System

Abstract

An object of a system according to an embodiment is to efficiently generate an optimal arrangement plan in an event venue.SOLUTION: A system includes a data collection part, an analysis part, and an arrangement generation part. The data collection unit collects human flow data. The analysis unit analyzes the human flow data collected by the data collection unit. The arrangement generation part generates an optimum arrangement plan in the event site on the basis of the data analyzed by the analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently generate an optimal layout plan within an event venue.

[0005] The system according to the embodiment aims to efficiently generate an optimal layout plan for an event venue. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a placement generation unit. The data collection unit collects people flow data. The analysis unit analyzes the people flow data collected by the data collection unit. The placement generation unit generates an optimal placement plan for the event venue based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate an optimal layout plan within an event venue. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The event placement optimization system according to an embodiment of the present invention utilizes chat generation AI and people flow data to maximize the efficiency of the placement of locations and members at any event, including SB events. As a result, the event placement optimization system can improve the efficiency of event management and participant satisfaction.

[0029] An event placement optimization system according to an embodiment includes a data collection unit, an analysis unit, and a placement generation unit. The data collection unit collects people flow data. For example, the data collection unit collects data from sensors and cameras installed in various areas of the event venue. The data collection unit can also acquire location information from participants' smartphones. The data collection unit can also collect people flow data from the air using drones. The analysis unit analyzes the people flow data collected by the data collection unit. For example, the analysis unit can analyze the people flow data using a generation AI to predict congestion. The analysis unit can also predict an optimal placement plan by learning from past event data. The analysis unit can also generate an emotionally comfortable placement plan based on participant emotion data. The placement generation unit generates an optimal placement plan within the event venue based on the data analyzed by the analysis unit. For example, the placement generation unit suggests allocating more staff to areas where congestion is expected. The placement generation unit also suggests optimizing the placement of booths and stages, taking into account the characteristics of the SB event and participant behavior. The placement generation unit can also generate placement plans for different event types. As a result, the event placement optimization system according to the embodiment can improve the efficiency of event management and enhance participant satisfaction.

[0030] The data collection unit can collect data from sensors or cameras installed in each area of ​​the event venue. For example, the data collection unit may scan handwritten answers and save them as image data. Then, the image data may be converted into text data using OCR technology. Alternatively, the data collection unit may take a photo of the handwritten answer using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. Alternatively, the data collection unit may write the handwritten answer with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor may detect the movement of the pen and save it as text data. Converting the handwritten answer into digital data in this way makes it easier for the generative AI to analyze it.

[0031] The analysis unit can suggest deploying more staff in areas where congestion is expected. The analysis unit, for example, uses a pen pressure sensor to analyze how an answer sheet is written. For example, it collects data on the strength of pen pressure and extracts the writer's characteristics. The analysis unit also tracks the movement of a digital pen to collect data on the writer's stroke order in order to analyze the stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. The analysis unit also combines the pen pressure and stroke order data to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in pen pressure and stroke order. In this way, by analyzing pen pressure and stroke order, the writer's characteristics can be grasped in detail.

[0032] The analysis unit can propose generating an emotionally comfortable placement plan based on the participants' emotional data. For example, the analysis unit records what students dictate with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The analysis unit also creates a system that recognizes what students dictate in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The analysis unit also uses voice input to convert what students dictate into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. This allows even students who have difficulty writing by hand or typing to submit their answers using voice input.

[0033] The analysis unit can learn from past event data and propose to predict the optimal placement plan. For example, the analysis unit uses image recognition technology to analyze images and diagrams included in answer sheets and convert the content into text data. For example, the content of the diagrams is automatically analyzed and reflected in the evaluation. The analysis unit also analyzes images and diagrams included in answer sheets to build a system that evaluates based on visual information. For example, the content of the images is analyzed and reflected in the evaluation of the answer. The analysis unit also analyzes answer sheets that include images and diagrams and integrates the visual information with text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. In this way, analyzing answer sheets that include images and diagrams enables more detailed evaluation.

[0034] The analysis unit can propose generating placement plans according to different event types. For example, when the generation AI generates a summary, the analysis unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The analysis unit also uses a topic model to understand the context when the generation AI generates a summary. For example, it extracts related keywords and phrases based on the topic model. The analysis unit also builds a system for understanding the context by referring to related background information and topic models when the generation AI generates a summary. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0035] The analysis unit can simulate placement plans and propose the selection of the optimal plan. For example, the analysis unit allows the generation AI to analyze the logical structure of the answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The analysis unit also analyzes the development of arguments in the answer and builds a system where the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The analysis unit also develops an algorithm for the generation AI to analyze the logical structure of the answer and development of arguments and generate a logical summary. For example, it evaluates the logical consistency and importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of the answer and development of arguments.

[0036] The analysis unit can learn from past data of SB events and propose predicting the optimal placement plan. For example, the analysis unit has the generation AI learn from past data of SB events and build a system that predicts the optimal placement plan. For example, the analysis unit proposes a placement plan based on past successful cases. The analysis unit also analyzes past data of SB events and the generation AI predicts the optimal placement plan. For example, it identifies areas where congestion is likely to occur and reflects this in the placement plan. The analysis unit also has the generation AI learn from past data of SB events and predict the optimal placement plan in real time. For example, it predicts congestion based on past data and adjusts the placement plan. In this way, by learning from past data of SB events, it is possible to predict placement plans with greater accuracy.

[0037] The analysis unit can propose generating a customized placement plan based on the attributes of participants in the SB event. For example, the analysis unit has the generation AI analyze the attribute data of participants in the SB event and generate a customized placement plan. For example, it proposes booth placement according to age groups. The analysis unit also has the generation AI propose an optimal placement plan based on participant interest data. For example, it creates an area where participants with specific interests tend to gather. The analysis unit also has the generation AI analyze the attribute data of participants in the SB event and generate a customized placement plan in real time. For example, it creates rest areas according to gender and age. In this way, participant satisfaction is improved by generating a customized placement plan based on the attributes of participants in the SB event.

[0038] The analysis unit can propose generating layout plans according to different SB event types. The analysis unit, for example, builds a system in which a generation AI generates layout plans according to different SB event types. For example, it optimizes booth layout for exhibitions and seating arrangements for seminars. The analysis unit also proposes an optimal layout plan for a workshop using a generation AI. For example, it devise table arrangements to make it easy for participants to interact with each other. The analysis unit also proposes an optimal seating arrangement for a seminar using a generation AI. For example, it makes an arrangement that takes into account participants' line of sight and acoustic effects. In this way, by generating layout plans according to different SB event types, it is possible to achieve an optimal layout that suits the characteristics of each SB event.

[0039] The analysis unit can simulate SB event placement plans and propose the selection of the optimal plan. The analysis unit, for example, constructs a system in which a generation AI simulates SB event placement plans and selects the optimal plan. For example, it compares multiple placement plans and selects the most efficient plan. The analysis unit also has the generation AI propose the optimal placement plan based on the simulation results. For example, it selects a placement that is less likely to cause congestion. The analysis unit also has the generation AI simulate SB event placement plans in real time and select the optimal plan. For example, it dynamically adjusts the placement plan based on the simulation results. In this way, the optimal plan can be selected by simulating SB event placement plans.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The analysis unit can propose generating a customized layout plan based on the attributes of the participants. For example, the analysis unit collects age data of the participants and proposes booth layouts according to age groups. This makes it possible to place activities for children in areas where children are likely to gather. The analysis unit can also provide areas where participants with specific interests are likely to gather based on interest data of the participants. For example, sports-related booths can be provided in areas where participants interested in sports are likely to gather. Furthermore, the analysis unit can collect gender data of the participants and provide rest areas according to gender. This makes it possible to improve the satisfaction of female participants by providing a rest area exclusively for women.

[0042] The analysis unit can propose generating layout plans according to different event types. For example, the analysis unit can optimize booth layout for an exhibition and seating layout for a seminar. This allows for an exhibition to be designed so that participants can move around the booths efficiently. The analysis unit can also design table layouts in workshops to facilitate interaction between participants. For example, circular tables can be arranged to provide an environment where participants can easily start conversations. Furthermore, the analysis unit can perform seating arrangements in seminars that take into account participants' line of sight and acoustics. This allows for an environment where participants can easily hear the lecturer and concentrate.

[0043] The analysis unit can simulate placement plans and suggest the selection of the optimal plan. For example, the analysis unit can simulate multiple placement plans and select the most efficient plan. This makes it possible to select a placement that is less likely to cause congestion. The analysis unit can also dynamically adjust the placement plan based on the simulation results. For example, it can monitor participant movements in real time and allocate staff to areas where congestion is likely to occur. Furthermore, the analysis unit can provide feedback to optimize the placement plan based on the simulation results. This makes it possible to streamline event management and improve participant satisfaction.

[0044] The analysis unit can learn from past event data and propose to predict an optimal placement plan. For example, the analysis unit can propose a placement plan based on past success stories. This makes it possible to generate a more effective placement plan by utilizing past data. The analysis unit can also analyze past event data and identify areas where congestion is likely to occur. This makes it possible to propose a placement plan to avoid congestion. Furthermore, the analysis unit can predict congestion in real time based on past data and adjust the placement plan. This makes it possible to predict a more accurate placement plan by utilizing past data.

[0045] The analysis unit can propose generating a customized layout plan based on the attributes of the participants. For example, the analysis unit collects age data of the participants and proposes booth layouts according to age groups. This makes it possible to place activities for children in areas where children are likely to gather. The analysis unit can also provide areas where participants with specific interests are likely to gather based on interest data of the participants. For example, sports-related booths can be provided in areas where participants interested in sports are likely to gather. Furthermore, the analysis unit can collect gender data of the participants and provide rest areas according to gender. This makes it possible to improve the satisfaction of female participants by providing a rest area exclusively for women.

[0046] The analysis unit can propose generating layout plans according to different event types. For example, the analysis unit can optimize booth layout for an exhibition and seating layout for a seminar. This allows for an exhibition to be designed so that participants can move around the booths efficiently. The analysis unit can also design table layouts in workshops to facilitate interaction between participants. For example, circular tables can be arranged to provide an environment where participants can easily start conversations. Furthermore, the analysis unit can perform seating arrangements in seminars that take into account participants' line of sight and acoustics. This allows for an environment where participants can easily hear the lecturer and concentrate.

[0047] The analysis unit can simulate placement plans and suggest the selection of the optimal plan. For example, the analysis unit can simulate multiple placement plans and select the most efficient plan. This makes it possible to select a placement that is less likely to cause congestion. The analysis unit can also dynamically adjust the placement plan based on the simulation results. For example, it can monitor participant movements in real time and allocate staff to areas where congestion is likely to occur. Furthermore, the analysis unit can provide feedback to optimize the placement plan based on the simulation results. This makes it possible to streamline event management and improve participant satisfaction.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The data collection unit collects people flow data. For example, data is collected from sensors and cameras installed in each area of ​​the event venue. The data collection unit can also obtain location information from participants' smartphones. Furthermore, the data collection unit can also collect people flow data from the air using drones. Step 2: The analysis unit analyzes the people flow data collected by the data collection unit. For example, the analysis unit uses generation AI to analyze the people flow data and predict congestion. The analysis unit can also learn from past event data and predict the optimal layout plan. Furthermore, the analysis unit can generate an emotionally comfortable layout plan based on participant emotional data. Step 3: The placement generation unit generates an optimal placement plan for the event venue based on the data analyzed by the analysis unit. For example, the placement generation unit may suggest allocating more staff to areas where congestion is expected. The placement generation unit may also suggest optimizing the placement of booths and stages, taking into account the characteristics of the event and the trends of participants. Furthermore, the placement generation unit can also generate placement plans for different event types.

[0050] (Example 2) The event placement optimization system according to an embodiment of the present invention utilizes chat generation AI and people flow data to maximize the efficiency of the placement of locations and members at any event, including SB events. As a result, the event placement optimization system can improve the efficiency of event management and participant satisfaction.

[0051] An event placement optimization system according to an embodiment includes a data collection unit, an analysis unit, and a placement generation unit. The data collection unit collects people flow data. For example, the data collection unit collects data from sensors and cameras installed in various areas of the event venue. The data collection unit can also acquire location information from participants' smartphones. The data collection unit can also collect people flow data from the air using drones. The analysis unit analyzes the people flow data collected by the data collection unit. For example, the analysis unit can analyze the people flow data using a generation AI to predict congestion. The analysis unit can also predict an optimal placement plan by learning from past event data. The analysis unit can also generate an emotionally comfortable placement plan based on participant emotion data. The placement generation unit generates an optimal placement plan within the event venue based on the data analyzed by the analysis unit. For example, the placement generation unit suggests allocating more staff to areas where congestion is expected. The placement generation unit also suggests optimizing the placement of booths and stages, taking into account the characteristics of the SB event and participant behavior. The placement generation unit can also generate placement plans for different event types. As a result, the event placement optimization system according to the embodiment can improve the efficiency of event management and enhance participant satisfaction.

[0052] The data collection unit can collect data from sensors or cameras installed in each area of ​​the event venue. For example, the data collection unit may scan handwritten answers and save them as image data. Then, the image data may be converted into text data using OCR technology. Alternatively, the data collection unit may take a photo of the handwritten answer using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app may automatically correct the image and perform character recognition. Alternatively, the data collection unit may write the handwritten answer with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor may detect the movement of the pen and save it as text data. Converting the handwritten answer into digital data in this way makes it easier for the generative AI to analyze it.

[0053] The analysis unit can suggest deploying more staff in areas where congestion is expected. The analysis unit, for example, uses a pen pressure sensor to analyze how an answer sheet is written. For example, it collects data on the strength of pen pressure and extracts the writer's characteristics. The analysis unit also tracks the movement of a digital pen to collect data on the writer's stroke order in order to analyze the stroke order. For example, it analyzes the stroke order pattern and identifies the writer's characteristics. The analysis unit also combines the pen pressure and stroke order data to comprehensively analyze the writer's characteristics. For example, it extracts the writer's characteristics based on the degree of agreement between changes in pen pressure and stroke order. In this way, by analyzing pen pressure and stroke order, the writer's characteristics can be grasped in detail.

[0054] The analysis unit can propose optimizing the placement of booths and stages based on the characteristics of the SB event and the trends of participants. For example, the analysis unit uses a camera to capture the facial expressions of students while they are writing their answers and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in their facial expressions. The analysis unit also records the students' voices while they are writing their answers and estimates their emotions using voice analysis technology. For example, it analyzes the tone and speed of their voices and calculates an emotion score. The analysis unit also uses sensors to collect the students' biometric data (heart rate and electrodermal activity) while they are writing their answers and analyzes their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on fluctuations in heart rate. This allows the students' emotions to be reflected in the evaluation, making it possible to perform a more comprehensive evaluation.

[0055] The analysis unit can propose generating an emotionally comfortable placement plan based on the participants' emotional data. For example, the analysis unit records what students dictate with a microphone and converts it into text data using voice recognition technology. For example, voice recognition software automatically analyzes the voice and saves it as text. The analysis unit also creates a system that recognizes what students dictate in real time and displays it as text data. For example, text is displayed on a screen simultaneously with voice input. The analysis unit also uses voice input to convert what students dictate into text and saves the text data as an answer sheet. For example, voice recognition technology is used to perform highly accurate text conversion. This allows even students who have difficulty writing by hand or typing to submit their answers using voice input.

[0056] The analysis unit can learn from past event data and propose to predict the optimal placement plan. For example, the analysis unit uses image recognition technology to analyze images and diagrams included in answer sheets and convert the content into text data. For example, the content of the diagrams is automatically analyzed and reflected in the evaluation. The analysis unit also analyzes images and diagrams included in answer sheets to build a system that evaluates based on visual information. For example, the content of the images is analyzed and reflected in the evaluation of the answer. The analysis unit also analyzes answer sheets that include images and diagrams and integrates the visual information with text data for evaluation. For example, the visual information is analyzed using image recognition technology and reflected in the evaluation of the answer. In this way, analyzing answer sheets that include images and diagrams enables more detailed evaluation.

[0057] The analysis unit can propose generating a customized placement plan based on the attributes of the participants. For example, the analysis unit captures the facial expressions of students when they read their answer sheets with a camera and analyzes their emotions in real time using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expressions and provides feedback. The analysis unit also records the students' voices when they read their answer sheets and estimates their emotions in real time using voice analysis technology. For example, the analysis unit analyzes the tone and speed of their voices, calculates an emotion score, and provides feedback. The analysis unit also collects biometric data (heart rate and electrodermal activity) of the students when they read their answer sheets with a sensor and analyzes their emotions in real time using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on fluctuations in heart rate and provides feedback. In this way, the analysis unit monitors students' emotions in real time and provides appropriate feedback, thereby improving learning effectiveness.

[0058] The analysis unit can propose generating placement plans according to different event types. For example, when the generation AI generates a summary, the analysis unit automatically collects related background information and refers to it to understand the context. For example, it collects related news articles and academic papers. The analysis unit also uses a topic model to understand the context when the generation AI generates a summary. For example, it extracts related keywords and phrases based on the topic model. The analysis unit also builds a system for understanding the context by referring to related background information and topic models when the generation AI generates a summary. For example, it automatically collects related information and reflects it in the summary. This enables more accurate summaries by referring to background information and topic models to understand the context.

[0059] The analysis unit can simulate placement plans and propose the selection of the optimal plan. For example, the analysis unit allows the generation AI to analyze the logical structure of the answer and generate a logical summary. For example, it analyzes the development of arguments and logical consistency and reflects this in the summary. The analysis unit also analyzes the development of arguments in the answer and builds a system where the generation AI generates a logical summary. For example, it generates a summary based on the importance and relevance of arguments. The analysis unit also develops an algorithm for the generation AI to analyze the logical structure of the answer and development of arguments and generate a logical summary. For example, it evaluates the logical consistency and importance of arguments. This makes it possible to generate a logical summary by analyzing the logical structure of the answer and development of arguments.

[0060] The analysis unit uses the emotion estimation function to generate a placement plan based on the emotions of the participants, and can propose placements that are emotionally satisfying. For example, when the generation AI summarizes, the analysis unit uses the emotion estimation function to capture the emotional nuances of the answer. For example, it generates a summary based on an emotion score. The analysis unit also uses the emotion estimation function to build a system that allows the generation AI to reflect the emotional elements of the answer in the evaluation. For example, it performs the evaluation based on the emotion score. The analysis unit also develops an algorithm that allows the generation AI to use the emotion estimation function to generate a summary that captures the emotional nuances of the answer. For example, it generates a summary based on the emotion score and reflects that in the evaluation. In this way, by generating a summary that captures the emotional nuances, emotional elements can also be reflected in the evaluation.

[0061] The analysis unit can learn from past data of SB events and propose predicting the optimal placement plan. For example, the analysis unit has the generation AI learn from past data of SB events and build a system that predicts the optimal placement plan. For example, the analysis unit proposes a placement plan based on past successful cases. The analysis unit also analyzes past data of SB events and the generation AI predicts the optimal placement plan. For example, it identifies areas where congestion is likely to occur and reflects this in the placement plan. The analysis unit also has the generation AI learn from past data of SB events and predict the optimal placement plan in real time. For example, it predicts congestion based on past data and adjusts the placement plan. In this way, by learning from past data of SB events, it is possible to predict placement plans with greater accuracy.

[0062] The analysis unit can propose generating a customized placement plan based on the attributes of participants in the SB event. For example, the analysis unit has the generation AI analyze the attribute data of participants in the SB event and generate a customized placement plan. For example, it proposes booth placement according to age groups. The analysis unit also has the generation AI propose an optimal placement plan based on participant interest data. For example, it creates an area where participants with specific interests tend to gather. The analysis unit also has the generation AI analyze the attribute data of participants in the SB event and generate a customized placement plan in real time. For example, it creates rest areas according to gender and age. In this way, participant satisfaction is improved by generating a customized placement plan based on the attributes of participants in the SB event.

[0063] The analysis unit can propose generating layout plans according to different SB event types. The analysis unit, for example, builds a system in which a generation AI generates layout plans according to different SB event types. For example, it optimizes booth layout for exhibitions and seating arrangements for seminars. The analysis unit also proposes an optimal layout plan for a workshop using a generation AI. For example, it devise table arrangements to make it easy for participants to interact with each other. The analysis unit also proposes an optimal seating arrangement for a seminar using a generation AI. For example, it makes an arrangement that takes into account participants' line of sight and acoustic effects. In this way, by generating layout plans according to different SB event types, it is possible to achieve an optimal layout that suits the characteristics of each SB event.

[0064] The analysis unit can simulate SB event placement plans and propose the selection of the optimal plan. The analysis unit, for example, constructs a system in which a generation AI simulates SB event placement plans and selects the optimal plan. For example, it compares multiple placement plans and selects the most efficient plan. The analysis unit also has the generation AI propose the optimal placement plan based on the simulation results. For example, it selects a placement that is less likely to cause congestion. The analysis unit also has the generation AI simulate SB event placement plans in real time and select the optimal plan. For example, it dynamically adjusts the placement plan based on the simulation results. In this way, the optimal plan can be selected by simulating SB event placement plans.

[0065] The analysis unit uses the emotion estimation function to generate a placement plan based on the emotions of SB event participants and can propose placements that provide high emotional satisfaction. The analysis unit, for example, uses the emotion estimation function to analyze the emotional data of SB event participants and generate a placement plan that provides high emotional satisfaction. For example, by providing rest areas in areas where positive emotions are strong. The analysis unit also uses the generation AI to propose a placement plan that will allow SB event participants to spend time comfortably based on the emotion data. For example, by placing more staff in areas with high emotion scores. The analysis unit also uses the emotion estimation function to analyze the emotional data of SB event participants in real time and generate a placement plan that provides high emotional satisfaction. For example, the placement plan is dynamically adjusted according to changes in emotions. In this way, by generating a placement plan based on the emotions of SB event participants, a placement that provides high emotional satisfaction is realized.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The analysis unit can propose generating an emotionally comfortable placement plan based on participants' emotional data. For example, the analysis unit collects participants' facial expression data using a camera and analyzes their emotions using an emotion estimation algorithm. This allows the analysis unit to identify areas where participants are relaxing and provide rest spaces in those areas. The analysis unit also collects participants' voice data and estimates their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of their voices to identify areas where participants are excited and provide activities in those areas. The analysis unit also collects participants' biometric data (heart rate and electrodermal activity) using sensors and analyzes their emotions using an emotion estimation algorithm. This allows the analysis unit to identify areas where participants are feeling stressed and provide relaxation spaces in those areas.

[0068] The analysis unit can propose generating a customized layout plan based on the attributes of the participants. For example, the analysis unit collects age data of the participants and proposes booth layouts according to age groups. This makes it possible to place activities for children in areas where children are likely to gather. The analysis unit can also provide areas where participants with specific interests are likely to gather based on interest data of the participants. For example, sports-related booths can be provided in areas where participants interested in sports are likely to gather. Furthermore, the analysis unit can collect gender data of the participants and provide rest areas according to gender. This makes it possible to improve the satisfaction of female participants by providing a rest area exclusively for women.

[0069] The analysis unit can propose generating layout plans according to different event types. For example, the analysis unit can optimize booth layout for an exhibition and seating layout for a seminar. This allows for an exhibition to be designed so that participants can move around the booths efficiently. The analysis unit can also design table layouts in workshops to facilitate interaction between participants. For example, circular tables can be arranged to provide an environment where participants can easily start conversations. Furthermore, the analysis unit can perform seating arrangements in seminars that take into account participants' line of sight and acoustics. This allows for an environment where participants can easily hear the lecturer and concentrate.

[0070] The analysis unit can simulate placement plans and suggest the selection of the optimal plan. For example, the analysis unit can simulate multiple placement plans and select the most efficient plan. This makes it possible to select a placement that is less likely to cause congestion. The analysis unit can also dynamically adjust the placement plan based on the simulation results. For example, it can monitor participant movements in real time and allocate staff to areas where congestion is likely to occur. Furthermore, the analysis unit can provide feedback to optimize the placement plan based on the simulation results. This makes it possible to streamline event management and improve participant satisfaction.

[0071] The analysis unit uses the emotion estimation function to generate a placement plan based on the emotions of the participants and can propose placements that provide high emotional satisfaction. For example, the analysis unit can provide rest areas in areas where participants have strong positive emotions based on the emotion data of the participants. This makes it possible to provide an area where participants can relax. The analysis unit can also allocate more staff to areas with high emotion scores. This makes it possible to provide an environment where participants can feel comfortable. Furthermore, the analysis unit can dynamically adjust the placement plan in response to changes in emotions. This makes it possible to realize a placement that provides high emotional satisfaction by generating a placement plan based on the emotions of the participants.

[0072] The analysis unit can learn from past event data and propose to predict an optimal placement plan. For example, the analysis unit can propose a placement plan based on past success stories. This makes it possible to generate a more effective placement plan by utilizing past data. The analysis unit can also analyze past event data and identify areas where congestion is likely to occur. This makes it possible to propose a placement plan to avoid congestion. Furthermore, the analysis unit can predict congestion in real time based on past data and adjust the placement plan. This makes it possible to predict a more accurate placement plan by utilizing past data.

[0073] The analysis unit can propose generating an emotionally comfortable placement plan based on participants' emotional data. For example, the analysis unit collects participants' facial expression data using a camera and analyzes their emotions using an emotion estimation algorithm. This allows the analysis unit to identify areas where participants are relaxing and provide rest spaces in those areas. The analysis unit also collects participants' voice data and estimates their emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of their voices to identify areas where participants are excited and provide activities in those areas. The analysis unit also collects participants' biometric data (heart rate and electrodermal activity) using sensors and analyzes their emotions using an emotion estimation algorithm. This allows the analysis unit to identify areas where participants are feeling stressed and provide relaxation spaces in those areas.

[0074] The analysis unit can propose generating a customized layout plan based on the attributes of the participants. For example, the analysis unit collects age data of the participants and proposes booth layouts according to age groups. This makes it possible to place activities for children in areas where children are likely to gather. The analysis unit can also provide areas where participants with specific interests are likely to gather based on interest data of the participants. For example, sports-related booths can be provided in areas where participants interested in sports are likely to gather. Furthermore, the analysis unit can collect gender data of the participants and provide rest areas according to gender. This makes it possible to improve the satisfaction of female participants by providing a rest area exclusively for women.

[0075] The analysis unit can propose generating layout plans according to different event types. For example, the analysis unit can optimize booth layout for an exhibition and seating layout for a seminar. This allows for an exhibition to be designed so that participants can move around the booths efficiently. The analysis unit can also design table layouts in workshops to facilitate interaction between participants. For example, circular tables can be arranged to provide an environment where participants can easily start conversations. Furthermore, the analysis unit can perform seating arrangements in seminars that take into account participants' line of sight and acoustics. This allows for an environment where participants can easily hear the lecturer and concentrate.

[0076] The analysis unit can simulate placement plans and suggest the selection of the optimal plan. For example, the analysis unit can simulate multiple placement plans and select the most efficient plan. This makes it possible to select a placement that is less likely to cause congestion. The analysis unit can also dynamically adjust the placement plan based on the simulation results. For example, it can monitor participant movements in real time and allocate staff to areas where congestion is likely to occur. Furthermore, the analysis unit can provide feedback to optimize the placement plan based on the simulation results. This makes it possible to streamline event management and improve participant satisfaction.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The data collection unit collects people flow data. For example, data is collected from sensors and cameras installed in each area of ​​the event venue. The data collection unit can also obtain location information from participants' smartphones. Furthermore, the data collection unit can also collect people flow data from the air using drones. Step 2: The analysis unit analyzes the people flow data collected by the data collection unit. For example, the analysis unit uses generation AI to analyze the people flow data and predict congestion. The analysis unit can also learn from past event data and predict the optimal layout plan. Furthermore, the analysis unit can generate an emotionally comfortable layout plan based on participant emotional data. Step 3: The placement generation unit generates an optimal placement plan for the event venue based on the data analyzed by the analysis unit. For example, the placement generation unit may suggest allocating more staff to areas where congestion is expected. The placement generation unit may also suggest optimizing the placement of booths and stages, taking into account the characteristics of the event and the trends of participants. Furthermore, the placement generation unit can also generate placement plans for different event types.

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] 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.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 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 system equipped with a generative AI, a data collection unit that collects people flow data; an analysis unit that analyzes the people flow data collected by the data collection unit; and a layout generation unit that generates an optimal layout plan within the event venue based on the data analyzed by the analysis unit. A system characterized by:

2. The data collection unit Collect data from sensors or cameras installed in various areas of the event venue 2. The system of claim 1.

3. The analysis unit Propose the optimal layout of booths and stages based on the characteristics of the SB event and the trends of participants.

2. The system of claim 1.

4. The analysis unit We propose to generate such placement plans according to different event types.

2. The system of claim 1.

5. The analysis unit The arrangement plan is generated based on the emotions of the participants, and an arrangement that is emotionally satisfying is proposed.

2. The system of claim 1.

Images