System

Abstract

An object of a system according to an embodiment is to generate and provide a Horror Story that is personalized based on a fear pattern of a user.SOLUTION: A system includes a position information acquiring part, a heart rate measuring part, a fear tendency data generating part, a Horror Story generating part, and a story correcting part. The position information acquisition unit acquires position information. The heart rate measurement unit measures a heart rate. The fear tendency data generating section converts the fear tendency into data on the basis of the heart rate measured by the heart rate measuring section. The Horror Story creating section creates Horror Story on the basis of the fear tendency digitized by the fear tendency digitizing section. The story correcting unit appropriately corrects the Horror Story generated by the Horror Story generating unit based on the reaction of the user.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 techniques have not adequately provided personalized horror stories based on a user's fear tendencies, and there is room for improvement.

[0005] The system according to the embodiment aims to generate and provide personalized horror stories based on the user's fear tendencies. [Means for solving the problem]

[0006] The system according to the embodiment includes a location information acquisition unit, a heart rate measurement unit, a fear tendency data conversion unit, a horror story generation unit, and a story correction unit. The location information acquisition unit acquires location information. The heart rate measurement unit measures the heart rate. The fear tendency data conversion unit converts the fear tendency into data based on the heart rate measured by the heart rate measurement unit. The horror story generation unit generates a horror story based on the fear tendency converted into data by the fear tendency data conversion unit. The story correction unit appropriately modifies the horror story generated by the horror story generation unit based on the user's reaction. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide personalized horror stories based on the user's fear tendencies. [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 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 horror experience providing system according to the embodiment of the present invention is a system that identifies surrounding buildings and objects based on location information and provides a horror experience to a user. This allows the horror experience providing system to provide a horror experience that is individually customized for the user.

[0029] A horror experience providing system according to an embodiment includes a location information acquisition unit, a heart rate measurement unit, a fear tendency data conversion unit, a horror story generation unit, and a story correction unit. The location information acquisition unit acquires location information. For example, the location of the user is identified using GPS. The location information can also be supplemented using Wi-Fi or Bluetooth. The location information acquisition unit can also use map data or sensor data to identify surrounding buildings and objects. The heart rate measurement unit measures the heart rate. For example, the heart rate measurement unit measures the heart rate in real time using a wearable watch. The heart rate measurement unit can also record heart rate fluctuations and provide data for analyzing the fear tendency. The fear tendency data conversion unit converts the fear tendency into data based on the heart rate measured by the heart rate measurement unit. For example, it can analyze sudden increases and fluctuation patterns in the heart rate to identify the user's fear tendency. The fear tendency data conversion unit can also accumulate the user's past data and analyze the user's long-term fear tendency. The horror story generation unit generates a horror story based on the fear tendency converted into data by the fear tendency data conversion unit. For example, a generation AI (text generation AI or multimodal generation AI) is used to generate the most frightening story for the user. The horror story generation unit can also set the setting for the story based on data from the location information acquisition unit. The story modification unit appropriately modifies the horror story generated by the horror story generation unit based on the user's reaction. For example, if the user's heart rate does not increase, the story content is changed to make it more frightening. The story modification unit can also introduce a feedback loop to reflect the user's real-time emotional state. This allows the horror experience provision system according to the embodiment to provide the user with an individually customized horror experience. For example, if the user is in a specific location, a horror story tailored to that location is generated, providing a terrifying experience in real time. Furthermore, the story is appropriately modified based on the user's fear tendency, so the optimal horror experience is always provided.

[0030] The location information acquisition unit can collect the surrounding sound environment using the smartphone's microphone and add realistic sound effects to the horror story. The location information acquisition unit, for example, uses the smartphone's microphone to collect the surrounding sound environment in real time. For example, by recording the sound of the wind, birds chirping, or the sound of distant cars, and incorporating these sounds into the horror story, a more realistic horror experience can be provided. The location information acquisition unit can also detect specific sounds and change the development of the story based on those sounds. For example, it can detect footsteps or the sound of a door opening and closing and generate a story accordingly. In this way, by adding realistic sound effects to the horror story, a more realistic horror experience can be provided.

[0031] The location information acquisition unit can analyze surrounding video in real time using the smartphone camera and set the stage for a horror story based on visual information. The location information acquisition unit, for example, can capture surrounding video in real time using the smartphone camera and analyze the video to set the stage for a horror story. For example, it can capture the characteristics of the location where the user is and generate a story tailored to that location. The location information acquisition unit can also use object recognition technology to detect specific objects and generate a story based on them. For example, it can detect trees, benches, buildings, etc. and generate a story based on them. This makes it possible to provide a more realistic terrifying experience by setting the stage for a horror story based on visual information.

[0032] The heart rate measurement unit collects not only heart rate but also biometric data such as electrodermal response and body temperature, allowing the fear tendency data generation unit to digitize more detailed fear tendency. The heart rate measurement unit, for example, uses a wearable watch to measure not only heart rate but also electrodermal response in real time and analyzes fear tendency based on the data. For example, a sudden increase in electrodermal response indicates that the scene was particularly frightening. The heart rate measurement unit can also measure body temperature and analyze fear tendency based on the data. For example, it can analyze body temperature fluctuations to identify the user's fear tendency. The heart rate measurement unit can also combine these biometric data to digitize more detailed fear tendency. This makes it possible to provide an individually customized fear experience by digitizing more detailed fear tendency.

[0033] The heart rate measurement unit can provide feedback to amplify the user's fear using the vibration function of the wearable watch. For example, the heart rate measurement unit can use the vibration function of the wearable watch to provide vibration feedback to the user at specific scenes in a horror story. For example, adding vibrations at the peak of fear amplifies the sense of fear. The heart rate measurement unit can also provide vibration feedback when the user's heart rate increases. For example, adding vibrations when the heart rate exceeds a certain threshold amplifies the sense of fear. The heart rate measurement unit can also adjust the intensity and pattern of the vibrations to optimize the user's sense of fear. In this way, the vibration function of the wearable watch can amplify the user's fear.

[0034] The fear tendency data generation unit can store data from the wearable watch in the cloud and track changes in fear tendency over the long term. The fear tendency data generation unit, for example, stores heart rate data collected by the wearable watch in the cloud and builds a system for tracking changes in fear tendency over the long term. For example, it compares data with past data to analyze changes in fear responses. The fear tendency data generation unit can also manage data on the cloud and analyze each user's fear tendency. For example, it can analyze fluctuations in fear tendency over a specific period of time and identify the user's fear tendency. The fear tendency data generation unit can also predict the user's fear tendency based on long-term data. In this way, by tracking changes in long-term fear tendency, the user's fear tendency can be understood in more detail.

[0035] The fear tendency data generation unit can add a social function for comparing fear tendencies with other users and identifying common fear elements. The fear tendency data generation unit can add a social function for comparing fear tendencies with other users, for example, using data collected by a wearable watch. For example, changes in heart rate in the same scene can be compared. The fear tendency data generation unit can also share data between users and identify common fear elements. For example, if many users feel fear in a particular scene, the scene can be identified as a common fear element. The fear tendency data generation unit can also allow users to share their fear experiences with each other through the social function. This allows users to compare their fear tendencies with other users and identify common fear elements, thereby providing a more effective fear experience.

[0036] The horror story generation unit can build a database to reflect the user's past scary experiences. The horror story generation unit, for example, builds a database that records the user's past scary experiences and provides that data to the generation AI. For example, scenes and situations in which the user felt scared in the past are stored in the database. The horror story generation unit can also generate stories that reflect the user's past scary experiences based on the database. For example, it recreates scenes in which the user felt scared in the past. The horror story generation unit can also update the database to reflect the latest scary experiences. In this way, by reflecting the user's past scary experiences, it is possible to provide a more individually customized scary experience.

[0037] The story modification unit can construct a database for referring to the user's past scary experiences. The story modification unit, for example, constructs a database that records the user's past scary experiences and modifies the development of the story based on that data. For example, scenes and situations in which the user felt scared in the past are stored in the database. The story modification unit can also generate a story that reflects the user's past scary experiences based on the database. For example, it recreates a scene in which the user felt scared in the past. The story modification unit can also update the database to reflect the latest scary experiences. In this way, by referring to the user's past scary experiences, it is possible to provide a more individually customized scary experience.

[0038] The story modification unit can add elements that refer to the scary experiences of other users. For example, the story modification unit collects data on the scary experiences of other users and modifies the development of the story based on that data. For example, the story can incorporate scenes that many users found scary. The story modification unit can also incorporate horror elements from different cultures or regions. For example, Japanese ghost stories or American urban legends can be incorporated into the story. In this way, by referring to the scary experiences of other users, a more effective scary experience can be provided.

[0039] The story modification unit can provide a variety of horror experiences by incorporating horror elements from different cultures and regions. For example, the story modification unit collects horror elements from different cultures and regions and modifies the development of the story based on that data. For example, Japanese ghost stories and American urban legends can be incorporated into the story. The story modification unit can also combine horror elements from different cultures and regions. For example, it can generate a story that blends horror elements from different cultures. In this way, by incorporating horror elements from different cultures and regions, a variety of horror experiences can be provided.

[0040] The sales department for fear data for each user attribute can provide fear data for each user attribute in combination with detailed demographic information. The sales department for fear data for each user attribute, for example, collects fear data for each user attribute and provides it in combination with detailed demographic information (age, gender, region, etc.). For example, it analyzes what scenes users of a specific age group or gender feel scared in. The sales department for fear data for each user attribute can also provide fear data tailored to specific attributes based on demographic information. For example, it provides fear data specialized for a specific age group or gender. By providing fear data for each user attribute in combination with detailed demographic information, more effective marketing and content development become possible.

[0041] The sales department of fear data for each user attribute can integrate the fear data for each user attribute with other psychological data to perform a more comprehensive analysis of fear tendencies. The sales department of fear data for each user attribute, for example, integrates the fear data for each user attribute with other psychological data (stress level, happiness level, etc.) to perform a comprehensive analysis of fear tendencies. For example, it analyzes what scenes make users with high stress levels feel scared. The sales department of fear data for each user attribute can also provide fear data tailored to specific attributes based on the psychological data. For example, it provides fear data specialized for users with high stress levels. In this way, by integrating the fear data for each user attribute with other psychological data, a more comprehensive analysis of fear tendencies becomes possible.

[0042] The sales department for fear data for each user attribute can customize and provide fear data for each user attribute according to different industries and uses. For example, the sales department for fear data for each user attribute collects fear data for each user attribute and provides it customized according to different industries and uses. For example, it provides scenes that will scare users of a specific age group or gender for the gaming industry. The sales department for fear data for each user attribute can also provide fear data tailored to specific uses. For example, it provides fear data specialized for educational or entertainment uses. This allows for more effective marketing and content development by customizing and providing fear data for each user attribute according to different industries and uses.

[0043] The sales department of fear data for each user attribute can integrate the fear data for each user attribute with other marketing data to provide new market insights. The sales department of fear data for each user attribute can, for example, integrate the fear data for each user attribute with other marketing data (purchase history, browsing history, etc.) to provide new market insights. For example, it can analyze what scenes make users of a specific age group or gender feel scared. The sales department of fear data for each user attribute can also provide market insights tailored to specific attributes based on the marketing data. For example, it can provide market insights specialized for a specific age group or gender. In this way, by integrating the fear data for each user attribute with other marketing data, it can provide new market insights.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The horror experience providing system can further include a database that records the user's past horror experiences. For example, the database can store the horror scenes and situations that the user has experienced in the past, and a new horror story can be generated based on these. The database can also provide data for long-term analysis of the user's fear tendencies. This makes it possible to provide a more individually customized horror experience that reflects the user's past horror experiences.

[0046] The horror experience provision system can also store the user's biometric data in the cloud and track changes in fear tendencies over the long term. For example, heart rate and galvanic skin response data collected by a wearable device can be stored in the cloud and compared with past data to analyze changes in fear responses. Data can also be managed on the cloud to analyze each user's fear tendencies. This makes it possible to track changes in fear tendencies over the long term and understand the user's fear tendencies in more detail.

[0047] The horror experience providing system can also add a social function that allows users to compare their fear tendencies with other users and identify common fear elements. For example, changes in heart rate in the same scene can be compared to identify common fear elements. Data can also be shared between users to identify common fear elements. This allows users to compare their fear tendencies with other users and identify common fear elements, thereby providing a more effective horror experience.

[0048] The horror experience provision system can also incorporate horror elements from different cultures and regions to provide a variety of horror experiences. For example, Japanese ghost stories and American urban legends can be incorporated into the story. It is also possible to combine horror elements from different cultures and regions. This allows for the provision of a variety of horror experiences.

[0049] The horror experience providing system can also build a database to reflect the user's past horror experiences. For example, the database can store the horror scenes and situations that the user has experienced in the past, and generate a new horror story based on this. The database can also provide data for long-term analysis of the user's fear tendencies. This makes it possible to provide a more individually customized horror experience that reflects the user's past horror experiences.

[0050] The horror experience providing system can further integrate the fear data for each user attribute with other psychological data to perform a more comprehensive analysis of fear trends. For example, the fear data for each user attribute can be integrated with other psychological data (stress level, happiness level, etc.) to perform a more comprehensive analysis of fear trends. In this way, by integrating the fear data for each user attribute with other psychological data, a more comprehensive analysis of fear trends becomes possible.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The location information acquisition unit acquires location information. For example, the user's current location is identified using GPS. Location information can also be supplemented using Wi-Fi or Bluetooth. Furthermore, surrounding buildings and objects can be identified using map data and sensor data. Step 2: The heart rate measurement unit measures the heart rate. For example, the heart rate is measured in real time using a wearable watch. The heart rate fluctuations can also be recorded to provide data for analyzing fear tendencies. Step 3: The fear tendency data generation unit digitizes the fear tendency based on the heart rate measured by the heart rate measurement unit. For example, it analyzes sudden increases and fluctuation patterns in the heart rate to identify the user's fear tendency. It is also possible to accumulate the user's past data and analyze the long-term fear tendency. Step 4: The horror story generator generates a horror story based on the fear tendency data collected by the fear tendency data collector. For example, it uses a generation AI (text generation AI or multimodal generation AI) to generate the most frightening story for the user. It can also set the story's setting based on data from the location information collector. Step 5: The story modification unit modifies the horror story generated by the horror story generation unit based on the user's reaction. For example, if the user's heart rate does not increase, the story content may be changed to be more frightening. A feedback loop may also be introduced to reflect the user's real-time emotional state.

[0053] (Example 2) The horror experience providing system according to the embodiment of the present invention is a system that identifies surrounding buildings and objects based on location information and provides a horror experience to a user. This allows the horror experience providing system to provide a horror experience that is individually customized for the user.

[0054] A horror experience providing system according to an embodiment includes a location information acquisition unit, a heart rate measurement unit, a fear tendency data conversion unit, a horror story generation unit, and a story correction unit. The location information acquisition unit acquires location information. For example, the location of the user is identified using GPS. The location information can also be supplemented using Wi-Fi or Bluetooth. The location information acquisition unit can also use map data or sensor data to identify surrounding buildings and objects. The heart rate measurement unit measures the heart rate. For example, the heart rate measurement unit measures the heart rate in real time using a wearable watch. The heart rate measurement unit can also record heart rate fluctuations and provide data for analyzing the fear tendency. The fear tendency data conversion unit converts the fear tendency into data based on the heart rate measured by the heart rate measurement unit. For example, it can analyze sudden increases and fluctuation patterns in the heart rate to identify the user's fear tendency. The fear tendency data conversion unit can also accumulate the user's past data and analyze the user's long-term fear tendency. The horror story generation unit generates a horror story based on the fear tendency converted into data by the fear tendency data conversion unit. For example, a generation AI (text generation AI or multimodal generation AI) is used to generate the most frightening story for the user. The horror story generation unit can also set the setting for the story based on data from the location information acquisition unit. The story modification unit appropriately modifies the horror story generated by the horror story generation unit based on the user's reaction. For example, if the user's heart rate does not increase, the story content is changed to make it more frightening. The story modification unit can also introduce a feedback loop to reflect the user's real-time emotional state. This allows the horror experience provision system according to the embodiment to provide the user with an individually customized horror experience. For example, if the user is in a specific location, a horror story tailored to that location is generated, providing a terrifying experience in real time. Furthermore, the story is appropriately modified based on the user's fear tendency, so the optimal horror experience is always provided.

[0055] The location information acquisition unit can collect the surrounding sound environment using the smartphone's microphone and add realistic sound effects to the horror story. The location information acquisition unit, for example, uses the smartphone's microphone to collect the surrounding sound environment in real time. For example, by recording the sound of the wind, birds chirping, or the sound of distant cars, and incorporating these sounds into the horror story, a more realistic horror experience can be provided. The location information acquisition unit can also detect specific sounds and change the development of the story based on those sounds. For example, it can detect footsteps or the sound of a door opening and closing and generate a story accordingly. In this way, by adding realistic sound effects to the horror story, a more realistic horror experience can be provided.

[0056] The location information acquisition unit can analyze surrounding video in real time using the smartphone camera and set the stage for a horror story based on visual information. The location information acquisition unit, for example, can capture surrounding video in real time using the smartphone camera and analyze the video to set the stage for a horror story. For example, it can capture the characteristics of the location where the user is and generate a story tailored to that location. The location information acquisition unit can also use object recognition technology to detect specific objects and generate a story based on them. For example, it can detect trees, benches, buildings, etc. and generate a story based on them. This makes it possible to provide a more realistic terrifying experience by setting the stage for a horror story based on visual information.

[0057] The location information acquisition unit can use the emotion estimation function to estimate the user's emotions when they are in a specific location and customize a horror story based on those emotions. The location information acquisition unit, for example, uses a smartphone's camera or microphone to analyze the user's facial expressions and tone of voice to estimate the user's emotions when they are in a specific location. For example, if the user is nervous, the location information acquisition unit can change the story to be more frightening based on that emotion. The location information acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) and estimate emotions based on that data. For example, the location information acquisition unit can analyze an increase in heart rate or changes in electrodermal activity to estimate the user's emotions. This allows the horror story to be customized based on the user's emotions, providing a more individually tailored horror experience.

[0058] The heart rate measurement unit collects not only heart rate but also biometric data such as electrodermal response and body temperature, allowing the fear tendency data generation unit to digitize more detailed fear tendency. The heart rate measurement unit, for example, uses a wearable watch to measure not only heart rate but also electrodermal response in real time and analyzes fear tendency based on the data. For example, a sudden increase in electrodermal response indicates that the scene was particularly frightening. The heart rate measurement unit can also measure body temperature and analyze fear tendency based on the data. For example, it can analyze body temperature fluctuations to identify the user's fear tendency. The heart rate measurement unit can also combine these biometric data to digitize more detailed fear tendency. This makes it possible to provide an individually customized fear experience by digitizing more detailed fear tendency.

[0059] The heart rate measurement unit can provide feedback to amplify the user's fear using the vibration function of the wearable watch. For example, the heart rate measurement unit can use the vibration function of the wearable watch to provide vibration feedback to the user at specific scenes in a horror story. For example, adding vibrations at the peak of fear amplifies the sense of fear. The heart rate measurement unit can also provide vibration feedback when the user's heart rate increases. For example, adding vibrations when the heart rate exceeds a certain threshold amplifies the sense of fear. The heart rate measurement unit can also adjust the intensity and pattern of the vibrations to optimize the user's sense of fear. In this way, the vibration function of the wearable watch can amplify the user's fear.

[0060] The fear tendency data generation unit can store data from the wearable watch in the cloud and track changes in fear tendency over the long term. The fear tendency data generation unit, for example, stores heart rate data collected by the wearable watch in the cloud and builds a system for tracking changes in fear tendency over the long term. For example, it compares data with past data to analyze changes in fear responses. The fear tendency data generation unit can also manage data on the cloud and analyze each user's fear tendency. For example, it can analyze fluctuations in fear tendency over a specific period of time and identify the user's fear tendency. The fear tendency data generation unit can also predict the user's fear tendency based on long-term data. In this way, by tracking changes in long-term fear tendency, the user's fear tendency can be understood in more detail.

[0061] The fear tendency data generation unit can add a social function for comparing fear tendencies with other users and identifying common fear elements. The fear tendency data generation unit can add a social function for comparing fear tendencies with other users, for example, using data collected by a wearable watch. For example, changes in heart rate in the same scene can be compared. The fear tendency data generation unit can also share data between users and identify common fear elements. For example, if many users feel fear in a particular scene, the scene can be identified as a common fear element. The fear tendency data generation unit can also allow users to share their fear experiences with each other through the social function. This allows users to compare their fear tendencies with other users and identify common fear elements, thereby providing a more effective fear experience.

[0062] The horror story generation unit can build a database to reflect the user's past scary experiences. The horror story generation unit, for example, builds a database that records the user's past scary experiences and provides that data to the generation AI. For example, scenes and situations in which the user felt scared in the past are stored in the database. The horror story generation unit can also generate stories that reflect the user's past scary experiences based on the database. For example, it recreates scenes in which the user felt scared in the past. The horror story generation unit can also update the database to reflect the latest scary experiences. In this way, by reflecting the user's past scary experiences, it is possible to provide a more individually customized scary experience.

[0063] The horror story generation unit can introduce a feedback loop to reflect the user's real-time emotional state. For example, the horror story generation unit introduces a feedback loop to reflect the user's real-time emotional state when the generation AI generates a horror story. For example, the unit collects the user's emotional data in real time and reflects it in the story. The horror story generation unit can also change the development of the story depending on the user's emotional state. For example, if the user is surprised, the emotion is converted into data to change the story to be more frightening. The horror story generation unit can also use an emotion estimation function to analyze the user's facial expressions and tone of voice to grasp the user's emotional state in real time. This allows the user's real-time emotional state to be reflected, providing a more effective horror experience.

[0064] The horror story generation unit can use the emotion estimation function to analyze the user's emotional state in real time and customize the horror story based on that emotion. For example, the horror story generation unit can use the emotion estimation function to analyze the user's facial expressions and tone of voice to grasp the user's emotional state in real time. For example, if the user is surprised, the horror story generation unit can convert that emotion into data and customize the horror story. The horror story generation unit can also collect the user's biometric data (heart rate and electrodermal activity) and analyze emotions based on that data. For example, the horror story generation unit can analyze an increase in heart rate or changes in electrodermal activity to estimate the user's emotion. This allows the user's emotional state to be analyzed in real time and the horror story to be customized based on that emotion, thereby providing a more individually customized horror experience.

[0065] The story modification unit can introduce a feedback loop to reflect the user's real-time emotional state. The story modification unit, for example, introduces a feedback loop to reflect the user's real-time emotional state when modifying the development of the story. For example, the story modification unit collects user emotional data in real time and reflects it in the story. The story modification unit can also change the development of the story according to the user's emotional state. For example, if the user is surprised, the emotion is converted into data and the development of the story is modified. In this way, by reflecting the user's real-time emotional state, a more effective horror experience can be provided.

[0066] The story modification unit can construct a database for referring to the user's past scary experiences. The story modification unit, for example, constructs a database that records the user's past scary experiences and modifies the development of the story based on that data. For example, scenes and situations in which the user felt scared in the past are stored in the database. The story modification unit can also generate a story that reflects the user's past scary experiences based on the database. For example, it recreates a scene in which the user felt scared in the past. The story modification unit can also update the database to reflect the latest scary experiences. In this way, by referring to the user's past scary experiences, it is possible to provide a more individually customized scary experience.

[0067] The story correction unit can use the emotion estimation function to analyze the user's emotional state in real time and modify the story development based on that emotion. For example, the story correction unit uses the emotion estimation function to analyze the user's facial expressions and tone of voice to grasp the user's emotional state in real time. For example, if the user is surprised, the story correction unit converts that emotion into data and modifies the story development. The story correction unit can also collect the user's biometric data (heart rate and electrodermal activity) and analyze emotions based on that data. For example, the story correction unit analyzes an increase in heart rate or changes in electrodermal activity to estimate the user's emotion. In this way, by analyzing the user's emotional state in real time and modifying the story development based on that emotion, a more effective horror experience can be provided.

[0068] The story modification unit can add elements that refer to the scary experiences of other users. For example, the story modification unit collects data on the scary experiences of other users and modifies the development of the story based on that data. For example, the story can incorporate scenes that many users found scary. The story modification unit can also incorporate horror elements from different cultures or regions. For example, Japanese ghost stories or American urban legends can be incorporated into the story. In this way, by referring to the scary experiences of other users, a more effective scary experience can be provided.

[0069] The story modification unit can provide a variety of horror experiences by incorporating horror elements from different cultures and regions. For example, the story modification unit collects horror elements from different cultures and regions and modifies the development of the story based on that data. For example, Japanese ghost stories and American urban legends can be incorporated into the story. The story modification unit can also combine horror elements from different cultures and regions. For example, it can generate a story that blends horror elements from different cultures. In this way, by incorporating horror elements from different cultures and regions, a variety of horror experiences can be provided.

[0070] The sales department for fear data for each user attribute can provide fear data for each user attribute in combination with detailed demographic information. The sales department for fear data for each user attribute, for example, collects fear data for each user attribute and provides it in combination with detailed demographic information (age, gender, region, etc.). For example, it analyzes what scenes users of a specific age group or gender feel scared in. The sales department for fear data for each user attribute can also provide fear data tailored to specific attributes based on demographic information. For example, it provides fear data specialized for a specific age group or gender. By providing fear data for each user attribute in combination with detailed demographic information, more effective marketing and content development become possible.

[0071] The sales department of fear data for each user attribute can integrate the fear data for each user attribute with other psychological data to perform a more comprehensive analysis of fear tendencies. The sales department of fear data for each user attribute, for example, integrates the fear data for each user attribute with other psychological data (stress level, happiness level, etc.) to perform a comprehensive analysis of fear tendencies. For example, it analyzes what scenes make users with high stress levels feel scared. The sales department of fear data for each user attribute can also provide fear data tailored to specific attributes based on the psychological data. For example, it provides fear data specialized for users with high stress levels. In this way, by integrating the fear data for each user attribute with other psychological data, a more comprehensive analysis of fear tendencies becomes possible.

[0072] The sales department of fear data for each user attribute can use the emotion estimation function to analyze the emotional state of the user in real time and classify the fear data based on that emotion. The sales department of fear data for each user attribute can, for example, use the emotion estimation function to analyze the user's facial expression and tone of voice to grasp the emotional state in real time. For example, if the user is surprised, that emotion is converted into data and the fear data is classified. The sales department of fear data for each user attribute can also collect the user's biometric data (heart rate and electrodermal activity) and analyze emotions based on that data. For example, the increase in heart rate and changes in electrodermal activity can be analyzed to estimate the user's emotion. This allows the sales department to analyze the user's emotional state in real time and classify the fear data based on that emotion, enabling more effective marketing and content development.

[0073] The sales department for fear data for each user attribute can customize and provide fear data for each user attribute according to different industries and uses. For example, the sales department for fear data for each user attribute collects fear data for each user attribute and provides it customized according to different industries and uses. For example, it provides scenes that will scare users of a specific age group or gender for the gaming industry. The sales department for fear data for each user attribute can also provide fear data tailored to specific uses. For example, it provides fear data specialized for educational or entertainment uses. This allows for more effective marketing and content development by customizing and providing fear data for each user attribute according to different industries and uses.

[0074] The sales department of fear data for each user attribute can integrate the fear data for each user attribute with other marketing data to provide new market insights. The sales department of fear data for each user attribute can, for example, integrate the fear data for each user attribute with other marketing data (purchase history, browsing history, etc.) to provide new market insights. For example, it can analyze what scenes make users of a specific age group or gender feel scared. The sales department of fear data for each user attribute can also provide market insights tailored to specific attributes based on the marketing data. For example, it can provide market insights specialized for a specific age group or gender. In this way, by integrating the fear data for each user attribute with other marketing data, it can provide new market insights.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The horror experience providing system can further include a database that records the user's past horror experiences. For example, the database can store the horror scenes and situations that the user has experienced in the past, and a new horror story can be generated based on these. The database can also provide data for long-term analysis of the user's fear tendencies. This makes it possible to provide a more individually customized horror experience that reflects the user's past horror experiences.

[0077] The horror experience providing system can also incorporate a feedback loop to reflect the user's real-time emotional state. For example, the system can collect the user's emotional data in real time and reflect it in the horror story. It can also change the story development depending on the user's emotional state. This allows the system to provide a more effective horror experience that reflects the user's real-time emotional state.

[0078] The horror experience provision system can also store the user's biometric data in the cloud and track changes in fear tendencies over the long term. For example, heart rate and galvanic skin response data collected by a wearable device can be stored in the cloud and compared with past data to analyze changes in fear responses. Data can also be managed on the cloud to analyze each user's fear tendencies. This makes it possible to track changes in fear tendencies over the long term and understand the user's fear tendencies in more detail.

[0079] The horror experience providing system can also add a social function that allows users to compare their fear tendencies with other users and identify common fear elements. For example, changes in heart rate in the same scene can be compared to identify common fear elements. Data can also be shared between users to identify common fear elements. This allows users to compare their fear tendencies with other users and identify common fear elements, thereby providing a more effective horror experience.

[0080] The horror experience providing system can further analyze the user's emotional state in real time and customize the horror story based on that emotion. For example, the emotion estimation function can be used to analyze the user's facial expressions and tone of voice to grasp the user's emotional state in real time. It can also collect the user's biometric data (heart rate and electrodermal activity) and analyze emotions based on that data. This allows the system to analyze the user's emotional state in real time and customize the horror story based on that emotion, thereby providing a more individually customized horror experience.

[0081] The horror experience provision system can also incorporate horror elements from different cultures and regions to provide a variety of horror experiences. For example, Japanese ghost stories and American urban legends can be incorporated into the story. It is also possible to combine horror elements from different cultures and regions. This allows for the provision of a variety of horror experiences.

[0082] The horror experience provision system can also introduce a feedback loop to reflect the user's real-time emotional state. For example, when the generation AI generates a horror story, it can collect the user's emotional data in real time and reflect it in the story. It can also change the development of the story depending on the user's emotional state. This makes it possible to provide a more effective horror experience that reflects the user's real-time emotional state.

[0083] The horror experience providing system can also build a database to reflect the user's past horror experiences. For example, the database can store the horror scenes and situations that the user has experienced in the past, and generate a new horror story based on this. The database can also provide data for long-term analysis of the user's fear tendencies. This makes it possible to provide a more individually customized horror experience that reflects the user's past horror experiences.

[0084] The horror experience providing system can further analyze the user's emotional state in real time and customize the horror story based on that emotion. For example, the emotion estimation function can be used to analyze the user's facial expressions and tone of voice to grasp the user's emotional state in real time. It can also collect the user's biometric data (heart rate and electrodermal activity) and analyze emotions based on that data. This allows the system to analyze the user's emotional state in real time and customize the horror story based on that emotion, thereby providing a more individually customized horror experience.

[0085] The horror experience providing system can further integrate the fear data for each user attribute with other psychological data to perform a more comprehensive analysis of fear trends. For example, the fear data for each user attribute can be integrated with other psychological data (stress level, happiness level, etc.) to perform a more comprehensive analysis of fear trends. In this way, by integrating the fear data for each user attribute with other psychological data, a more comprehensive analysis of fear trends becomes possible.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The location information acquisition unit acquires location information. For example, the user's current location is identified using GPS. Location information can also be supplemented using Wi-Fi or Bluetooth. Furthermore, surrounding buildings and objects can be identified using map data and sensor data. Step 2: The heart rate measurement unit measures the heart rate. For example, the heart rate is measured in real time using a wearable watch. The heart rate fluctuations can also be recorded to provide data for analyzing fear tendencies. Step 3: The fear tendency data generation unit digitizes the fear tendency based on the heart rate measured by the heart rate measurement unit. For example, it analyzes sudden increases and fluctuation patterns in the heart rate to identify the user's fear tendency. It is also possible to accumulate the user's past data and analyze the long-term fear tendency. Step 4: The horror story generator generates a horror story based on the fear tendency data collected by the fear tendency data collector. For example, it uses a generation AI (text generation AI or multimodal generation AI) to generate the most frightening story for the user. It can also set the story's setting based on data from the location information collector. Step 5: The story modification unit modifies the horror story generated by the horror story generation unit based on the user's reaction. For example, if the user's heart rate does not increase, the story content may be changed to be more frightening. A feedback loop may also be introduced to reflect the user's real-time emotional state.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 location information acquisition unit that acquires location information; a heart rate measuring unit that measures a heart rate; a fear tendency data generation unit that generates data on the fear tendency based on the heart rate measured by the heart rate measurement unit; a horror story generation unit that generates a horror story based on the fear tendency data generated by the fear tendency data generation unit; a story correction unit that appropriately corrects the horror story generated by the horror story generation unit based on a user's reaction. A system characterized by:

2. The location information acquisition unit The surrounding sound environment is collected by the smartphone microphone, and realistic sound effects are added to the horror story.

2. The system of claim 1.

3. The heart rate measurement unit Not only heart rate but also biological data such as skin electrodermal response and body temperature are collected, and more detailed fear tendency is converted into data by the fear tendency data conversion unit.

2. The system of claim 1.

4. The horror story generation unit Build a database to reflect the user's past frightening experiences 2. The system of claim 1.

5. The story correction unit Introducing a feedback loop to reflect the user's real-time emotional state 2. The system of claim 1.

6. Sales department of fear data for each user attribute, Analyzing the user's emotional state in real time using an emotion estimation function and classifying the fear data based on the emotion.

2. The system of claim 1.

7. The location information acquisition unit Using an emotion estimation function to estimate the user's emotions when in a specific location and customize the horror story based on those emotions.

2. The system of claim 1.

8. The horror story generation unit Introducing a feedback loop to reflect the user's real-time emotional state 2. The system of claim 1.

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