes that a worker on a factory assembly line is a little tired at 2pm, the "Comfortable Factory BGM Maker" will do the following:

[1243] 1. Data Collection:

[1244] The server retrieves from the scheduling tool that assembly line workers are performing their regular tasks at 2:00 PM.

[1245] Assembly line usage data is collected from sensors within the factory and workers' emotional data is analyzed.

[1246] 2. Data Analysis:

[1247] The server analyzes that it is 2:00 PM and that the worker is working on the assembly line.

[1248] The emotion engine recognizes that the worker is a little tired.

[1249] 3. BGM selection:

[1250] The server determines that upbeat pop music, which has a relaxing effect while maintaining work efficiency, is optimal and selects it.

[1251] 4. BGM distribution:

[1252] The server broadcasts the selected pop music to the assembly line's audio system and begins playing the music.

[1253] Adjust the volume appropriately to play at the optimum volume for your work environment.

[1254] 5. Feedback Collection:

[1255] After completing the task, the user can submit feedback on the background music from their device.

[1256] The server collects feedback data and optimizes the algorithm so that it will be reflected in future BGM selections.

[1257] Prompt Sentence Examples

[1258] It's 2 PM in the factory, and the emotion engine recognizes that the assembly line workers are feeling a bit tired. The Comfortable Factory BGM maker selects upbeat pop music that brings positive energy and streams it to the break area.

[1259] Example prompt for a generative AI model:

[1260] What algorithm should be used to select and deliver the optimal background music when workers on a factory assembly line are feeling a little tired at 2 p.m.? Please propose an optimization method based on emotion data and schedule data.

[1261] In this way, the "Comfortable Factory BGM Maker" can provide optimal background music based on workers' emotions and real-time data, creating an effective working environment.

[1262] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1263] Step 1:

[1264] The server obtains worker schedule information from a schedule management tool or calendar API. The input is the API endpoint of the schedule management tool. The output is the worker schedule data, which includes work hours and work content. This process sends an API request and analyzes the obtained data.

[1265] Step 2:

[1266] The server collects real-time usage data for each work area from sensors and IoT devices in the factory. The input is data collected from the sensors and IoT devices. The output is usage data for each work area, and work progress is understood based on this data. This process involves collecting and analyzing raw data from the sensors.

[1267] Step 3:

[1268] The server uses cameras and microphones installed in the work area to capture and analyze emotion data from the worker's facial expressions and voice. The input is real-time video and audio data obtained from the cameras and microphones. The output is data indicating the worker's emotional state, and an emotion engine is used to recognize facial expressions and analyze voice.

[1269] Step 4:

[1270] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, event purpose, and acquired emotional data. The input is all the data acquired in the previous step. The output is a list of selected background music, and the optimal music is identified using the background music selection algorithm.

[1271] Step 5:

[1272] The server distributes the selected background music to the audio systems installed in each work area in the factory and starts playing it in real time. The input is the background music music file or streaming URL. The output is music playback in the work area, controlling the audio system to distribute the music.

[1273] Step 6:

[1274] The server automatically adjusts the volume based on environmental noise and feedback from workers. The input is real-time environmental sound data and feedback data. The output is an adjusted volume setting, optimizing the volume using a microphone and feedback collection tools.

[1275] Step 7:

[1276] The user provides feedback on the background music from a device (smartphone or PC). The input is the feedback data entered by the user. The output is the feedback data sent to the server, and the feedback is submitted through the device application.

[1277] Step 8:

[1278] The server analyzes the collected feedback and optimizes the algorithm to reflect it in future BGM selections. The input is the feedback data from users. The output is an updated BGM selection algorithm, which is adjusted using a feedback analysis tool.

[1279] 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.

[1280] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1281] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1282] [Fourth embodiment]

[1283] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1284] 7, a 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.

[1285] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1286] 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.

[1287] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1288] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1289] 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.

[1290] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1291] 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.

[1292] 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 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.

[1293] 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.

[1294] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1295] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1296] The system of the present invention, "Comfortable Office BGM Maker," collects employee schedule information and office usage status in real time, selects the most appropriate background music based on the time of day, space usage status, participant attributes, and the purpose of the event, and distributes it to each space, providing a comfortable office environment.

[1297] System Overview

[1298] The system mainly consists of the following elements:

[1299] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[1300] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[1301] 3. BGM selection method: The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, usage, participant attributes, and the purpose of the event.

[1302] 4. BGM distribution method: The server distributes the selected background music to the audio system in each space in the office and begins playback.

[1303] 5. Volume adjustment method: The server automatically adjusts the volume based on the environment and user feedback.

[1304] 6. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[1305] Program processing flow

[1306] Data collection

[1307] The server obtains employee schedule information via the company's internal calendar API and collects real-time usage data from sensors and IoT devices within the office.

[1308] Data analysis

[1309] The server determines the time period based on the acquired data and analyzes the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and purpose of the event (e.g., meeting, break, work).

[1310] BGM selection

[1311] The server runs an algorithm to select the optimal background music based on the analysis results, and selects background music that is appropriate for the space in use.

[1312] BGM playback

[1313] The selected background music is distributed from the server to the audio system in each space and played automatically, with the server automatically adjusting the volume based on the data to set it to the optimal level.

[1314] Feedback collection and optimization

[1315] Users can submit feedback on the background music from their devices, and the server collects this feedback and feeds the analysis results back into the algorithm for optimization.

[1316] Specific examples

[1317] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[1318] 1. Data Collection

[1319] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[1320] The server checks the usage sensor of conference room A to see if the conference room is reserved.

[1321] 2. Data Analysis

[1322] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[1323] 3. BGM selection

[1324] The server determines that quiet jazz music is appropriate for this situation and selects it.

[1325] 4. BGM playback

[1326] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to a level that allows people to concentrate on the meeting.

[1327] 5. Gather feedback and optimize

[1328] After the meeting, users (conference participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[1329] In this way, the Comfortable Office BGM Maker provides optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[1330] The processing flow will be explained below.

[1331] Step 1:

[1332] The server retrieves each employee's calendar information through the API. This information includes details of each employee's schedule and individual schedule. For example, it confirms that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[1333] Step 2:

[1334] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that the room has been reserved.

[1335] Step 3:

[1336] The server analyzes the retrieved calendar information and usage data to determine the current time zone, for example, confirming that it is "3:00 PM."

[1337] Step 4:

[1338] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "it is being used by a member of the sales department."

[1339] Step 5:

[1340] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[1341] Step 6:

[1342] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[1343] Step 7:

[1344] The server runs an algorithm to select the best background music based on the time of day, space availability, participant attributes, and the purpose of the event. For example, it might select "quiet jazz music" as the best choice.

[1345] Step 8:

[1346] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[1347] Step 9:

[1348] The server automatically adjusts the volume of background music based on the environment and user feedback, for example, setting it to a volume level that makes it easy to concentrate in a meeting environment.

[1349] Step 10:

[1350] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[1351] Step 11:

[1352] The server collects user feedback and optimizes the background music selection algorithm based on it, for example, adjusting the list of music candidates for the next selection based on the feedback results.

[1353] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules and office usage, optimizing the office environment in real time.

[1354] Example 1

[1355] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1356] In conventional office environments, there was no system that could select optimal background music (BGM) in real time based on employees' schedules and office usage, and deliver it at an appropriate volume. This made it difficult to improve employees' concentration and achieve a relaxing effect. Another issue was that the selected BGM was not evaluated to see if it was optimal for employees, and the system was not optimized to reflect that feedback.

[1357] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1358] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage information, a means for analyzing the information acquired by the data acquisition means, a means for selecting optimal music based on the analysis results, and a means for distributing the selected music to each space in the office. This makes it possible to automatically select optimal background music in real time based on employee schedules and office usage and distribute it at an appropriate volume. Furthermore, by collecting feedback from users and optimizing the algorithm based on that feedback, a system can be realized that can continuously provide optimal background music.

[1359] "Means for obtaining employee schedule information" refers to a function that automatically collects employee schedule data using the API of a planning management tool.

[1360] "Means of obtaining office usage status" refers to a function that uses sensors and IoT devices to monitor and obtain the usage status of each space in real time.

[1361] The "means for analyzing information obtained by the data obtaining means" is a function for analyzing the obtained schedule information and usage status data and performing data processing corresponding to a specific time period or situation.

[1362] "Means for selecting optimal music based on analysis results" refers to an algorithm that automatically selects the background music that is most suitable for specific conditions based on the results of data analysis.

[1363] "Means for distributing selected music to each space within the office" refers to the function of transmitting selected background music to the audio systems installed in each space in the office and playing it back.

[1364] "Means for automatically adjusting volume" refers to a function that automatically adjusts the volume of selected background music to an optimal level based on the environment and user feedback.

[1365] "Means for collecting feedback from users and optimizing the music selection algorithm based on that feedback" refers to a function that collects opinions and impressions from users, updates the music selection algorithm based on that data, and improves its accuracy.

[1366] The system of this invention, "Comfortable Office BGM Maker," is a system that provides a comfortable office environment by acquiring employee schedule information and office usage status in real time, and selecting and distributing the most suitable background music.

[1367] System configuration

[1368] The system mainly consists of the following components:

[1369] 1. A way to obtain employee schedule information

[1370] 2. How to obtain office usage information

[1371] 3. Means for analyzing information obtained by data acquisition means

[1372] 4. A method for selecting optimal music based on analysis results

[1373] 5. A means of distributing selected music to each space in the office

[1374] 6. Automatic volume control

[1375] 7. A means of collecting user feedback and optimizing the music selection algorithm based on that feedback

[1376] Hardware and software used

[1377] Hardware:

[1378] Sensors and IoT devices installed in the office

[1379] Audio systems installed in each space (e.g., smart speakers)

[1380] software:

[1381] API for obtaining employee schedule information (e.g., calendar API)

[1382] A platform for acquiring data from IoT devices (e.g. IoT Hub)

[1383] Data analysis programs (e.g., Python, pandas library)

[1384] BGM selection algorithm (e.g., scikit-learn)

[1385] Music streaming service API (e.g., music service API)

[1386] Programs for collecting and analyzing feedback (e.g., TensorFlow, PyTorch)

[1387] Processing flow

[1388] Data collection

[1389] The server accesses an external scheduling tool (e.g., a calendar API) to retrieve employee schedule data. This information is returned in JSON format, which the server stores in a database. The server also collects real-time usage data from sensors and IoT devices (e.g., IoT Hub) installed in the office.

[1390] Data analysis

[1391] The server analyzes the acquired schedule information and usage data using data science tools (e.g., Python's pandas library). Based on the analysis results, data processing is performed according to specific time periods and situations.

[1392] BGM selection

[1393] The server then runs an algorithm to select the optimal background music based on the analysis results. This selection uses machine learning libraries (e.g., scikit-learn) and takes into account past feedback data. The algorithm selects the optimal music based on conditions (e.g., time of day, usage, participant attributes, and event purpose).

[1394] BGM playback

[1395] The server distributes the selected background music to each space in the office. For example, a music service API is used to distribute music played through a smart speaker. The server automatically adjusts the volume to the optimal level.

[1396] Gathering feedback and optimizing the algorithm

[1397] Users can submit feedback about the background music via a dedicated app or web portal from their device (smartphone or PC). This feedback data is collected and analyzed by the server, and the background music selection algorithm is optimized using a machine learning model (e.g., TensorFlow or PyTorch).

[1398] Specific examples

[1399] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[1400] 1. Data Collection

[1401] The server uses the calendar API to retrieve information that a new product planning meeting is scheduled for 3:00 PM to 4:00 PM in conference room A.

[1402] The server checks the usage status of conference room A through the IoT device.

[1403] 2. Data Analysis

[1404] The server analyzes the time of day, participant attributes, purpose of the event, etc.

[1405] 3. BGM selection

[1406] Based on the analysis results, the server determines that quiet jazz music is appropriate and selects it.

[1407] 4. BGM playback

[1408] At 3:00 PM, the server will stream jazz music to the audio system in conference room A and automatically start playing it, adjusting the volume to a level appropriate for the meeting.

[1409] 5. Gathering feedback and optimizing the algorithm

[1410] After the meeting, users (conference participants) send feedback from their devices. The server adjusts the algorithm based on this feedback and reflects it in the selection of background music for the next meeting and beyond.

[1411] Prompt Sentence Examples

[1412] "The sales department will hold a new product planning meeting in Conference Room A from 3:00 PM to 4:00 PM. Please select background music appropriate for this situation and develop an algorithm to optimally adjust the volume when playing it."

[1413] As described above, the Comfortable Office BGM Maker is a system that selects and distributes optimal background music in real time, improving employees' concentration and creating a relaxing effect.

[1414] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1415] Step 1: Obtain employee schedule information

[1416] Input: The server takes as input an authentication token to retrieve employee schedule information via the calendar API.

[1417] How it works: The server sends an HTTP GET request to an API endpoint to retrieve event information. Specifically, it uses the requests library to send a request to https: / / api.calendarservice.com / v3 / calendars / primary / events.

[1418] Output: The retrieved schedule information is returned in JSON format, which is parsed and stored in an internal database.

[1419] Step 2: Get Office usage

[1420] Input: The server takes the IoT device's connection string and authentication information as input.

[1421] How it works: The server collects data from Microsoft Azure IoT Hub and other IoT platforms, specifically by asynchronously fetching data from devices using the asyncio and azure-iot-device libraries.

[1422] Output: The captured usage data is stored in an internal database as real-time space utilization.

[1423] Step 3: Data analysis

[1424] Input: The server inputs the employee schedule information and usage data obtained in steps 1 and 2.

[1425] How it works: The server uses the pandas library to store information in a data frame and analyzes things like time of day, usage, attendee demographics, and event purpose.

[1426] Output: The analysis results are stored in a database as information for a specific time period and situation.

[1427] Step 4: Select background music

[1428] Input: The server takes the result of the analysis in step 3 as input.

[1429] How it works: The server uses a machine learning library (e.g., scikit-learn) to run an algorithm to select the optimal background music, taking into account past feedback data.

[1430] Output: The selected background music is output as a music URL obtained using a music service API (e.g., Spotify's API).

[1431] Step 5: Play background music

[1432] Input: The server takes as input the music URL of the background music selected in step 4.

[1433] How it works: The server sends a music URL via HTTP POST request to each space's audio system (e.g., smart speaker), and also adjusts the volume to set an appropriate volume level for the environment.

[1434] Output: Music is played in the designated space, with the volume adjusted to an optimal level.

[1435] Step 6: Gather feedback and optimize

[1436] Input: Users enter feedback from their devices through a dedicated app or web portal.

[1437] How it works: The server receives the feedback data in JSON format and stores it in a database. It then optimizes the algorithm using a machine learning model (e.g., TensorFlow or PyTorch).

[1438] Output: The updated algorithm will be reflected in the next BGM selection.

[1439] (Application example 1)

[1440] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1441] In conventional automobiles, there was no mechanism to provide optimal background music according to the passenger's mood or in-car environment. Furthermore, there was a lack of systems that could meet the specific needs of passengers, such as relaxation and improved concentration. As a result, it was difficult to ensure a comfortable ride. The present invention aims to provide passengers with a comfortable riding experience by selecting optimal background music based on passenger schedule information and in-car environment information.

[1442] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1443] In this invention, the server includes means for acquiring employee schedule information, means for acquiring the usage status of the work space, means for selecting optimal background music based on the time of day, the usage status of the space, the attributes of the participants, and the purpose of the event, means for distributing the selected background music to each area in the work space, means for acquiring in-vehicle environment information, means for selecting optimal background music based on the time of day, the in-vehicle environment information, and the passengers' schedule information and mood, and means for distributing the selected background music to the in-vehicle sound system. This allows the server to always provide optimal background music based on various information, making it possible to provide passengers with a comfortable riding experience and a comfortable working environment in the work space.

[1444] The "means for acquiring employee schedule information" is a means for acquiring schedule information from a schedule management system for an office or a vehicle's passengers.

[1445] "Means for obtaining information on the usage status of business spaces" refers to means for obtaining information on the usage status of each area within a business space in real time using sensors and IoT devices.

[1446] "Means for selecting optimal background music based on the time of day, space usage, participant attributes, and event purpose" refers to a means for analyzing specified data and selecting the most suitable background music using an algorithm.

[1447] The "means for distributing the selected background music to each area within the business space" refers to a means for distributing the selected background music to the sound system in each area within the business space and starting playback.

[1448] The "means for acquiring in-vehicle environmental information" is a means for acquiring environmental data such as temperature, humidity, and number of passengers using sensors installed inside the vehicle.

[1449] "Means for selecting optimal background music based on the time of day, in-car environment information, passenger schedule information, and mood" refers to a means for analyzing this information and selecting the most suitable background music using an algorithm.

[1450] The "means for distributing the selected background music to the sound system in the vehicle" refers to a means for distributing the selected background music to the sound system in the vehicle and starting playback.

[1451] The "means for automatically adjusting the volume of selected background music" is a means for automatically adjusting the volume of background music to an appropriate level based on ambient environmental sounds and feedback.

[1452] "Means for collecting feedback from users and optimizing the background music selection algorithm based on that feedback" refers to means for collecting feedback from users and optimizing the algorithm by reflecting the analysis results in the algorithm.

[1453] This invention is a system for providing a comfortable environment in autonomous vehicles and work spaces, and aims to select optimal background music based on employee schedule information, usage status, and in-vehicle environmental information, and distribute it at the appropriate time. This system is mainly composed of a server, sensors, an audio system, and a feedback terminal.

[1454] Overall system overview

[1455] 1. How to obtain employee schedule information

[1456] The server obtains schedule information from the employee or passenger calendar API. For example, it uses the Google Calendar API to obtain schedule information for employees and passengers.

[1457] 2. Means of obtaining information on the usage status of work spaces or vehicles

[1458] The server uses sensors (temperature sensors, humidity sensors, motion sensors, etc.) installed in the work space and inside the vehicle to obtain real-time usage data for each area or the vehicle environment, using IoT devices and sensor APIs.

[1459] 3. BGM selection method

[1460] The server analyzes the acquired data and runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. For example, if passengers are on their way to work in the morning, it will select relaxing background music.

[1461] 4. Means of distributing the selected background music

[1462] The server distributes the selected background music to the sound systems (e.g., Bluetooth speakers) in each area of ​​the work space or vehicle, and issues instructions to automatically start playback. The selected background music is distributed using a music streaming API (e.g., Spotify API).

[1463] 5. Automatic BGM volume adjustment method

[1464] The server automatically adjusts the volume of the background music to an appropriate level based on the noise level in the vehicle or work space and user feedback.

[1465] 6. Feedback collection and optimization measures

[1466] Users submit feedback on the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the algorithm for optimization.

[1467] Specific application examples

[1468] Commuting time (9:00 AM): With the temperature inside the car at 25°C, relaxing background music is selected based on the passenger's schedule and played on the car's sound system. If the feedback indicates that the music was relaxing, it will be reflected in future background music selections.

[1469] Prompt Sentence Examples

[1470] Based on in-car environmental data (temperature, humidity, number of passengers) while driving and passenger calendar information (commencement time, important meetings, etc.), a program is created to select the most suitable background music and play it in the car.

[1471] This system will allow passengers and employees to always enjoy the optimal music environment while riding or working, improving comfort and allowing for an even more personalized music environment by improving the background music selection algorithm based on collected feedback.

[1472] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1473] Step 1:

[1474] Data collection

[1475] The server obtains schedule information using the employee or passenger calendar API. Specifically, it uses the Google Calendar API to obtain each user's schedule information and stores it internally in the system. The server also obtains real-time environmental data (temperature, humidity, number of passengers) through the work space or in-vehicle sensor API. The input for this data collection is calendar information and sensor information, and the output is a dataset that compiles this data.

[1476] Step 2:

[1477] Data analysis

[1478] The server analyzes the acquired schedule information and environmental data. The analyzed data identifies the time of day, space usage, participant attributes, and event purpose. For example, if the current time is 9:00 AM, it determines that the person is "on their way to work" and checks the environmental data to see that the temperature inside the car is 25 degrees. The input to this analysis process is the dataset obtained from data collection, and the output is the analysis result (e.g., "on their way to work, temperature 25 degrees").

[1479] Step 3:

[1480] BGM selection

[1481] The server runs an algorithm to select optimal background music based on the analysis results. For example, if you are on your way to work and the temperature is 25 degrees, it will select relaxing background music. The input to this selection process is the analysis results, and the output is information about the selected background music (e.g., a relaxing playlist).

[1482] Step 4:

[1483] BGM distribution

[1484] The server distributes the selected background music to the sound system in the work space or vehicle. Specifically, it uses a music streaming API (e.g., Spotify API) to distribute the selected background music to the sound system (Bluetooth speaker, etc.) in each area, and playback begins automatically. The input for this distribution is the selected background music information, and the output is the background music played on each sound system.

[1485] Step 5:

[1486] Volume adjustment

[1487] The server automatically adjusts the volume of the background music being played. It sets the optimal volume based on the noise level in the car or work space and user feedback. Specifically, it obtains environmental noise data from sensors and automatically adjusts the volume to an appropriate level based on that data. The inputs for this volume adjustment are the noise level and feedback information, and the output is the adjusted volume.

[1488] Step 6:

[1489] Feedback collection and optimization

[1490] Users submit feedback about the background music from their devices (smartphones). The server collects this feedback and feeds the analysis results back to the background music selection algorithm for optimization. Specifically, the feedback data is analyzed and reflected in future background music selections. The input for this feedback collection and optimization is user feedback, and the output is an optimized algorithm.

[1491] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1492] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[1493] System Overview

[1494] The system mainly consists of the following elements:

[1495] 1. Method for obtaining employee schedule information: The server obtains schedule information from employees' calendars and schedule management tools via the API.

[1496] 2. Office usage status acquisition method: The server acquires usage data for each space from sensors and IoT devices installed within the office.

[1497] 3. Emotion engine: The server analyzes the user's facial expressions and voice from sensors installed on the terminal and in each space, and recognizes their emotions.

[1498] 4. BGM Selection: The server runs an algorithm to select the optimal BGM based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions as recognized by the emotion engine.

[1499] 5. BGM distribution method: The server distributes the selected background music to the audio systems installed in each space within the office and begins playback.

[1500] 6. Volume adjustment method: The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine.

[1501] 7. Feedback collection method: Users submit feedback about the background music from their devices, and the server analyzes this to optimize the algorithm.

[1502] Program processing flow

[1503] Data collection

[1504] The server obtains employee schedule information via the company calendar API, acquires real-time usage data from sensors and IoT devices in the office, and uses an emotion engine to recognize emotions from users' facial expressions and voices and collects them as data.

[1505] Data analysis

[1506] The server analyzes the various data it acquires to determine the current time period, including the space usage status (e.g., meeting, working, break), participant attributes (e.g., department, position), and event purpose (e.g., meeting, break, work). It also analyzes user emotion data recognized by an emotion engine.

[1507] BGM selection

[1508] The server runs an algorithm to select the optimal background music based on the analysis results and the emotional data recognized by the emotion engine. For example, if the user is feeling stressed, music with a high relaxing effect will be selected.

[1509] BGM playback

[1510] The selected background music is distributed from the server to the audio system in each space and played automatically. The server automatically adjusts the volume based on real-time data to provide the optimal sound environment.

[1511] Feedback collection and optimization

[1512] Users can submit feedback about the background music from their devices (smartphones, PCs, etc.) The server collects this feedback and combines it with data from the emotion engine to optimize the algorithm.

[1513] Specific examples

[1514] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[1515] 1. Data Collection

[1516] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[1517] The occupancy sensor in conference room A confirms that the conference room is reserved.

[1518] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[1519] 2. Data Analysis

[1520] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[1521] The emotion engine recognizes that the user is a little nervous.

[1522] 3. BGM selection

[1523] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[1524] 4. BGM playback

[1525] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[1526] 5. Gather feedback and optimize

[1527] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[1528] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[1529] The processing flow will be explained below.

[1530] Step 1:

[1531] The server retrieves each employee's calendar information via the API, including details of the employee's schedule and individual schedule, and confirms, for example, that the "Sales Department New Product Planning Meeting" is scheduled for 3:00 PM to 4:00 PM.

[1532] Step 2:

[1533] The server collects usage data in real time from various sensors and IoT devices installed in the office. For example, it collects data from the usage sensor in conference room A and confirms that the room has been reserved.

[1534] Step 3:

[1535] The server collects the user's facial expressions and voice from sensors such as cameras and microphones installed on the device and in each space, and uses an emotion engine to recognize the user's emotions. For example, it can recognize a "tense facial expression" from camera footage and analyze the user's voice to determine that the user has a "tense tone of voice."

[1536] Step 4:

[1537] The server analyzes the acquired calendar information, usage data, and emotion data to identify the current time zone. For example, it verifies that it is "3:00 PM" and that it is the start time of the meeting.

[1538] Step 5:

[1539] The server analyzes the usage status of the space. For example, it analyzes that "Conference room A is in use" and "the user is a member of the sales department."

[1540] Step 6:

[1541] The server analyzes the attributes of the participants, for example, identifying that all users are "members of the sales department."

[1542] Step 7:

[1543] The server identifies the purpose of the event, for example, by analyzing that it is a "new product planning meeting."

[1544] Step 8:

[1545] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as emotional data recognized by the emotion engine. For example, it might determine that "quiet jazz music that enhances concentration and relaxation" is optimal.

[1546] Step 9:

[1547] The server distributes the selected background music to the audio systems installed in each space in the office. For example, at 3:00 PM, jazz music is sent to the audio system in conference room A and playback begins.

[1548] Step 10:

[1549] The server automatically adjusts the volume of background music based on real-time data, for example, adjusting the volume to a level that allows for concentration during meetings and setting an optimal volume level to "relieve tense emotions."

[1550] Step 11:

[1551] Users submit feedback about the background music from their devices (such as smartphones or PCs). For example, they can send positive feedback such as, "The music helped me concentrate."

[1552] Step 12:

[1553] The server collects user feedback, analyzes it together with the emotion engine data, and optimizes the background music selection algorithm. For example, it adjusts the list of music candidates for the next selection based on feedback such as "whether the user felt relieved."

[1554] Through this series of operations, the Comfortable Office BGM Maker can provide optimal background music based on employees' schedules, office usage, and user emotions, optimizing the office environment in real time.

[1555] Example 2

[1556] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1557] In modern office environments, optimizing the work environment is important to increase employee productivity and comfort. However, conventional background music systems are limited in their ability to create an effective environment because they are unable to select and play optimal music based on the user's schedule or emotional state. Furthermore, optimization through BGM volume adjustment and feedback collection is also insufficient. Therefore, there is a need for a system that can provide optimal BGM and adjust it in real time based on the office situation and the user's emotional state.

[1558] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office usage status, a means for recognizing user emotions, a means for selecting optimal background music based on the time period, space usage status, participant attributes, event purpose, and user emotions, a means for distributing the selected background music to each space in the office, a means for automatically adjusting the volume of the selected background music, and a means for collecting user feedback and optimizing the background music selection algorithm based on the feedback and user emotion data. This makes it possible to provide optimal background music based on various data in the office environment and continuously adjust the volume in real time.

[1559] "Employee schedule information" is data about the time and location of meetings, tasks, events, etc. scheduled by employees.

[1560] "Office usage status" refers to data such as the utilization rate, occupancy status, and availability of each space within the office.

[1561] "Means for recognizing user emotions" refers to technology that uses sensors such as cameras and microphones to analyze the user's facial expressions and voice and identify their emotional state.

[1562] "Time zone" refers to a specific time period of the day that the system is classified into based on the current time.

[1563] "Space usage" is data that shows the current usage of each space within the office, such as conference rooms and work areas.

[1564] "Participant attributes" refers to information such as the department and position of employees who participate in events or work in the office.

[1565] "Purpose of the event" refers to the purpose of various activities that take place in the office, such as meetings, work, and breaks.

[1566] "Means for selecting optimal background music" refers to algorithms or mechanisms that select the most appropriate background music for each situation based on collected data and analysis results.

[1567] "Means for distributing selected background music to each space within the office" refers to technology that allows the server to send the selected background music to the audio system in each space and begin playback.

[1568] The "means for automatically adjusting the volume of the selected background music" is a mechanism for automatically adjusting the volume of the background music being played based on environmental noise and user feedback.

[1569] "Means for collecting feedback from users" refers to an interface or system that allows users to send their opinions and ratings about background music from their devices.

[1570] "Means to optimize the BGM selection algorithm" refers to a technology that analyzes collected user feedback and emotional data and improves the algorithm to increase the accuracy of future BGM selection.

[1571] The system of the present invention, "Comfortable Office BGM Maker," obtains employee schedule information and office usage status in real time, and selects the most suitable background music using an emotion engine that recognizes the user's emotions in addition to the time of day, space usage status, participant attributes, and event purpose, and distributes it to each space, providing a comfortable office environment.

[1572] System Overview

[1573] The system mainly consists of the following elements:

[1574] 1. How to obtain employee schedule information

[1575] The server retrieves schedule information from employee calendars and schedule management tools via APIs, such as Google Calendar API.

[1576] 2. Means of obtaining office usage status

[1577] The server collects usage data for each space from sensors and IoT devices installed in the office, including conference room reservation systems and seat occupancy sensors.

[1578] 3. Emotion Engine

[1579] The server analyzes the user's facial expressions and voice from sensors installed on the device and in each space to recognize emotions. A specific implementation example would be to use an emotion recognition API such as Microsoft Azure Emotion API.

[1580] 4. BGM selection method

[1581] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event. It also takes into account the user's emotions, as determined by an emotion engine. For example, if the user is feeling stressed, music with a relaxing effect will be selected.

[1582] 5. BGM distribution methods

[1583] The server distributes the selected background music to the audio systems installed in each space in the office and starts playback, sending a signal to the audio systems via the network.

[1584] 6. Volume adjustment means

[1585] The server automatically adjusts the volume based on the environment, user feedback, and emotions recognized by the emotion engine. It uses data from sound environment sensors to optimize the volume in real time.

[1586] 7. Feedback Collection Methods

[1587] Users submit feedback about the background music from their devices, and the server analyzes this feedback to optimize the algorithm. This is done using a feedback interface via devices such as smartphones and PCs.

[1588] Specific examples

[1589] For example, if a new product planning meeting is held by members of the sales department in conference room A from 3:00 PM to 4:00 PM, this system works as follows:

[1590] 1. Data Collection

[1591] The server uses the calendar API to obtain information that a new product planning meeting is scheduled to take place in conference room A from 3:00 PM to 4:00 PM.

[1592] The occupancy sensor in conference room A confirms that the conference room is reserved.

[1593] Using an emotion engine, emotions are recognized from the facial expressions and voices of users in conference room A and collected as data.

[1594] 2. Data Analysis

[1595] Analyze that the time is 3:00 PM, the participants are members of the sales department, and the purpose of the event is a new product planning meeting.

[1596] The emotion engine recognizes that the user is a little nervous.

[1597] 3. BGM selection

[1598] The server determines that quiet jazz music, which promotes concentration and relaxation, is appropriate for this situation and selects it.

[1599] 4. BGM playback

[1600] The server will stream jazz music to the audio system in conference room A at 3:00 PM and automatically start playing it, adjusting the volume to an appropriate level.

[1601] 5. Gather feedback and optimize

[1602] After the meeting, users (meeting participants) submit feedback from their devices. The server adjusts the algorithm based on this feedback and emotion engine data, and reflects this in future background music selections.

[1603] In this way, the Comfort Office BGM maker can recognize the user's emotions and provide optimal background music based on real-time data to optimize the office environment.

[1604] Examples of prompt statements

[1605] "Please explain in detail how the system recognizes the emotions of individual users based on their schedules and office usage, and selects and plays the most appropriate background music."

[1606] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1607] Step 1: Data collection

[1608] Obtaining employee schedule information

[1609] The server calls a calendar API (e.g., Google Calendar API) to retrieve employee schedule information. The input data includes employee schedule information (e.g., meeting dates and times, attendee list) from the calendar API. The output data is stored in the system. This data is used in subsequent processing steps.

[1610] Acquisition of office usage status

[1611] The server collects usage data from sensors and IoT devices in the office.,Input data includes real-time data from sensors (e.g.,,conference room occupancy, seat usage).,Output data is stored in the system.

[1612] Obtaining user emotion data

[1613] The server uses an emotion engine (e.g., Microsoft Azure Emotion API) to analyze the user's facial and voice data and recognize their emotions. Input data includes facial and voice data captured by a camera and microphone. The output is the analyzed emotional data stored within the system.

[1614] Step 2: Data analysis

[1615] Identifying the time period

[1616] The server checks the current system time and determines the current time zone. The system time is used as input. The output is the determined time zone (e.g., 3:00 PM) and is stored in the system.

[1617] Analyzing space usage

[1618] The server analyzes the collected sensor data and identifies the usage status of each space. Office usage data is used as input data. Information on occupied and vacant spaces is stored in the system as output.

[1619] Analysis of attendee demographics and event objectives

[1620] The server analyzes the acquired schedule information to determine the attributes of participants and the purpose of the event. The schedule information is used as input data. As output, the attributes of participants (e.g., sales department, engineering department) and the purpose of the event (e.g., meeting, work) are saved in the system.

[1621] Emotional Data Analysis

[1622] The server analyzes the recognized emotion data and identifies the user's emotional state. The acquired emotion data is used as input data. As output, the analyzed user's emotional state (e.g., tension, relaxation) is stored in the system.

[1623] Step 3: Select background music

[1624] Execution of the BGM selection algorithm

[1625] The server runs an algorithm to select the optimal background music based on the analysis results and emotional data. Input data includes the time of day, space usage, participant attributes, event purpose, and emotional data. The selected background music is saved in the system as output. For example, music with a high relaxing effect is selected for a user who is feeling stressed.

[1626] Step 4: Play background music

[1627] BGM distribution

[1628] The server distributes the selected background music to the audio system in each space. The selected background music is used as input data. The background music sent to the audio system is played as output. Specifically, jazz music is distributed to conference room A at 3:00 PM.

[1629] Volume adjustment

[1630] The server automatically adjusts the volume based on real-time data. Environmental noise data and user feedback are used as input data. The output is background music adjusted to an appropriate volume. For example, the server detects the environmental noise in conference room A and adjusts the volume accordingly.

[1631] Step 5: Gather feedback and optimize

[1632] Collecting feedback

[1633] Users submit feedback about the background music from their devices (smartphones or PCs). The user's feedback information is used as input data. The collected feedback information is saved in the system as output. Specifically, the user submits feedback such as "The background music is a little loud."

[1634] Algorithm optimization

[1635] The server analyzes the collected feedback and emotion data and optimizes the background music selection algorithm. The feedback information and emotion data are used as input data. The optimized algorithm is reflected in the system as output. This will result in more appropriate background music selection from the next time onwards.

[1636] (Application example 2)

[1637] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1638] Conventional background music systems in factories were unable to provide appropriate background music that matched the emotions and working conditions of workers. As a result, effects such as improved work efficiency and reduced employee stress could not be expected. Furthermore, they lacked a mechanism for collecting feedback and optimizing the background music selection algorithm. There was a need to solve these problems and provide a more comfortable and efficient working environment.

[1639] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1640] In this invention, the server includes a means for acquiring employee schedule information, a means for acquiring office (factory) usage status, a means for selecting optimal background music based on the time period, space usage status, participant attributes, and event purpose, a means for distributing the selected background music to each space in the office (factory), a means for acquiring and analyzing worker emotion data, and a means for selecting and distributing optimal background music for each work area in real time. This makes it possible to select and distribute background music suitable for each work area and worker in real time.

[1641] "Employee schedule information" refers to information recorded in calendars or schedule management tools that employees use to record their daily work and schedules.

[1642] "Office usage status" is data that indicates the usage status and occupancy information of each space during a specific time period.

[1643] "Time period" refers to a specific time period within a day, and is one of the criteria the system uses to select background music.

[1644] "Space usage status" refers to information that indicates the current usage status of physical locations such as conference rooms and work areas.

[1645] "Participant attributes" refers to basic information such as job title, department, and age of people attending a particular event.

[1646] The "purpose of the event" is information indicating the intention of an activity or event that takes place during a specific time period, such as a meeting, training session, or break.

[1647] "Optimal background music" is music selected to maximize work efficiency and relaxation in a specific environment or situation.

[1648] "Worker emotion data" is information that indicates the emotional state of a worker, obtained using facial expression recognition, voice analysis, etc.

[1649] "Means for selecting and delivering optimal background music for each work area in real time" refers to methods and technologies for instantly providing optimal background music to each work area based on fluctuations in the work environment and data on workers' emotions.

[1650] MODE FOR CARRYING OUT THE INVENTION

[1651] The system of this invention, "Comfortable Factory BGM Maker," selects and distributes optimal background music in real time based on worker schedule information, factory usage status, and worker emotional data in order to optimize the work environment within a factory.

[1652] System Overview

[1653] The system mainly consists of the following elements:

[1654] 1. Schedule information acquisition means: The server acquires the schedule information of workers through a schedule management tool or calendar API.

[1655] 2. Factory usage status acquisition method: The server acquires usage status data for each work area in real time from sensors and IoT devices within the factory.

[1656] 3. Means for acquiring emotional data: The server uses sensors such as cameras and microphones installed in each work area to analyze the facial expressions and voices of workers and acquire emotional data.

[1657] 4. BGM selection method: The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, and event purpose, as well as the acquired emotional data.

[1658] 5. BGM distribution means: The server distributes the selected background music to the audio systems installed in each work area within the factory and starts playing it in real time.

[1659] 6. Volume adjustment method: The server automatically adjusts the volume based on environmental sounds and feedback from workers.

[1660] 7. Feedback collection method: Users provide feedback on the background music from their devices (smartphones, PCs, etc.), and the server analyzes this feedback to optimize the algorithm.

[1661] Example of operation

[1662] For example, if the emotion engine recognizes that a worker on a factory assembly line is a little tired at 2pm, the "Comfortable Factory BGM Maker" will do the following:

[1663] 1. Data Collection:

[1664] The server retrieves from the scheduling tool that assembly line workers are performing their regular tasks at 2:00 PM.

[1665] Assembly line usage data is collected from sensors within the factory and workers' emotional data is analyzed.

[1666] 2. Data Analysis:

[1667] The server analyzes that it is 2:00 PM and that the worker is working on the assembly line.

[1668] The emotion engine recognizes that the worker is a little tired.

[1669] 3. BGM selection:

[1670] The server determines that upbeat pop music, which has a relaxing effect while maintaining work efficiency, is optimal and selects it.

[1671] 4. BGM distribution:

[1672] The server broadcasts the selected pop music to the assembly line's audio system and begins playing the music.

[1673] Adjust the volume appropriately to play at the optimum volume for your work environment.

[1674] 5. Feedback Collection:

[1675] After completing the task, the user can submit feedback on the background music from their device.

[1676] The server collects feedback data and optimizes the algorithm so that it will be reflected in future BGM selections.

[1677] Prompt Sentence Examples

[1678] It's 2 p.m. in the factory, and the emotion engine recognizes that the assembly line workers are feeling a bit tired. The Comfortable Factory BGM maker selects upbeat pop music that brings positive energy and streams it to the break area.

[1679] Example prompt for a generative AI model:

[1680] What algorithm should be used to select and deliver the optimal background music when workers on a factory assembly line are feeling a little tired at 2 p.m.? Please propose an optimization method based on emotion data and schedule data.

[1681] In this way, the "Comfortable Factory BGM Maker" can provide optimal background music based on workers' emotions and real-time data, creating an effective working environment.

[1682] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1683] Step 1:

[1684] The server obtains worker schedule information from a schedule management tool or calendar API. The input is the API endpoint of the schedule management tool. The output is the worker schedule data, which includes work hours and work content. This process sends an API request and analyzes the obtained data.

[1685] Step 2:

[1686] The server collects real-time usage data for each work area from sensors and IoT devices in the factory. The input is data collected from the sensors and IoT devices. The output is usage data for each work area, and work progress is understood based on this data. This process involves collecting and analyzing raw data from the sensors.

[1687] Step 3:

[1688] The server uses cameras and microphones installed in the work area to capture and analyze emotion data from the worker's facial expressions and voice. The input is real-time video and audio data obtained from the cameras and microphones. The output is data indicating the worker's emotional state, and an emotion engine is used to recognize facial expressions and analyze voice.

[1689] Step 4:

[1690] The server runs an algorithm to select the optimal background music based on the time of day, space usage, participant attributes, event purpose, and acquired emotional data. The input is all the data acquired in the previous step. The output is a list of selected background music, and the optimal music is identified using the background music selection algorithm.

[1691] Step 5:

[1692] The server distributes the selected background music to the audio systems installed in each work area in the factory and starts playing it in real time. The input is the background music music file or streaming URL. The output is music playback in the work area, controlling the audio system to distribute the music.

[1693] Step 6:

[1694] The server automatically adjusts the volume based on environmental noise and feedback from workers. The input is real-time environmental sound data and feedback data. The output is an adjusted volume setting, optimized using a microphone and feedback collection tools.

[1695] Step 7:

[1696] The user provides feedback on the background music from a device (smartphone or PC). The input is the feedback data entered by the user. The output is the feedback data sent to the server, and the feedback is submitted through the device application.

[1697] Step 8:

[1698] The server analyzes the collected feedback and optimizes the algorithm to reflect it in future BGM selections. The input is the feedback data from users. The output is an updated BGM selection algorithm, which is adjusted using a feedback analysis tool.

[1699] 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.

[1700] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1701] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1702] 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.

[1703] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1704] 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.

[1705] 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).

[1706] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1707] 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."

[1708] 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.

[1709] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1710] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1711] 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.

[1712] 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.

[1713] 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.

[1714] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1715] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1716] 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.

[1717] 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.

[1718] 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.

[1719] 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.

[1720] The following is further disclosed regarding the above embodiment.

[1721] (Claim 1)

[1722] a means of obtaining employee schedule information;

[1723] A means for obtaining office usage information;

[1724] A means for selecting optimal background music based on the time of day, space usage, participant attributes, and event purpose;

[1725] A system that includes a means for distributing selected background music to each space within the office.

[1726] (Claim 2)

[1727] 10. The system of claim 1, further comprising means for automatically adjusting the volume of the selected background music.

[1728] (Claim 3)

[1729] 10. The system of claim 1, further comprising means for collecting feedback from users and optimizing the background music selection algorithm based on the feedback.

[1730] "Example 1"

[1731] (Claim 1)

[1732] a means for obtaining employee schedule information;

[1733] A means for obtaining office usage information;

[1734] A means for analyzing the information obtained by the data acquisition means;

[1735] A means for selecting the most suitable music based on the analysis results;

[1736] A system that includes a means for distributing selected music to each space within an office.

[1737] (Claim 2)

[1738] 10. The system of claim 1, wherein the volume of the selected music is automatically adjusted.

[1739] (Claim 3)

[1740] 10. The system of claim 1, wherein the system collects feedback from users and optimizes the music selection algorithm based on the feedback.

[1741] "Application Example 1"

[1742] (Claim 1)

[1743] a means of obtaining employee schedule information;

[1744] A means for acquiring the usage status of the work space;

[1745] A means for selecting optimal background music based on the time of day, space usage, participant attributes, and the purpose of the event;

[1746] A means of distributing the selected background music to each area within the work space;

[1747] A means for acquiring in-vehicle environment information;

[1748] A means for selecting optimal background music based on the time of day, in-car environment information, passenger schedule information, and passenger mood;

[1749] A means of delivering selected background music to the vehicle's sound system

[1750] A system including:

[1751] (Claim 2)

[1752] 10. The system of claim 1, further comprising means for automatically adjusting the volume of the selected background music.

[1753] (Claim 3)

[1754] 10. The system of claim 1, further comprising means for collecting feedback from users and optimizing the background music selection algorithm based on the feedback.

[1755] "Example 2: Combining Emotion Engines"

[1756] (Claim 1)

[1757] a means of obtaining employee schedule information;

[1758] A means for obtaining office usage information;

[1759] means for recognizing a user's emotion;

[1760] A means for selecting optimal background music based on the time of day, the usage status of the space, the attributes of the participants, the purpose of the event, and the emotions of the user;

[1761] A system that includes a means for distributing selected background music to each space within the office.

[1762] (Claim 2)

[1763] 10. The system of claim 1, further comprising means for automatically adjusting the volume of the selected background music.

[1764] (Claim 3)

[1765] 10. The system of claim 1, further comprising means for collecting feedback from a user and optimizing the background music selection algorithm based on the feedback and the user's emotional data.

[1766] "Application example 2 when combining emotion engines"

[1767] (Claim 1)

[1768] a means of obtaining employee schedule information;

[1769] A means for obtaining office usage information;

[1770] A means for selecting optimal background music based on the time of day, space usage, participant attributes, and event purpose;

[1771] A means to distribute the selected background music to each space in the office,

[1772] A means for acquiring and analyzing worker emotion data;

[1773] A system that includes a means to select and distribute the optimal background music for each work area in real time.

[1774] (Claim 2)

[1775] 10. The system of claim 1, further comprising means for automatically adjusting the volume of the selected background music.

[1776] (Claim 3)

[1777] 10. The system of claim 1, further comprising means for collecting feedback from users and optimizing the background music selection algorithm based on the feedback. [Explanation of symbols]

[1778] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means of obtaining employee schedule information; A means for obtaining office usage information; A means for selecting optimal background music based on the time of day, space usage, participant attributes, and event purpose; A system that includes a means for distributing selected background music to each space within the office.

2. 10. The system of claim 1, further comprising means for automatically adjusting the volume of the selected background music.

3. The system of claim 1 , further comprising means for collecting feedback from users and optimizing the background music selection algorithm based on the feedback.

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