system

Abstract

The system according to the embodiment aims to efficiently carry out optimal allocation of event locations and members. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a placement unit, and a provision unit. The reception unit receives image input from a user. The analysis unit analyzes the image received by the reception unit and identifies similar locations. The placement unit calculates appropriate locations and placement of members based on the analysis results obtained by the analysis unit. The provision unit provides the results calculated by the placement unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently allocate optimal locations and members for events.

[0005] The system according to the embodiment aims to efficiently carry out optimal allocation of event locations and members. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a placement unit, and a provision unit. The reception unit receives image input from a user. The analysis unit analyzes the image received by the reception unit and identifies similar locations. The placement unit calculates appropriate locations and placement of members based on the analysis results obtained by the analysis unit. The provision unit provides the results calculated by the placement unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently perform optimal allocation of event locations and members. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An event allocation system according to an embodiment of the present invention utilizes an image analysis system and people flow data to maximize the efficiency of various events, including SB events. This event allocation system allows users to input an image of their ideal event venue into the image analysis system, which then analyzes the image and quickly finds similar locations. Furthermore, people flow data can be used to optimally allocate event locations and participants. For example, optimal location and participant placement for large-scale concerts and exhibitions can improve participant satisfaction. Specifically, a user uploads an image of their ideal event venue, and the image analysis system analyzes the image to identify similar locations. Next, the system calculates the optimal location and participant placement based on past event data and real-time people flow data, and provides the results to the user. This allows users to easily find their ideal event venue and maximize event efficiency by utilizing people flow data. This event allocation system allows users to easily find their ideal event venue and maximize event efficiency by utilizing people flow data.

[0029] An event placement system according to an embodiment includes a reception unit, an analysis unit, a placement unit, and a provision unit. The reception unit receives image input from a user. The image input from the user includes, but is not limited to, image formats such as JPEG, PNG, and GIF, and image types such as landscapes, people, and buildings. The reception unit allows, for example, a user to upload an image of an ideal event venue. The analysis unit analyzes the image received by the reception unit and identifies similar locations. The analysis unit extracts features within the image using, for example, image analysis technology, and searches for similar locations. Image analysis technology includes, but is not limited to, deep learning, computer vision, and pattern recognition. The placement unit calculates an appropriate location and member placement based on the analysis results obtained by the analysis unit. The placement unit calculates the optimal location and member placement based, for example, on past event data and real-time people flow data. Past event data includes, for example, but is not limited to, the number of participants, event ratings, and venue. The real-time people flow data includes, but is not limited to, sensor data, GPS data, social media data, etc. The providing unit provides the results calculated by the arrangement unit to the user. The providing unit provides, for example, optimal location and member arrangement results to the user. The optimal location and member arrangement results include, but are not limited to, layout diagrams, detailed descriptions, and reasons for recommendation. As a result, the event arrangement system according to the embodiment can maximize the efficiency of the event by accepting and analyzing image input from the user, and calculating and providing optimal location and member arrangements.

[0030] The analysis unit can extract features in an image using image analysis technology and search for similar locations. Image analysis technology includes, but is not limited to, deep learning, computer vision, and pattern recognition. The analysis unit can extract features in an image using, for example, deep learning and search for similar locations. Deep learning can learn from large amounts of data and extract features in an image with high accuracy. The analysis unit can also extract features in an image using computer vision and search for similar locations. Computer vision can recognize objects and patterns in an image using image processing technology. The analysis unit can also extract features in an image using pattern recognition and search for similar locations. Pattern recognition is a technology that identifies specific patterns and shapes in an image, allowing for rapid search for similar locations. As a result, image analysis technology can be used to extract features in an image and search for similar locations quickly.

[0031] The placement unit can calculate appropriate locations and placement of members based on past event data or real-time people flow data. Past event data includes, but is not limited to, the number of participants, event ratings, and venue location. The placement unit can calculate appropriate locations and placement of members based on, for example, past event data. Past event data is a valuable source of information for analyzing factors that contribute to the success or failure of an event and is used to calculate optimal placement. The placement unit can also calculate appropriate locations and placement of members based on real-time people flow data. Real-time people flow data includes, but is not limited to, sensor data, GPS data, social media data, and the like. Real-time people flow data is an important source of information for understanding current people flow and congestion conditions and is used to calculate optimal placement. In this way, by utilizing past event data and real-time people flow data, optimal locations and placement of members can be calculated.

[0032] The reception unit allows a user to upload an image of their ideal event space. The image of the ideal event space may include, but is not limited to, characteristics such as size, facilities, and ease of access. The reception unit, for example, provides an interface that allows a user to easily upload an image of their ideal event space. The interface is designed to be intuitive for a user to operate, simplifying the image uploading procedure. The reception unit also has a function to automatically analyze the image uploaded by the user and extract necessary information. This allows a user to easily upload an image of their ideal event space, and the system can identify the optimal location based on the image.

[0033] The providing unit can provide the user with the optimal location and member allocation results. The optimal location and member allocation results include, but are not limited to, for example, an allocation diagram, a detailed description, and reasons for recommendation. The providing unit, for example, has an interface for providing the optimal location and member allocation results to the user. The interface is designed to allow the user to intuitively understand the results and displays the results in a visually easy-to-understand format. The providing unit also has a support function for the user to take specific actions based on the results. For example, the providing unit can provide a function for automatically making necessary resources and arrangements based on the allocation results. This allows the user to easily understand the optimal location and member allocation results and quickly take action.

[0034] The reception unit can analyze the user's past image upload history and select the optimal reception method. The past image upload history includes, for example, upload date and time, image type, frequency, etc., but is not limited to these examples. The reception unit can analyze the user's past image upload history and select the optimal reception method. For example, the reception unit can automatically display the types of images that the user has frequently uploaded in the past as candidates. The reception unit can also preferentially suggest upload methods (audio, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the type of image to be used in a specific time period based on the user's past upload history. In this way, the optimal reception method can be provided by analyzing the user's past upload history.

[0035] When receiving images, the reception unit can filter the images based on the user's current project or field of interest. Examples of the current project or field of interest include, but are not limited to, the content of the project and keywords related to the field of interest. For example, the reception unit can preferentially receive only images related to the user's ongoing project. The reception unit can also automatically filter highly relevant images based on the user's field of interest. The reception unit can also suggest and receive necessary images according to the progress of the user's project. In this way, by filtering images based on the user's project or field of interest, highly relevant images can be preferentially received.

[0036] When receiving images, the reception unit can prioritize receiving highly relevant images in consideration of the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, the reception unit can prioritize receiving images related to the user's current location. The reception unit can also prioritize receiving images of highly relevant events based on the user's geographical location information. The reception unit can also prioritize receiving region-specific images based on the user's location information. This allows for more appropriate images to be acquired by preferentially receiving highly relevant images based on the user's geographical location information.

[0037] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit, for example, preferentially receives related images based on the content of posts made by the user on social media. The reception unit can also analyze the user's social media activity history and suggest highly relevant images. The reception unit can also receive related images by referring to the content posted by the user's followers and friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant images.

[0038] During image analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. The importance of an image includes, but is not limited to, the content of the image, the user's interest, and the intended use. For example, the analysis unit performs a detailed analysis on an image with high importance. The analysis unit can also perform a concise analysis on an image with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the image. By adjusting the level of detail of the analysis based on the importance of the image, efficient analysis is possible.

[0039] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. Image categories include, but are not limited to, categories such as landscapes, people, and buildings. For example, the analysis unit can apply an analysis algorithm dedicated to buildings to an image of a building. The analysis unit can also apply an analysis algorithm dedicated to natural landscapes to an image of a natural landscape. The analysis unit can also apply an analysis algorithm dedicated to people to an image of a person. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the image category.

[0040] During image analysis, the analysis unit can determine the priority of analysis based on the time of image submission. The time of image submission includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit, for example, prioritizes analysis of images submitted earlier. The analysis unit can also postpone images submitted later. The analysis unit can also adjust the analysis schedule depending on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of image submission.

[0041] During image analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. Image relevance includes, but is not limited to, the content of the image, the user's interest, and the purpose of use. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. The analysis unit can also adjust the analysis schedule according to the relevance of the images. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the images.

[0042] The placement unit can improve the accuracy of placement by taking into account the interrelationships between events when calculating placement. Examples of interrelationships between events include, but are not limited to, the content of the events and the relationships between participants. For example, the placement unit places related events close to each other by taking into account the interrelationships between events. The placement unit can also place competing events farther apart by taking into account the interrelationships between events. The placement unit can also calculate an optimal placement by taking into account the interrelationships between events. In this way, the accuracy of placement is improved by taking into account the interrelationships between events.

[0043] The placement unit can perform placement calculations taking into account attribute information of event participants. Participant attribute information includes, but is not limited to, for example, age, gender, and occupation. The placement unit performs optimal placement, for example, according to the age group of the participants. The placement unit can also perform optimal placement according to the interests of the participants. The placement unit can also calculate optimal placement based on the attribute information of the participants. This makes it possible to provide optimal placement by taking into account the attribute information of the event participants.

[0044] The placement unit can perform placement calculations taking into account the geographical distribution of events. Examples of the geographical distribution of events include, but are not limited to, the venue and the residences of participants. The placement unit performs optimal placement, for example, taking into account the geographical distribution of events. The placement unit can also place geographically close events together. The placement unit can also place geographically distant events in a dispersed manner. In this way, optimal placement can be provided by taking into account the geographical distribution of events.

[0045] The placement unit can improve the accuracy of placement by referring to related literature of the event when calculating the placement. Related literature includes, but is not limited to, for example, academic papers, technical reports, and industry reports. The placement unit, for example, performs optimal placement by referring to related literature of the event. The placement unit can also improve the accuracy of placement based on data of related literature. The placement unit can also calculate the optimal placement by utilizing information from related literature. In this way, the accuracy of placement is improved by referring to related literature of the event.

[0046] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. The past operation history includes, for example, operation date and time, operation content, frequency, etc., but is not limited to these examples. For example, the providing unit preferentially provides the display method that the user used in the past. Furthermore, the providing unit can also suggest the optimal display method from the user's past operation history. Furthermore, the providing unit can also provide a customized display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0047] When providing the display information, the providing unit can select the optimal display method by taking into consideration the user's device information. Device information includes, but is not limited to, for example, the device type, OS, and screen size. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The reception unit can analyze the user's past event participation history and suggest the optimal timing for uploading images. For example, the reception unit calculates the optimal upload timing based on the date, time, and frequency of events the user has participated in in the past. The reception unit can also analyze the user's tendency to upload during specific time periods based on the user's past event participation history and send notifications during those time periods. Furthermore, the reception unit can preferentially accept images related to specific events based on the user's past event participation history. This makes it possible to suggest the optimal timing for uploading images by utilizing the user's past event participation history.

[0050] The analysis unit can improve the accuracy of the analysis by taking into account the user's current weather information during image analysis. For example, the analysis unit can consider the influence of weather in image analysis of an outdoor event based on the current weather information. The analysis unit can also adjust lighting conditions in image analysis of an indoor event based on the weather information. Furthermore, the analysis unit can make suggestions to minimize the influence of weather on the selection of an event location based on the weather information. In this way, the accuracy of image analysis can be improved by taking into account the user's current weather information.

[0051] The reception unit can adjust the image reception method taking into account the remaining battery power of the user's device. For example, when the remaining battery power is low, the reception unit can preferentially receive lightweight image formats. Also, when the remaining battery power is sufficient, the reception unit can receive high-resolution images. Furthermore, the reception unit can adjust the image upload speed according to the remaining battery power. This makes it possible to provide an optimal image reception method by taking into account the remaining battery power of the user's device.

[0052] When analyzing images, the analysis unit can improve the accuracy of the analysis by taking into account the user's past search history. For example, the analysis unit prioritizes analysis of related images based on keywords searched for by the user in the past. The analysis unit can also prioritize analysis of images related to a specific category based on the user's past search history. Furthermore, the analysis unit can prioritize analysis of images related to a specific time period based on the user's past search history. In this way, the accuracy of image analysis can be improved by taking into account the user's past search history.

[0053] The providing unit can adjust the method of providing information by taking into account the user's device usage status. For example, the providing unit can reduce notifications when the user uses the device frequently. Alternatively, the providing unit can actively provide notifications when the user does not use the device very often. Furthermore, the providing unit can adjust the frequency of displaying information according to the user's device usage status. This makes it possible to provide an optimal information provision method by taking into account the user's device usage status.

[0054] When analyzing images, the analysis unit can adjust the analysis priority taking into account the user's current activity status. For example, the analysis unit can perform analysis quickly when the user is active. Alternatively, the analysis unit can perform analysis slowly when the user is taking a break. Furthermore, the analysis unit can adjust the analysis schedule according to the user's current activity status. This allows for efficient image analysis by taking into account the user's current activity status.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The reception unit receives image input from the user. The image input from the user includes, for example, image formats such as JPEG, PNG, and GIF, and image types such as landscapes, people, and buildings. The reception unit allows the user to upload an image of their ideal event venue, for example. Step 2: The analysis unit analyzes the image received by the reception unit and identifies similar locations. The analysis unit extracts features in the image using, for example, image analysis technology and searches for similar locations. Image analysis technology includes, for example, deep learning, computer vision, and pattern recognition. Step 3: The placement unit calculates the appropriate location and placement of members based on the analysis results obtained by the analysis unit. The placement unit calculates the optimal location and placement of members based on, for example, past event data and real-time people flow data. Past event data includes, for example, the number of participants, event ratings, and the venue. Real-time people flow data includes, for example, sensor data, GPS data, and social media data. Step 4: The providing unit provides the results calculated by the placement unit to the user. The providing unit provides, for example, the placement results of the optimal locations and members to the user. The placement results of the optimal locations and members may include, for example, a placement diagram, a detailed description, and reasons for recommendation.

[0057] (Example 2) An event allocation system according to an embodiment of the present invention utilizes an image analysis system and people flow data to maximize the efficiency of various events, including SB events. This event allocation system allows users to input an image of their ideal event venue into the image analysis system, which then analyzes the image and quickly finds similar locations. Furthermore, people flow data can be used to optimally allocate event locations and participants. For example, optimal location and participant placement for large-scale concerts and exhibitions can improve participant satisfaction. Specifically, a user uploads an image of their ideal event venue, and the image analysis system analyzes the image to identify similar locations. Next, the system calculates the optimal location and participant placement based on past event data and real-time people flow data, and provides the results to the user. This allows users to easily find their ideal event venue and maximize event efficiency by utilizing people flow data. This event allocation system allows users to easily find their ideal event venue and maximize event efficiency by utilizing people flow data.

[0058] An event placement system according to an embodiment includes a reception unit, an analysis unit, a placement unit, and a provision unit. The reception unit receives image input from a user. The image input from the user includes, but is not limited to, image formats such as JPEG, PNG, and GIF, and image types such as landscapes, people, and buildings. The reception unit allows, for example, a user to upload an image of an ideal event venue. The analysis unit analyzes the image received by the reception unit and identifies similar locations. The analysis unit extracts features within the image using, for example, image analysis technology, and searches for similar locations. Image analysis technology includes, but is not limited to, deep learning, computer vision, and pattern recognition. The placement unit calculates an appropriate location and member placement based on the analysis results obtained by the analysis unit. The placement unit calculates the optimal location and member placement based, for example, on past event data and real-time people flow data. Past event data includes, for example, but is not limited to, the number of participants, event ratings, and venue. The real-time people flow data includes, but is not limited to, sensor data, GPS data, social media data, etc. The providing unit provides the results calculated by the arrangement unit to the user. The providing unit provides, for example, optimal location and member arrangement results to the user. The optimal location and member arrangement results include, but are not limited to, layout diagrams, detailed descriptions, and reasons for recommendation. As a result, the event arrangement system according to the embodiment can maximize the efficiency of the event by accepting and analyzing image input from the user, and calculating and providing optimal location and member arrangements.

[0059] The analysis unit can extract features in an image using image analysis technology and search for similar locations. Image analysis technology includes, but is not limited to, deep learning, computer vision, and pattern recognition. The analysis unit can extract features in an image using, for example, deep learning and search for similar locations. Deep learning can learn from large amounts of data and extract features in an image with high accuracy. The analysis unit can also extract features in an image using computer vision and search for similar locations. Computer vision can recognize objects and patterns in an image using image processing technology. The analysis unit can also extract features in an image using pattern recognition and search for similar locations. Pattern recognition is a technology that identifies specific patterns and shapes in an image, allowing for rapid search for similar locations. As a result, image analysis technology can be used to extract features in an image and search for similar locations quickly.

[0060] The placement unit can calculate appropriate locations and placement of members based on past event data or real-time people flow data. Past event data includes, but is not limited to, the number of participants, event ratings, and venue location. The placement unit can calculate appropriate locations and placement of members based on, for example, past event data. Past event data is a valuable source of information for analyzing factors that contribute to the success or failure of an event and is used to calculate optimal placement. The placement unit can also calculate appropriate locations and placement of members based on real-time people flow data. Real-time people flow data includes, but is not limited to, sensor data, GPS data, social media data, and the like. Real-time people flow data is an important source of information for understanding current people flow and congestion conditions and is used to calculate optimal placement. In this way, by utilizing past event data and real-time people flow data, optimal locations and placement of members can be calculated.

[0061] The reception unit allows a user to upload an image of their ideal event space. The image of the ideal event space may include, but is not limited to, characteristics such as size, facilities, and ease of access. The reception unit, for example, provides an interface that allows a user to easily upload an image of their ideal event space. The interface is designed to be intuitive for a user to operate, simplifying the image uploading procedure. The reception unit also has a function to automatically analyze the image uploaded by the user and extract necessary information. This allows a user to easily upload an image of their ideal event space, and the system can identify the optimal location based on the image.

[0062] The providing unit can provide the user with the optimal location and member allocation results. The optimal location and member allocation results include, but are not limited to, for example, an allocation diagram, a detailed description, and reasons for recommendation. The providing unit, for example, has an interface for providing the optimal location and member allocation results to the user. The interface is designed to allow the user to intuitively understand the results and displays the results in a visually easy-to-understand format. The providing unit also has a support function for the user to take specific actions based on the results. For example, the providing unit can provide a function for automatically making necessary resources and arrangements based on the allocation results. This allows the user to easily understand the optimal location and member allocation results and quickly take action.

[0063] The reception unit can estimate the user's emotion and adjust the timing of image reception based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and adjusts the timing of image reception. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of image reception. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and adjusts the timing of image reception. This adjusts the timing of image reception according to the user's emotion, thereby improving user convenience. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0064] The reception unit can analyze the user's past image upload history and select the optimal reception method. The past image upload history includes, for example, upload date and time, image type, frequency, etc., but is not limited to these examples. The reception unit can analyze the user's past image upload history and select the optimal reception method. For example, the reception unit can automatically display the types of images that the user has frequently uploaded in the past as candidates. The reception unit can also preferentially suggest upload methods (audio, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the type of image to be used in a specific time period based on the user's past upload history. In this way, the optimal reception method can be provided by analyzing the user's past upload history.

[0065] When receiving images, the reception unit can filter the images based on the user's current project or field of interest. Examples of the current project or field of interest include, but are not limited to, the content of the project and keywords related to the field of interest. For example, the reception unit can preferentially receive only images related to the user's ongoing project. The reception unit can also automatically filter highly relevant images based on the user's field of interest. The reception unit can also suggest and receive necessary images according to the progress of the user's project. In this way, by filtering images based on the user's project or field of interest, highly relevant images can be preferentially received.

[0066] The reception unit can estimate the user's emotions and determine the priority of images to be accepted based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and determines the priority of images to be accepted. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of images to be accepted. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and determines the priority of images to be accepted. This allows important images to be preferentially accepted by determining the priority of images according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] When receiving images, the reception unit can prioritize receiving highly relevant images in consideration of the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, and the like. For example, the reception unit can prioritize receiving images related to the user's current location. The reception unit can also prioritize receiving images of highly relevant events based on the user's geographical location information. The reception unit can also prioritize receiving region-specific images based on the user's location information. This allows for more appropriate images to be acquired by preferentially receiving highly relevant images based on the user's geographical location information.

[0068] When receiving an image, the reception unit can analyze the user's social media activity and receive related images. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The reception unit, for example, preferentially receives related images based on the content of posts made by the user on social media. The reception unit can also analyze the user's social media activity history and suggest highly relevant images. The reception unit can also receive related images by referring to the content posted by the user's followers and friends on social media. In this way, by analyzing the user's social media activity, it is possible to preferentially receive highly relevant images.

[0069] The analysis unit can estimate the user's emotions and adjust the image analysis presentation method based on the estimated user emotions. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions and adjusts the image analysis presentation method. The analysis unit can also record the user's voice and estimate emotions using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the image analysis presentation method. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate emotions using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the image analysis presentation method. This allows the user to receive optimal analysis results by adjusting the image analysis presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0070] During image analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the image. The importance of an image includes, but is not limited to, the content of the image, the user's interest, and the intended use. For example, the analysis unit performs a detailed analysis on an image with high importance. The analysis unit can also perform a concise analysis on an image with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the image. By adjusting the level of detail of the analysis based on the importance of the image, efficient analysis is possible.

[0071] When analyzing an image, the analysis unit can apply different analysis algorithms depending on the category of the image. Image categories include, but are not limited to, categories such as landscapes, people, and buildings. For example, the analysis unit can apply an analysis algorithm dedicated to buildings to an image of a building. The analysis unit can also apply an analysis algorithm dedicated to natural landscapes to an image of a natural landscape. The analysis unit can also apply an analysis algorithm dedicated to people to an image of a person. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the image category.

[0072] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the length of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the analysis. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the analysis. This allows the length of the analysis to be adjusted according to the user's emotion, thereby providing optimal analysis results for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] During image analysis, the analysis unit can determine the priority of analysis based on the time of image submission. The time of image submission includes, but is not limited to, for example, the submission date and time, the submission frequency, etc. The analysis unit, for example, prioritizes analysis of images submitted earlier. The analysis unit can also postpone images submitted later. The analysis unit can also adjust the analysis schedule depending on the time of submission. This enables efficient analysis by determining the priority of analysis based on the time of image submission.

[0074] During image analysis, the analysis unit can adjust the order of analysis based on the relevance of the images. Image relevance includes, but is not limited to, the content of the image, the user's interest, and the purpose of use. For example, the analysis unit prioritizes analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. The analysis unit can also adjust the analysis schedule according to the relevance of the images. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the images.

[0075] The placement unit can estimate the user's emotion and adjust the placement criteria based on the estimated user's emotion. For example, the placement unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the placement unit calculates an emotion score based on changes in facial expression and adjusts the placement criteria. The placement unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the placement unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the placement criteria. The placement unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the placement unit calculates an emotion score based on heart rate fluctuations and adjusts the placement criteria. This allows the placement criteria to be adjusted according to the user's emotion, thereby providing an optimal placement for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0076] The placement unit can improve the accuracy of placement by taking into account the interrelationships between events when calculating placement. Examples of interrelationships between events include, but are not limited to, the content of the events and the relationships between participants. For example, the placement unit places related events close to each other by taking into account the interrelationships between events. The placement unit can also place competing events farther apart by taking into account the interrelationships between events. The placement unit can also calculate an optimal placement by taking into account the interrelationships between events. In this way, the accuracy of placement is improved by taking into account the interrelationships between events.

[0077] The placement unit can perform placement calculations taking into account attribute information of event participants. Participant attribute information includes, but is not limited to, for example, age, gender, and occupation. The placement unit performs optimal placement, for example, according to the age group of the participants. The placement unit can also perform optimal placement according to the interests of the participants. The placement unit can also calculate optimal placement based on the attribute information of the participants. This makes it possible to provide optimal placement by taking into account the attribute information of the event participants.

[0078] The placement unit can estimate the user's emotions and adjust the order in which the placement results are displayed based on the estimated user emotions. For example, the placement unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the placement unit calculates an emotion score based on changes in facial expression and adjusts the order in which the placement results are displayed. The placement unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the placement unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the order in which the placement results are displayed. The placement unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the placement unit calculates an emotion score based on fluctuations in heart rate and adjusts the order in which the placement results are displayed. This allows the order in which the placement results are displayed to be optimized for the user by adjusting the order in which the placement results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0079] The placement unit can perform placement calculations taking into account the geographical distribution of events. Examples of the geographical distribution of events include, but are not limited to, the venue and the residences of participants. The placement unit performs optimal placement, for example, taking into account the geographical distribution of events. The placement unit can also place geographically close events together. The placement unit can also place geographically distant events in a dispersed manner. In this way, optimal placement can be provided by taking into account the geographical distribution of events.

[0080] The placement unit can improve the accuracy of placement by referring to related literature of the event when calculating the placement. Related literature includes, but is not limited to, for example, academic papers, technical reports, and industry reports. The placement unit, for example, performs optimal placement by referring to related literature of the event. The placement unit can also improve the accuracy of placement based on data of related literature. The placement unit can also calculate the optimal placement by utilizing information from related literature. In this way, the accuracy of placement is improved by referring to related literature of the event.

[0081] The providing unit can estimate the user's emotions and adjust the display method of the information to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the display method of the information. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the information. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and adjusts the display method of the information. This allows the display method of the information to be optimized for the user by adjusting the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] When providing the display method, the providing unit can select the optimal display method by referring to the user's past operation history. The past operation history includes, for example, operation date and time, operation content, frequency, etc., but is not limited to these examples. For example, the providing unit preferentially provides the display method that the user used in the past. Furthermore, the providing unit can also suggest the optimal display method from the user's past operation history. Furthermore, the providing unit can also provide a customized display method based on the user's operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0083] The providing unit can estimate the user's emotions and adjust the operation procedures for the information to be provided based on the estimated user emotions. For example, the providing unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression and adjusts the operation procedures for the information. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the operation procedures for the information. The providing unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations and adjusts the operation procedures for the information. This allows the operation procedures for the information to be adjusted according to the user's emotions, enabling optimal operation for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] When providing the display information, the providing unit can select the optimal display method by taking into consideration the user's device information. Device information includes, but is not limited to, for example, the device type, OS, and screen size. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. In this way, the optimal display method can be provided by taking into consideration the user's device information. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, placement unit, and provision unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives image input from a user. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and extracts features in the image using image analysis technology to search for similar locations. The placement unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and calculates optimal locations and member placements based on past event data and real-time people flow data. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and provides the optimal location and member placement results to the user. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, placement unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives image input from a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and extracts features in an image using image analysis technology to search for similar locations. The placement unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and calculates optimal locations and member placements based on past event data and real-time people flow data. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the optimal location and member placement results to a user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, placement unit, and provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and receives image input from a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and extracts features in an image using image analysis technology and searches for similar locations. The placement unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and calculates the optimal location and member placement based on past event data and real-time people flow data. The provision unit is realized, for example, by the display 343 of the headset terminal 314 and provides the optimal location and member placement results to the user. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, placement unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives image input from a user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and extracts features in an image using image analysis technology and searches for similar locations. The placement unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and calculates the optimal location and member placement based on past event data and real-time people flow data. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the optimal location and member placement results to the user.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The reception unit can analyze the user's past event participation history and suggest the optimal timing for uploading images. For example, the reception unit calculates the optimal upload timing based on the date, time, and frequency of events the user has participated in in the past. The reception unit can also analyze the user's tendency to upload during specific time periods based on the user's past event participation history and send notifications during those time periods. Furthermore, the reception unit can preferentially accept images related to specific events based on the user's past event participation history. This makes it possible to suggest the optimal timing for uploading images by utilizing the user's past event participation history.

[0087] The analysis unit can improve the accuracy of the analysis by taking into account the user's current weather information during image analysis. For example, the analysis unit can consider the influence of weather in image analysis of an outdoor event based on the current weather information. The analysis unit can also adjust lighting conditions in image analysis of an indoor event based on the weather information. Furthermore, the analysis unit can make suggestions to minimize the influence of weather on the selection of an event location based on the weather information. In this way, the accuracy of image analysis can be improved by taking into account the user's current weather information.

[0088] The placement unit can estimate the user's emotions and adjust placement priorities based on the estimated user emotions. For example, if the user is excited, the placement unit can prioritize placing important events. Also, if the user is relaxed, the placement unit can prioritize placing relaxing events. Furthermore, if the user is feeling stressed, the placement unit can prioritize placing events that will help relieve stress. In this way, by adjusting the placement priorities according to the user's emotions, it is possible to provide an optimal event placement for the user.

[0089] The reception unit can adjust the image reception method taking into account the remaining battery power of the user's device. For example, when the remaining battery power is low, the reception unit can preferentially receive lightweight image formats. Also, when the remaining battery power is sufficient, the reception unit can receive high-resolution images. Furthermore, the reception unit can adjust the image upload speed according to the remaining battery power. This makes it possible to provide an optimal image reception method by taking into account the remaining battery power of the user's device.

[0090] The providing unit can estimate the user's emotions and adjust the level of detail of the information to be provided based on the estimated user's emotions. For example, the providing unit can provide detailed information when the user is excited. The providing unit can also provide concise information when the user is relaxed. Furthermore, the providing unit can also provide information to reduce stress when the user is feeling stressed. In this way, by adjusting the level of detail of the information according to the user's emotions, it is possible to provide optimal information to the user.

[0091] When analyzing images, the analysis unit can improve the accuracy of the analysis by taking into account the user's past search history. For example, the analysis unit prioritizes analysis of related images based on keywords searched for by the user in the past. The analysis unit can also prioritize analysis of images related to a specific category based on the user's past search history. Furthermore, the analysis unit can prioritize analysis of images related to a specific time period based on the user's past search history. In this way, the accuracy of image analysis can be improved by taking into account the user's past search history.

[0092] The placement unit can estimate the user's emotions and adjust the timing of placement based on the estimated user's emotions. For example, the placement unit can quickly perform placement when the user is excited. Alternatively, the placement unit can slowly perform placement when the user is relaxed. Furthermore, the placement unit can adjust the timing of placement to reduce stress when the user is feeling stressed. In this way, by adjusting the timing of placement according to the user's emotions, it is possible to provide an optimal placement for the user.

[0093] The providing unit can adjust the method of providing information by taking into account the user's device usage status. For example, the providing unit can reduce notifications when the user uses the device frequently. Alternatively, the providing unit can actively provide notifications when the user does not use the device very often. Furthermore, the providing unit can adjust the frequency of displaying information according to the user's device usage status. This makes it possible to provide an optimal information provision method by taking into account the user's device usage status.

[0094] When analyzing images, the analysis unit can adjust the analysis priority taking into account the user's current activity status. For example, the analysis unit can perform analysis quickly when the user is active. Alternatively, the analysis unit can perform analysis slowly when the user is taking a break. Furthermore, the analysis unit can adjust the analysis schedule according to the user's current activity status. This allows for efficient image analysis by taking into account the user's current activity status.

[0095] The providing unit can estimate the user's emotions and adjust the format of the information to be provided based on the estimated user's emotions. For example, if the user is excited, the providing unit can provide information in a visually appealing format. If the user is relaxed, the providing unit can also provide information in a simple format. Furthermore, if the user is feeling stressed, the providing unit can also provide information in a format designed to reduce stress. In this way, by adjusting the format of information according to the user's emotions, it is possible to provide optimal information to the user.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The reception unit receives image input from the user. The image input from the user includes, for example, image formats such as JPEG, PNG, and GIF, and image types such as landscapes, people, and buildings. The reception unit allows the user to upload an image of their ideal event venue, for example. Step 2: The analysis unit analyzes the image received by the reception unit and identifies similar locations. The analysis unit extracts features in the image using, for example, image analysis technology and searches for similar locations. Image analysis technology includes, for example, deep learning, computer vision, and pattern recognition. Step 3: The placement unit calculates the appropriate location and placement of members based on the analysis results obtained by the analysis unit. The placement unit calculates the optimal location and placement of members based on, for example, past event data and real-time people flow data. Past event data includes, for example, the number of participants, event ratings, and the venue. Real-time people flow data includes, for example, sensor data, GPS data, and social media data. Step 4: The providing unit provides the results calculated by the placement unit to the user. The providing unit provides, for example, the placement results of the optimal locations and members to the user. The placement results of the optimal locations and members may include, for example, a placement diagram, a detailed description, and reasons for recommendation.

[0098] 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.

[0099] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0100] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0101] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] 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.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

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

[0121] The 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.

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

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

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

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] 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.

[0141] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0142] 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.

[0143] 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.

[0144] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0145] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0146] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0149] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] 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.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] 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.

[0154] 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).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] 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."

[0157] 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.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] 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.

[0165] 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.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] 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.

[0168] 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.

[0169] [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives an image input from a user; an analysis unit that analyzes the image received by the reception unit and identifies similar locations; a placement unit that calculates an appropriate location and placement of members based on the analysis results obtained by the analysis unit; a providing unit that provides the result calculated by the placing unit. A system characterized by:

2. The analysis unit Image analysis technology is used to extract features from images and search for similar locations 2. The system of claim 1.

3. The placement unit Calculate optimal locations and member placements based on historical event data or real-time people flow data 2. The system of claim 1.

4. The reception unit Users upload images of their ideal venue 2. The system of claim 1.

5. The providing unit Providing users with optimal location and member placement results 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the timing of image reception based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past image upload history and select the optimal reception method 2. The system of claim 1.

8. The reception unit Filtering images as they arrive based on the user's current project or area of ​​interest 2. The system of claim 1.

Images