system

Abstract

The system according to the embodiment aims to collect and analyze different perspectives and emotions, and generate new perspectives and thoughts. [Solution] A system according to an embodiment includes a setting unit, a collection unit, an analysis unit, and a generation unit. The setting unit sets the characters. The collection unit collects information from the viewpoints of the characters set by the setting unit. The analysis unit analyzes the information collected by the collection unit. The generation unit generates viewpoints and thoughts based on the information analyzed by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of effectively collecting and analyzing different perspectives and emotions from people who share the same experiences and memories, and generating new perspectives and thoughts.

[0005] The system according to the embodiment aims to collect and analyze different perspectives and emotions, and generate new perspectives and thoughts. [Means for solving the problem]

[0006] The system according to the embodiment includes a setting unit, a collection unit, an analysis unit, and a generation unit. The setting unit sets the characters. The collection unit collects information from the viewpoints of the characters set by the setting unit. The analysis unit analyzes the information collected by the collection unit. The generation unit generates viewpoints and thoughts based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect and analyze different perspectives and emotions, and generate new perspectives and thoughts. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​system according to an embodiment of the present invention is a system that generates new perspectives and feelings by connecting different viewpoints, based on the premise that people who have the same experiences or memories have different perspectives and emotions. This AI system sets up characters, collects information from each of their perspectives, analyzes the collected information, and generates new perspectives and feelings by connecting different perspectives and emotions. For example, regarding an episode that a child tells their parents, the system collects the child's perspective, the parents' perspective, and the child's siblings' perspectives. The AI ​​analyzes these perspectives and generates new perspectives and feelings by connecting different perspectives and emotions. This activates communication among family members and leads to a deeper understanding. In this way, the AI ​​system can activate communication by connecting different perspectives and emotions to generate new perspectives and feelings.

[0029] The AI ​​system according to this embodiment comprises a setting unit, a collection unit, an analysis unit, and a generation unit. The setting unit sets up the characters. Character settings include, for example, names, ages, personalities, and backgrounds, but are not limited to these examples. The collection unit collects information from each perspective of the characters set up by the setting unit. The information collected includes, for example, visual information, auditory information, and emotional information, but is not limited to these examples. The collection unit collects, for example, the perspectives and emotions of the characters. For example, the collection unit can collect the perspectives of the characters using cameras and microphones. The collection unit can also collect the emotions of the characters using sensors and emotion analysis algorithms. The analysis unit analyzes the information collected by the collection unit. Analysis includes, for example, text analysis, emotion analysis, and data mining, but is not limited to these examples. The analysis unit analyzes the collected information and connects different perspectives and emotions. For example, the analysis unit can extract commonalities and adjust differences. The generation unit generates new perspectives and thoughts based on the information analyzed by the analysis unit. The generation process may utilize, but is not limited to, generation algorithms or emotion models. The generation unit may, for example, generate new perspectives or feelings. For example, the generation unit may provide the generated perspectives or feelings to the user. For example, the generation unit may include an interface for displaying the generated perspectives or feelings. This allows the AI ​​system according to the embodiment to connect different perspectives and feelings to generate new perspectives and feelings, thereby stimulating communication.

[0030] The data collection unit can collect the perspectives and emotions of the characters. For example, the data collection unit can collect the perspectives of the characters using cameras and microphones. For example, the data collection unit can capture the perspectives of the characters with a camera and collect visual information. The data collection unit can also record the perspectives of the characters with a microphone and collect auditory information. For example, the data collection unit can collect the perspectives of the characters as audio data. The data collection unit can also collect the emotions of the characters using sensors and emotion analysis algorithms. For example, the data collection unit can measure the emotions of the characters with sensors and collect emotion data. The data collection unit can also analyze the emotions of the characters with emotion analysis algorithms and collect emotion data. For example, the data collection unit can collect the emotions of the characters as text data. In this way, the data collection unit can obtain more diverse information by collecting the perspectives and emotions of the characters.

[0031] The analysis unit can analyze collected information and connect different perspectives and emotions. For example, the analysis unit can analyze collected information using text analysis and sentiment analysis. For instance, it can analyze collected text data and extract commonalities and differences. The analysis unit can also analyze collected sentiment data and understand changes in emotions. For example, it can identify patterns of emotional change based on sentiment data. Furthermore, the analysis unit can analyze collected information using data mining techniques. For example, it can use data mining techniques to extract useful patterns and relationships from collected information. As a result, the analysis unit can improve its accuracy in generating new perspectives and thoughts by connecting different perspectives and emotions.

[0032] The generation unit can provide the generated viewpoints and thoughts to the user. The generation unit, for example, includes an interface for displaying the generated viewpoints and thoughts. For example, the generation unit displays the generated viewpoints and thoughts on a screen. The generation unit can also provide the generated viewpoints and thoughts as audio. For example, the generation unit provides the generated viewpoints and thoughts as audio using voice synthesis technology. The generation unit can also provide the generated viewpoints and thoughts as text data. For example, the generation unit transmits the generated viewpoints and thoughts to the user as a text message. In this way, the generation unit contributes to revitalizing communication by providing the generated viewpoints and thoughts to the user.

[0033] The collection unit can collect information based on the settings of the characters. The collection unit, for example, collects related information based on the settings of the characters. For example, the collection unit collects information based on the names and ages of the characters. The collection unit can also collect information based on the personalities and backgrounds of the characters. For example, the collection unit collects information related to the personalities of the characters. The collection unit can also collect information related to the backgrounds of the characters. For example, the collection unit collects information related to the past experiences and memories of the characters. In this way, the collection unit can obtain more relevant information by collecting information based on the settings of the characters.

[0034] The setting unit can analyze the character's past behavior history and select an appropriate setting method. The setting unit, for example, analyzes the character's past behavior history and selects the optimal setting method. For example, the setting unit adjusts the setting content based on places the character has frequently visited in the past. The setting unit can also analyze the character's past behavior patterns and select the optimal setting method. For example, the setting unit analyzes the character's past behavior patterns and selects the optimal setting method. The setting unit can also predict the character's behavior during a specific time period based on the character's past behavior history and adjust the setting content. For example, the setting unit predicts the character's behavior during a specific time period based on the character's past behavior history and adjusts the setting content. In this way, the setting unit can select a more optimal setting method by analyzing the character's past behavior history.

[0035] The setting unit can customize the settings based on the character's current situation and areas of interest. The setting unit, for example, customizes the settings based on the character's current situation and areas of interest. For example, the setting unit customizes the settings based on a project the character is currently working on. The setting unit can also provide related setting content based on the character's current areas of interest. For example, the setting unit provides information related to the character's current areas of interest. The setting unit can also adjust the setting content based on the character's current situation (e.g., at work, on vacation, etc.). For example, if the character is at work, the setting unit provides work-related setting content. If the character is on vacation, the setting unit can provide relaxing setting content. This allows the setting unit to customize the settings based on the current situation and areas of interest, thereby enabling more appropriate settings.

[0036] The settings unit can prioritize relevant settings by considering the geographical location of the characters during the setting process. For example, if a character is in a specific region, the settings unit will prioritize settings related to that region. The settings unit can also adjust settings based on information about the travel destination if a character is traveling. For example, if a character is traveling, the settings unit will adjust settings based on information about the travel destination. The settings unit can also prioritize settings related to the home if a character is at home. For example, if a character is at home, the settings unit will prioritize settings related to the home. In this way, the settings unit can create more relevant settings by considering geographical location.

[0037] The settings unit can analyze the characters' social media activity during the setup process and make relevant settings. For example, the settings unit can analyze the characters' social media activity and adjust the settings. For example, the settings unit can adjust the settings based on information shared by the characters on social media. The settings unit can also make relevant settings based on information about accounts that the characters follow on social media. For example, the settings unit can adjust the settings based on information about accounts that the characters follow. The settings unit can also analyze the characters' social media activity history and make optimal settings. For example, the settings unit makes optimal settings based on the characters' social media activity history. This allows the settings unit to make more relevant settings by analyzing social media activity.

[0038] The data collection unit can analyze the characters' past information submission history and select an appropriate collection method. For example, the data collection unit can analyze the characters' past information submission history and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on the format of the information the characters have submitted in the past. The data collection unit can also select the most efficient collection method from the characters' past information submission history. For example, the data collection unit can select the most efficient collection method based on the characters' past information submission history. The data collection unit can also analyze the frequency of information the characters have submitted in the past and select the optimal collection timing. For example, the data collection unit can select the optimal collection timing based on the frequency of information submissions in the past. In this way, the data collection unit can select a more optimal collection method by analyzing past information submission history.

[0039] The data collection unit can filter information based on the characters' current projects and areas of interest. For example, the data collection unit can filter information based on the characters' current projects and areas of interest. For instance, the data collection unit prioritizes collecting information related to the projects the characters are currently working on. The data collection unit can also filter relevant information based on the characters' current areas of interest. For example, the data collection unit filters information related to the characters' current areas of interest. The data collection unit can also select and collect necessary information based on the characters' current situation. For example, the data collection unit selects and collects necessary information based on the characters' current situation. This allows the data collection unit to collect more relevant information by filtering based on current projects and areas of interest.

[0040] The data collection unit can prioritize collecting highly relevant information by considering the geographical location of the characters when gathering information. For example, if a character is in a specific region, the data collection unit will prioritize collecting information related to that region. Also, if a character is traveling, the data collection unit can collect information based on information about their travel destination. For example, if a character is traveling, the data collection unit will prioritize collecting information based on information about their travel destination. Also, if a character is at home, the data collection unit can prioritize collecting information related to their home. For example, if a character is at home, the data collection unit will prioritize collecting information related to their home. In this way, the data collection unit can collect more relevant information by considering geographical location.

[0041] The data collection unit can analyze the social media activities of the characters and collect relevant information when gathering data. For example, the data collection unit can analyze the social media activities of the characters and collect information. For example, the data collection unit can collect relevant information based on information shared by the characters on social media. The data collection unit can also collect relevant information based on information about accounts that the characters follow on social media. For example, the data collection unit can collect relevant information based on information about accounts that the characters follow. The data collection unit can also analyze the activity history of the characters on social media and collect the most relevant information. For example, the data collection unit can collect the most relevant information based on the activity history of the characters on social media. In this way, the data collection unit can collect more relevant information by analyzing social media activities.

[0042] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between pieces of information during the analysis process. For example, the analysis unit analyzes the interrelationships of collected information to improve the accuracy of the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information based on the interrelationships of the collected information. The analysis unit can also apply algorithms to improve the accuracy of the analysis based on the interrelationships of the information. For example, the analysis unit applies algorithms to improve the accuracy of the analysis based on the interrelationships of the information. As a result, the analysis unit improves the accuracy of its analysis by considering the interrelationships of the information.

[0043] The analysis unit can perform analysis while considering the attribute information of the characters. For example, the analysis unit can perform analysis while considering the attribute information of the characters. For example, the analysis unit can perform analysis while considering attribute information such as the age and gender of the characters. The analysis unit can also improve the accuracy of the analysis based on attribute information such as the occupation and hobbies of the characters. For example, the analysis unit can improve the accuracy of the analysis based on attribute information such as the occupation and hobbies of the characters. The analysis unit can also select the optimal analysis method based on the attribute information of the characters. For example, the analysis unit selects the optimal analysis method based on the attribute information of the characters. As a result, the analysis unit can perform more appropriate analysis by considering attribute information.

[0044] The analysis unit can perform analysis while considering the geographical distribution of information. For example, the analysis unit can perform analysis based on the geographical distribution of information. For example, the analysis unit can improve the accuracy of the analysis based on the geographical distribution of information. The analysis unit can also prioritize the analysis of highly relevant information by considering the geographical distribution. For example, the analysis unit prioritizes the analysis of highly relevant information by considering the geographical distribution. The analysis unit can also select the optimal analysis method based on the geographical distribution. For example, the analysis unit selects the optimal analysis method based on the geographical distribution. As a result, the analysis unit can perform more appropriate analysis by considering the geographical distribution.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit performs the analysis by referring to relevant literature. For example, the analysis unit improves the accuracy of its analysis by referring to relevant literature during the analysis. The analysis unit can also apply algorithms to improve the accuracy of its analysis based on relevant literature. For example, the analysis unit applies algorithms to improve the accuracy of its analysis based on relevant literature. The analysis unit can also select the optimal analysis method by referring to relevant literature. For example, the analysis unit selects the optimal analysis method by referring to relevant literature. As a result, the accuracy of the analysis is improved by the analysis unit referring to relevant literature.

[0046] The generation unit can improve the accuracy of generation by considering the interrelationships of information during generation. For example, the generation unit can analyze the interrelationships of collected information to improve generation accuracy. For example, the generation unit can prioritize generating highly relevant information based on the interrelationships of collected information. The generation unit can also apply algorithms to improve generation accuracy based on the interrelationships of information. For example, the generation unit can apply algorithms to improve generation accuracy based on the interrelationships of information. As a result, the generation unit improves generation accuracy by considering the interrelationships of information.

[0047] The generation unit can consider the attribute information of the characters during the generation process. For example, the generation unit can consider the attribute information of the characters when generating content. For example, the generation unit can consider attribute information such as the age and gender of the characters to generate appropriate perspectives and thoughts. The generation unit can also generate highly relevant perspectives and thoughts based on attribute information such as the occupation and hobbies of the characters. For example, the generation unit can generate highly relevant perspectives and thoughts based on attribute information such as the occupation and hobbies of the characters. The generation unit can also select the optimal generation method based on the attribute information of the characters. For example, the generation unit selects the optimal generation method based on the attribute information of the characters. As a result, the generation unit can generate more appropriate perspectives and thoughts by considering attribute information.

[0048] The generation unit can generate information taking into consideration the geographical distribution of the information. The generation unit generates information based on, for example, the geographical distribution of the information. For example, the generation unit improves the accuracy of generation based on the geographical distribution of the information. The generation unit can also generate highly relevant information preferentially by taking the geographical distribution into consideration. For example, the generation unit generates highly relevant information preferentially by taking the geographical distribution into consideration. The generation unit can also select an optimal generation method based on the geographical distribution. For example, the generation unit selects an optimal generation method based on the geographical distribution. In this way, the generation unit can generate more appropriate perspectives and thoughts by taking the geographical distribution into consideration.

[0049] The generation unit can improve the accuracy of generation by referring to related literature during generation. The generation unit, for example, performs generation by referring to related literature. For example, the generation unit improves the accuracy of generation by referring to related literature during generation. The generation unit can also apply an algorithm for improving the accuracy of generation based on the related literature. For example, the generation unit applies an algorithm for improving the accuracy of generation based on the related literature. The generation unit can also select an optimal generation method by referring to related literature. For example, the generation unit selects an optimal generation method by referring to related literature. As a result, the generation unit improves the accuracy of generation by referring to related literature.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The setting unit can analyze the characters' past behavioral history and select an appropriate setting method. For example, the setting unit adjusts the setting based on places the characters have frequently visited in the past. The setting unit can also analyze the characters' past behavioral patterns and select the optimal setting method. For example, the setting unit analyzes the characters' past behavioral patterns and selects the optimal setting method. The setting unit can also predict actions the characters will take during specific time periods based on their past behavioral history and adjust the setting accordingly. For example, the setting unit predicts actions the characters will take during specific time periods based on their past behavioral history and adjusts the setting accordingly. In this way, the setting unit can select a more optimal setting method by analyzing past behavioral history.

[0052] The data collection unit can customize settings based on the characters' current situation and areas of interest. For example, the unit can customize settings based on the project the character is currently working on. The unit can also provide relevant settings based on the characters' current areas of interest. For example, the unit can provide information related to the characters' current areas of interest. The unit can also adjust settings based on the characters' current situation (e.g., at work, on vacation). For example, if the character is at work, the unit will provide work-related settings. If the character is on vacation, the unit can also provide relaxing settings. This allows the data collection unit to provide more appropriate settings by customizing them based on the character's current situation and areas of interest.

[0053] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between pieces of information during analysis. For example, the analysis unit analyzes the interrelationships between pieces of collected information and improves the accuracy of the analysis. For example, the analysis unit prioritizes the analysis of highly related information based on the interrelationships between pieces of collected information. The analysis unit can also apply an algorithm to improve the accuracy of the analysis based on the interrelationships between pieces of information. For example, the analysis unit applies an algorithm to improve the accuracy of the analysis based on the interrelationships between pieces of information. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between pieces of information.

[0054] The generation unit can generate information taking into consideration the geographical distribution of the information during generation. For example, the generation unit generates information based on the geographical distribution of the information. For example, the generation unit improves the accuracy of generation based on the geographical distribution of the information. The generation unit can also generate highly relevant information preferentially by taking the geographical distribution into consideration. For example, the generation unit generates highly relevant information preferentially by taking the geographical distribution into consideration. The generation unit can also select an optimal generation method based on the geographical distribution. For example, the generation unit selects an optimal generation method based on the geographical distribution. In this way, the generation unit can generate more appropriate perspectives and thoughts by taking the geographical distribution into consideration.

[0055] The collection unit can analyze the social media activities of the characters and collect related information. For example, the collection unit collects related information based on information shared by the characters on social media. The collection unit can also collect related information based on information about accounts that the characters follow on social media. For example, the collection unit collects related information based on information about accounts that the characters follow. The collection unit can also analyze the characters' social media activity history and collect optimal information. For example, the collection unit collects optimal information based on the characters' social media activity history. This allows the collection unit to collect more relevant information by analyzing social media activities.

[0056] The generation unit can improve the accuracy of generation by referring to related literature during generation. For example, the generation unit improves the accuracy of generation by referring to related literature during generation. The generation unit can also apply an algorithm for improving the accuracy of generation based on the related literature. For example, the generation unit applies an algorithm for improving the accuracy of generation based on the related literature. The generation unit can also select an optimal generation method by referring to related literature. For example, the generation unit selects an optimal generation method by referring to related literature. In this way, the generation unit improves the accuracy of generation by referring to related literature.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The setting section creates the character settings. Character settings include, but are not limited to, names, ages, personalities, and backgrounds. Step 2: The collection unit collects information from the viewpoints of the characters set by the setting unit. The collected information includes, but is not limited to, visual information, auditory information, and emotional information. For example, the collection unit can collect the viewpoints of the characters using a camera or a microphone. The collection unit can also collect the emotions of the characters using a sensor or an emotion analysis algorithm. Step 3: The analysis unit analyzes the information collected by the collection unit. Examples of analysis include, but are not limited to, text analysis, sentiment analysis, and data mining. For example, the analysis unit analyzes the collected information to connect different perspectives and emotions. For example, the analysis unit can extract commonalities and reconcile differences. Step 4: The generation unit generates new perspectives and thoughts based on the information analyzed by the analysis unit. For example, a generation algorithm or an emotion model may be used for the generation, but is not limited to such examples. The generation unit generates new perspectives and thoughts, for example. For example, the generation unit provides the generated perspectives and thoughts to the user. For example, the generation unit has an interface for displaying the generated perspectives and thoughts.

[0059] (Example 2) The AI ​​system according to an embodiment of the present invention is a system that generates new perspectives and feelings by connecting different viewpoints, based on the premise that people who have the same experiences or memories have different perspectives and emotions. This AI system sets up characters, collects information from each of their perspectives, analyzes the collected information, and generates new perspectives and feelings by connecting different perspectives and emotions. For example, regarding an episode that a child tells their parents, the system collects the child's perspective, the parents' perspective, and the child's siblings' perspectives. The AI ​​analyzes these perspectives and generates new perspectives and feelings by connecting different perspectives and emotions. This activates communication among family members and leads to a deeper understanding. In this way, the AI ​​system can activate communication by connecting different perspectives and emotions to generate new perspectives and feelings.

[0060] The AI ​​system according to this embodiment comprises a setting unit, a collection unit, an analysis unit, and a generation unit. The setting unit sets up the characters. Character settings include, for example, names, ages, personalities, and backgrounds, but are not limited to these examples. The collection unit collects information from each perspective of the characters set up by the setting unit. The information collected includes, for example, visual information, auditory information, and emotional information, but is not limited to these examples. The collection unit collects, for example, the perspectives and emotions of the characters. For example, the collection unit can collect the perspectives of the characters using cameras and microphones. The collection unit can also collect the emotions of the characters using sensors and emotion analysis algorithms. The analysis unit analyzes the information collected by the collection unit. Analysis includes, for example, text analysis, emotion analysis, and data mining, but is not limited to these examples. The analysis unit analyzes the collected information and connects different perspectives and emotions. For example, the analysis unit can extract commonalities and adjust differences. The generation unit generates new perspectives and thoughts based on the information analyzed by the analysis unit. The generation process may utilize, but is not limited to, generation algorithms or emotion models. The generation unit may, for example, generate new perspectives or feelings. For example, the generation unit may provide the generated perspectives or feelings to the user. For example, the generation unit may include an interface for displaying the generated perspectives or feelings. This allows the AI ​​system according to the embodiment to connect different perspectives and feelings to generate new perspectives and feelings, thereby stimulating communication.

[0061] The data collection unit can collect the perspectives and emotions of the characters. For example, the data collection unit can collect the perspectives of the characters using cameras and microphones. For example, the data collection unit can capture the perspectives of the characters with a camera and collect visual information. The data collection unit can also record the perspectives of the characters with a microphone and collect auditory information. For example, the data collection unit can collect the perspectives of the characters as audio data. The data collection unit can also collect the emotions of the characters using sensors and emotion analysis algorithms. For example, the data collection unit can measure the emotions of the characters with sensors and collect emotion data. The data collection unit can also analyze the emotions of the characters with emotion analysis algorithms and collect emotion data. For example, the data collection unit can collect the emotions of the characters as text data. In this way, the data collection unit can obtain more diverse information by collecting the perspectives and emotions of the characters.

[0062] The analysis unit can analyze collected information and connect different perspectives and emotions. For example, the analysis unit can analyze collected information using text analysis and sentiment analysis. For instance, it can analyze collected text data and extract commonalities and differences. The analysis unit can also analyze collected sentiment data and understand changes in emotions. For example, it can identify patterns of emotional change based on sentiment data. Furthermore, the analysis unit can analyze collected information using data mining techniques. For example, it can use data mining techniques to extract useful patterns and relationships from collected information. As a result, the analysis unit can improve its accuracy in generating new perspectives and thoughts by connecting different perspectives and emotions.

[0063] The generation unit can provide the generated viewpoints and thoughts to the user. The generation unit, for example, includes an interface for displaying the generated viewpoints and thoughts. For example, the generation unit displays the generated viewpoints and thoughts on a screen. The generation unit can also provide the generated viewpoints and thoughts as audio. For example, the generation unit provides the generated viewpoints and thoughts as audio using voice synthesis technology. The generation unit can also provide the generated viewpoints and thoughts as text data. For example, the generation unit transmits the generated viewpoints and thoughts to the user as a text message. In this way, the generation unit contributes to revitalizing communication by providing the generated viewpoints and thoughts to the user.

[0064] The collection unit can collect information based on the settings of the characters. The collection unit, for example, collects related information based on the settings of the characters. For example, the collection unit collects information based on the names and ages of the characters. The collection unit can also collect information based on the personalities and backgrounds of the characters. For example, the collection unit collects information related to the personalities of the characters. The collection unit can also collect information related to the backgrounds of the characters. For example, the collection unit collects information related to the past experiences and memories of the characters. In this way, the collection unit can obtain more relevant information by collecting information based on the settings of the characters.

[0065] The setting unit can estimate the emotions of the characters and adjust the setting content based on the estimated emotions. For example, the setting unit can estimate the emotions of the characters using an emotion engine and adjust the setting content. For example, if the character is sad, the setting unit can estimate that emotion using the emotion engine and adjust the setting content to be comforting. Also, if the character is happy, the setting unit can estimate that emotion using the emotion engine and adjust the setting content to further enhance the happiness. For example, if the character is happy, the setting unit can adjust the setting content to be celebratory. Also, if the character is angry, the setting unit can estimate that emotion using the emotion engine and adjust the setting content to calm the character. For example, if the character is angry, the setting unit can adjust the setting content to be calming. In this way, the setting unit can make more appropriate settings by adjusting the setting content based on the emotions of the characters.

[0066] The setting unit can analyze the character's past behavior history and select an appropriate setting method. The setting unit, for example, analyzes the character's past behavior history and selects the optimal setting method. For example, the setting unit adjusts the setting content based on places the character has frequently visited in the past. The setting unit can also analyze the character's past behavior patterns and select the optimal setting method. For example, the setting unit analyzes the character's past behavior patterns and selects the optimal setting method. The setting unit can also predict the character's behavior during a specific time period based on the character's past behavior history and adjust the setting content. For example, the setting unit predicts the character's behavior during a specific time period based on the character's past behavior history and adjusts the setting content. In this way, the setting unit can select a more optimal setting method by analyzing the character's past behavior history.

[0067] The setting unit can customize the settings based on the character's current situation and areas of interest. The setting unit, for example, customizes the settings based on the character's current situation and areas of interest. For example, the setting unit customizes the settings based on a project the character is currently working on. The setting unit can also provide related setting content based on the character's current areas of interest. For example, the setting unit provides information related to the character's current areas of interest. The setting unit can also adjust the setting content based on the character's current situation (e.g., at work, on vacation, etc.). For example, if the character is at work, the setting unit provides work-related setting content. If the character is on vacation, the setting unit can provide relaxing setting content. This allows the setting unit to customize the settings based on the current situation and areas of interest, thereby enabling more appropriate settings.

[0068] The setting unit can estimate the emotions of the characters and determine the priority of settings based on the estimated emotions. The setting unit, for example, estimates the emotions of the characters using an emotion engine and determines the priority of settings. For example, if the character is feeling stressed, the setting unit estimates the emotion using the emotion engine and prioritizes a setting that helps the character relax. Also, if the character is excited, the setting unit can estimate the emotion using the emotion engine and prioritize a setting that helps the character regain their composure. For example, if the character is excited, the setting unit prioritizes a setting that helps the character regain their composure. Also, if the character is tired, the setting unit can estimate the emotion using the emotion engine and prioritize a setting that encourages the character to rest. For example, if the character is tired, the setting unit prioritizes a setting that encourages the character to rest. In this way, the setting unit can determine the priority of settings based on emotions, thereby enabling more appropriate settings.

[0069] The settings unit can prioritize relevant settings by considering the geographical location of the characters during the setting process. For example, if a character is in a specific region, the settings unit will prioritize settings related to that region. The settings unit can also adjust settings based on information about the travel destination if a character is traveling. For example, if a character is traveling, the settings unit will adjust settings based on information about the travel destination. The settings unit can also prioritize settings related to the home if a character is at home. For example, if a character is at home, the settings unit will prioritize settings related to the home. In this way, the settings unit can create more relevant settings by considering geographical location.

[0070] The settings unit can analyze the characters' social media activity during the setup process and make relevant settings. For example, the settings unit can analyze the characters' social media activity and adjust the settings. For example, the settings unit can adjust the settings based on information shared by the characters on social media. The settings unit can also make relevant settings based on information about accounts that the characters follow on social media. For example, the settings unit can adjust the settings based on information about accounts that the characters follow. The settings unit can also analyze the characters' social media activity history and make optimal settings. For example, the settings unit makes optimal settings based on the characters' social media activity history. This allows the settings unit to make more relevant settings by analyzing social media activity.

[0071] The collection unit can estimate the emotions of the characters and adjust the timing of information collection based on the estimated emotions. The collection unit, for example, estimates the emotions of the characters using an emotion engine and adjusts the timing of information collection. For example, if the character is relaxed, the collection unit estimates the emotion using the emotion engine and collects information. Furthermore, if the character is busy, the collection unit can estimate the emotion using the emotion engine and postpone information collection. For example, if the character is busy, the collection unit postpones information collection. Furthermore, if the character is concentrating, the collection unit can estimate the emotion using the emotion engine and suspend information collection. For example, if the character is concentrating, the collection unit suspends information collection. In this way, the collection unit can adjust the timing of information collection based on the emotions, thereby collecting information at a more appropriate time.

[0072] The data collection unit can analyze the characters' past information submission history and select an appropriate collection method. For example, the data collection unit can analyze the characters' past information submission history and select the optimal collection method. For example, the data collection unit can select the optimal collection method based on the format of the information the characters have submitted in the past. The data collection unit can also select the most efficient collection method from the characters' past information submission history. For example, the data collection unit can select the most efficient collection method based on the characters' past information submission history. The data collection unit can also analyze the frequency of information the characters have submitted in the past and select the optimal collection timing. For example, the data collection unit can select the optimal collection timing based on the frequency of information submissions in the past. In this way, the data collection unit can select a more optimal collection method by analyzing past information submission history.

[0073] The data collection unit can filter information based on the characters' current projects and areas of interest. For example, the data collection unit can filter information based on the characters' current projects and areas of interest. For instance, the data collection unit prioritizes collecting information related to the projects the characters are currently working on. The data collection unit can also filter relevant information based on the characters' current areas of interest. For example, the data collection unit filters information related to the characters' current areas of interest. The data collection unit can also select and collect necessary information based on the characters' current situation. For example, the data collection unit selects and collects necessary information based on the characters' current situation. This allows the data collection unit to collect more relevant information by filtering based on current projects and areas of interest.

[0074] The data collection unit can estimate the emotions of the characters and determine the priority of information to collect based on those estimated emotions. For example, the data collection unit can estimate the emotions of the characters using an emotion engine and determine the priority of information to collect. For example, if a character is feeling stressed, the data collection unit can estimate that emotion using the emotion engine and prioritize collecting information that will help them relax. Similarly, if a character is excited, the data collection unit can estimate that emotion using the emotion engine and prioritize collecting information that will help them regain their composure. For example, if a character is tired, the data collection unit can estimate that emotion using the emotion engine and prioritize collecting information that will encourage them to rest. In this way, the data collection unit can collect more appropriate information by prioritizing information based on emotions.

[0075] The data collection unit can prioritize collecting highly relevant information by considering the geographical location of the characters when gathering information. For example, if a character is in a specific region, the data collection unit will prioritize collecting information related to that region. Also, if a character is traveling, the data collection unit can collect information based on information about their travel destination. For example, if a character is traveling, the data collection unit will prioritize collecting information based on information about their travel destination. Also, if a character is at home, the data collection unit can prioritize collecting information related to their home. For example, if a character is at home, the data collection unit will prioritize collecting information related to their home. In this way, the data collection unit can collect more relevant information by considering geographical location.

[0076] The data collection unit can analyze the social media activities of the characters and collect relevant information when gathering data. For example, the data collection unit can analyze the social media activities of the characters and collect information. For example, the data collection unit can collect relevant information based on information shared by the characters on social media. The data collection unit can also collect relevant information based on information about accounts that the characters follow on social media. For example, the data collection unit can collect relevant information based on information about accounts that the characters follow. The data collection unit can also analyze the activity history of the characters on social media and collect the most relevant information. For example, the data collection unit can collect the most relevant information based on the activity history of the characters on social media. In this way, the data collection unit can collect more relevant information by analyzing social media activities.

[0077] The analysis unit can estimate the emotions of the characters and adjust the analysis criteria based on the estimated emotions. The analysis unit, for example, estimates the emotions of the characters using an emotion engine and adjusts the analysis criteria. For example, if the character is relaxed, the analysis unit estimates the emotions using the emotion engine and relaxes the analysis criteria. Also, if the character is nervous, the analysis unit can estimate the emotions using the emotion engine and tighten the analysis criteria. For example, if the character is nervous, the analysis unit tightens the analysis criteria. Also, if the character is excited, the analysis unit can estimate the emotions using the emotion engine and adjust the analysis criteria. For example, if the character is excited, the analysis unit adjusts the analysis criteria. In this way, the analysis unit can adjust the analysis criteria based on the emotions, enabling more appropriate analysis.

[0078] The analysis unit can improve the accuracy of its analysis by considering the interrelationships between pieces of information during the analysis process. For example, the analysis unit analyzes the interrelationships of collected information to improve the accuracy of the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information based on the interrelationships of the collected information. The analysis unit can also apply algorithms to improve the accuracy of the analysis based on the interrelationships of the information. For example, the analysis unit applies algorithms to improve the accuracy of the analysis based on the interrelationships of the information. As a result, the analysis unit improves the accuracy of its analysis by considering the interrelationships of the information.

[0079] The analysis unit can perform analysis while considering the attribute information of the characters. For example, the analysis unit can perform analysis while considering the attribute information of the characters. For example, the analysis unit can perform analysis while considering attribute information such as the age and gender of the characters. The analysis unit can also improve the accuracy of the analysis based on attribute information such as the occupation and hobbies of the characters. For example, the analysis unit can improve the accuracy of the analysis based on attribute information such as the occupation and hobbies of the characters. The analysis unit can also select the optimal analysis method based on the attribute information of the characters. For example, the analysis unit selects the optimal analysis method based on the attribute information of the characters. As a result, the analysis unit can perform more appropriate analysis by considering attribute information.

[0080] The analysis unit can estimate the emotions of the characters and adjust the display order of the analysis results based on the estimated emotions. For example, the analysis unit estimates the emotions of the characters using an emotion engine and adjusts the display order of the analysis results. For example, if a character is relaxed, the analysis unit estimates that emotion using the emotion engine and displays the analysis results in detail. Also, if a character is tense, the analysis unit can estimate that emotion using the emotion engine and display the analysis results concisely. For example, if a character is tense, the analysis unit displays the analysis results concisely. Also, if a character is excited, the analysis unit can estimate that emotion using the emotion engine and display the analysis results in a visually stimulating way. For example, if a character is excited, the analysis unit displays the analysis results in a visually stimulating way. In this way, the analysis unit can provide more appropriate analysis results by adjusting the display order based on emotions.

[0081] The analysis unit can perform analysis while considering the geographical distribution of information. For example, the analysis unit can perform analysis based on the geographical distribution of information. For example, the analysis unit can improve the accuracy of the analysis based on the geographical distribution of information. The analysis unit can also prioritize the analysis of highly relevant information by considering the geographical distribution. For example, the analysis unit prioritizes the analysis of highly relevant information by considering the geographical distribution. The analysis unit can also select the optimal analysis method based on the geographical distribution. For example, the analysis unit selects the optimal analysis method based on the geographical distribution. As a result, the analysis unit can perform more appropriate analysis by considering the geographical distribution.

[0082] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis. For example, the analysis unit performs the analysis by referring to relevant literature. For example, the analysis unit improves the accuracy of its analysis by referring to relevant literature during the analysis. The analysis unit can also apply algorithms to improve the accuracy of its analysis based on relevant literature. For example, the analysis unit applies algorithms to improve the accuracy of its analysis based on relevant literature. The analysis unit can also select the optimal analysis method by referring to relevant literature. For example, the analysis unit selects the optimal analysis method by referring to relevant literature. As a result, the accuracy of the analysis is improved by the analysis unit referring to relevant literature.

[0083] The generation unit can estimate the emotions of the characters and determine the priority of the viewpoints and thoughts to be generated based on the estimated emotions. The generation unit, for example, estimates the emotions of the characters using an emotion engine and determines the priority of the viewpoints and thoughts to be generated. For example, if the character is relaxed, the generation unit estimates the emotion using the emotion engine and prioritizes generating relaxed viewpoints and thoughts. Also, if the character is tense, the generation unit can estimate the emotion using the emotion engine and prioritize generating viewpoints and thoughts that relieve the tension. For example, if the character is tense, the generation unit prioritizes generating viewpoints and thoughts that relieve the tension. Also, if the character is excited, the generation unit can estimate the emotion using the emotion engine and prioritize generating viewpoints and thoughts that increase the excitement. For example, if the character is excited, the generation unit prioritizes generating viewpoints and thoughts that increase the excitement. In this way, the generation unit can generate more appropriate viewpoints and thoughts by determining the priority of viewpoints and thoughts based on emotions.

[0084] The generation unit can improve the accuracy of generation by considering the interrelationships of information during generation. For example, the generation unit can analyze the interrelationships of collected information to improve generation accuracy. For example, the generation unit can prioritize generating highly relevant information based on the interrelationships of collected information. The generation unit can also apply algorithms to improve generation accuracy based on the interrelationships of information. For example, the generation unit can apply algorithms to improve generation accuracy based on the interrelationships of information. As a result, the generation unit improves generation accuracy by considering the interrelationships of information.

[0085] The generation unit can consider the attribute information of the characters during the generation process. For example, the generation unit can consider the attribute information of the characters when generating content. For example, the generation unit can consider attribute information such as the age and gender of the characters to generate appropriate perspectives and thoughts. The generation unit can also generate highly relevant perspectives and thoughts based on attribute information such as the occupation and hobbies of the characters. For example, the generation unit can generate highly relevant perspectives and thoughts based on attribute information such as the occupation and hobbies of the characters. The generation unit can also select the optimal generation method based on the attribute information of the characters. For example, the generation unit selects the optimal generation method based on the attribute information of the characters. As a result, the generation unit can generate more appropriate perspectives and thoughts by considering attribute information.

[0086] The generation unit can estimate the emotions of the characters and adjust the way the generated perspectives and thoughts are displayed based on the estimated emotions. For example, the generation unit can estimate the emotions of the characters using an emotion engine and adjust the way the generated perspectives and thoughts are displayed. For example, if a character is relaxed, the generation unit can estimate that emotion using the emotion engine and provide a visually calm display method. Also, if a character is tense, the generation unit can estimate that emotion using the emotion engine and provide a visually simple display method. For example, if a character is tense, the generation unit provides a visually simple display method. Also, if a character is excited, the generation unit can estimate that emotion using the emotion engine and provide a visually stimulating display method. For example, if a character is excited, the generation unit provides a visually stimulating display method. In this way, the generation unit can provide more appropriate perspectives and thoughts by adjusting the display method based on emotions.

[0087] The generation unit can generate information taking into consideration the geographical distribution of the information. The generation unit generates information based on, for example, the geographical distribution of the information. For example, the generation unit improves the accuracy of generation based on the geographical distribution of the information. The generation unit can also generate highly relevant information preferentially by taking the geographical distribution into consideration. For example, the generation unit generates highly relevant information preferentially by taking the geographical distribution into consideration. The generation unit can also select an optimal generation method based on the geographical distribution. For example, the generation unit selects an optimal generation method based on the geographical distribution. In this way, the generation unit can generate more appropriate perspectives and thoughts by taking the geographical distribution into consideration.

[0088] The generation unit can improve the accuracy of generation by referring to related literature during generation. The generation unit, for example, performs generation by referring to related literature. For example, the generation unit improves the accuracy of generation by referring to related literature during generation. The generation unit can also apply an algorithm for improving the accuracy of generation based on the related literature. For example, the generation unit applies an algorithm for improving the accuracy of generation based on the related literature. The generation unit can also select an optimal generation method by referring to related literature. For example, the generation unit selects an optimal generation method by referring to related literature. As a result, the generation unit improves the accuracy of generation by referring to related literature. === Hard Collateral 1-1 === Each of the multiple elements including the setting unit, collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart device 14 and sets up the characters. The collection unit, for example, collects visual information and auditory information using the camera 42 and microphone 38B of the smart device 14, and collects emotional information using the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to connect different perspectives and emotions. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates new perspectives and thoughts based on the analyzed information. === Hard Collateral 1-2 === Each of the multiple elements including the setting unit, collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the smart glasses 214 and sets the characters. The collection unit, for example, collects visual information and auditory information using the camera 42 and microphone 238 of the smart glasses 214, and collects emotional information by the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to connect different perspectives and emotions. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates new perspectives and thoughts based on the analyzed information. === Hard Collateral 1-3 === Each of the multiple elements including the setting unit, collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the setting unit is realized by the control unit 46A of the headset-type terminal 314 and sets up the characters. The collection unit, for example, collects visual information and auditory information using the camera 42 and microphone 238 of the headset-type terminal 314, and collects emotional information by the control unit 46A. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to connect different perspectives and emotions. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates new perspectives and feelings based on the analyzed information. === Hard Collateral 1-4 === Each of the multiple elements described above, including the setting unit, collection unit, analysis unit, and generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the setting unit is implemented by the control unit 46A of the robot 414 and sets the characters. The collection unit collects visual and auditory information using the camera 42 and microphone 238 of the robot 414, and emotional information is collected by the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information to connect different perspectives and emotions. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates new perspectives and thoughts based on the analyzed information.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The setting unit can analyze the characters' past behavioral history and select an appropriate setting method. For example, the setting unit adjusts the setting based on places the characters have frequently visited in the past. The setting unit can also analyze the characters' past behavioral patterns and select the optimal setting method. For example, the setting unit analyzes the characters' past behavioral patterns and selects the optimal setting method. The setting unit can also predict actions the characters will take during specific time periods based on their past behavioral history and adjust the setting accordingly. For example, the setting unit predicts actions the characters will take during specific time periods based on their past behavioral history and adjusts the setting accordingly. In this way, the setting unit can select a more optimal setting method by analyzing past behavioral history.

[0091] The data collection unit can customize settings based on the characters' current situation and areas of interest. For example, the unit can customize settings based on the project the character is currently working on. The unit can also provide relevant settings based on the characters' current areas of interest. For example, the unit can provide information related to the characters' current areas of interest. The unit can also adjust settings based on the characters' current situation (e.g., at work, on vacation). For example, if the character is at work, the unit will provide work-related settings. If the character is on vacation, the unit can also provide relaxing settings. This allows the data collection unit to provide more appropriate settings by customizing them based on the character's current situation and areas of interest.

[0092] The analysis unit can estimate the emotions of the characters and adjust the analysis criteria based on the estimated emotions. For example, if a character is relaxed, the analysis unit can estimate that emotion using the emotion engine and loosen the analysis criteria. Conversely, if a character is tense, the analysis unit can estimate that emotion using the emotion engine and tighten the analysis criteria. For example, if a character is tense, the analysis unit will tighten the analysis criteria. Furthermore, if a character is excited, the analysis unit can estimate that emotion using the emotion engine and adjust the analysis criteria. For example, if a character is excited, the analysis unit will adjust the analysis criteria. In this way, the analysis unit can perform more appropriate analysis by adjusting the analysis criteria based on emotions.

[0093] The generation unit can estimate the emotions of the characters and determine the priority of the viewpoints and thoughts to be generated based on the estimated emotions. For example, if the character is relaxed, the generation unit estimates the emotion using the emotion engine and prioritizes generating relaxed viewpoints and thoughts. Also, if the character is tense, the generation unit can estimate the emotion using the emotion engine and prioritize generating viewpoints and thoughts that relieve the tension. For example, if the character is tense, the generation unit can prioritize generating viewpoints and thoughts that relieve the tension. Also, if the character is excited, the generation unit can estimate the emotion using the emotion engine and prioritize generating viewpoints and thoughts that increase the excitement. For example, if the character is excited, the generation unit prioritizes generating viewpoints and thoughts that increase the excitement. In this way, the generation unit can generate more appropriate viewpoints and thoughts by determining the priority of viewpoints and thoughts based on emotions.

[0094] The collection unit can estimate the emotions of the characters and adjust the timing of collecting information based on the estimated emotions. For example, if the character is relaxed, the collection unit estimates the emotion using the emotion engine and collects information. Also, if the character is busy, the collection unit can estimate the emotion using the emotion engine and postpone information collection. For example, if the character is busy, the collection unit postpones information collection. Also, if the character is concentrating, the collection unit can estimate the emotion using the emotion engine and pause information collection. For example, if the character is concentrating, the collection unit pauses information collection. In this way, the collection unit can adjust the timing of collecting information based on the emotions and collect information at a more appropriate time.

[0095] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between pieces of information during analysis. For example, the analysis unit analyzes the interrelationships between pieces of collected information and improves the accuracy of the analysis. For example, the analysis unit prioritizes the analysis of highly related information based on the interrelationships between pieces of collected information. The analysis unit can also apply an algorithm to improve the accuracy of the analysis based on the interrelationships between pieces of information. For example, the analysis unit applies an algorithm to improve the accuracy of the analysis based on the interrelationships between pieces of information. In this way, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between pieces of information.

[0096] The generation unit can generate information taking into consideration the geographical distribution of the information during generation. For example, the generation unit generates information based on the geographical distribution of the information. For example, the generation unit improves the accuracy of generation based on the geographical distribution of the information. The generation unit can also generate highly relevant information preferentially by taking the geographical distribution into consideration. For example, the generation unit generates highly relevant information preferentially by taking the geographical distribution into consideration. The generation unit can also select an optimal generation method based on the geographical distribution. For example, the generation unit selects an optimal generation method based on the geographical distribution. In this way, the generation unit can generate more appropriate perspectives and thoughts by taking the geographical distribution into consideration.

[0097] The collection unit can analyze the social media activities of the characters and collect related information. For example, the collection unit collects related information based on information shared by the characters on social media. The collection unit can also collect related information based on information about accounts that the characters follow on social media. For example, the collection unit collects related information based on information about accounts that the characters follow. The collection unit can also analyze the characters' social media activity history and collect optimal information. For example, the collection unit collects optimal information based on the characters' social media activity history. This allows the collection unit to collect more relevant information by analyzing social media activities.

[0098] The analysis unit can estimate the emotions of the characters and adjust the display order of the analysis results based on the estimated emotions. For example, if the character is relaxed, the analysis unit can estimate the emotions using the emotion engine and display the analysis results in detail. Also, if the character is nervous, the analysis unit can estimate the emotions using the emotion engine and display the analysis results concisely. For example, if the character is nervous, the analysis unit can display the analysis results concisely. Also, if the character is excited, the analysis unit can estimate the emotions using the emotion engine and display the analysis results in a visually stimulating manner. For example, if the character is excited, the analysis unit can display the analysis results in a visually stimulating manner. In this way, the analysis unit can provide more appropriate analysis results by adjusting the display order based on emotions.

[0099] The generation unit can improve the accuracy of generation by referring to related literature during generation. For example, the generation unit improves the accuracy of generation by referring to related literature during generation. The generation unit can also apply an algorithm for improving the accuracy of generation based on the related literature. For example, the generation unit applies an algorithm for improving the accuracy of generation based on the related literature. The generation unit can also select an optimal generation method by referring to related literature. For example, the generation unit selects an optimal generation method by referring to related literature. In this way, the generation unit improves the accuracy of generation by referring to related literature.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The setting section creates the character settings. Character settings include, but are not limited to, names, ages, personalities, and backgrounds. Step 2: The collection unit collects information from the viewpoints of the characters set by the setting unit. The collected information includes, but is not limited to, visual information, auditory information, and emotional information. For example, the collection unit can collect the viewpoints of the characters using a camera or a microphone. The collection unit can also collect the emotions of the characters using a sensor or an emotion analysis algorithm. Step 3: The analysis unit analyzes the information collected by the collection unit. Examples of analysis include, but are not limited to, text analysis, sentiment analysis, and data mining. For example, the analysis unit analyzes the collected information to connect different perspectives and emotions. For example, the analysis unit can extract commonalities and reconcile differences. Step 4: The generation unit generates new perspectives and thoughts based on the information analyzed by the analysis unit. For example, a generation algorithm or an emotion model may be used for the generation, but is not limited to such examples. The generation unit generates new perspectives and thoughts, for example. For example, the generation unit provides the generated perspectives and thoughts to the user. For example, the generation unit has an interface for displaying the generated perspectives and thoughts.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

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

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 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.

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

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 setting section that sets up the characters; a collection unit that collects information from each viewpoint of the characters set by the setting unit; an analysis unit that analyzes the information collected by the collection unit; a generation unit that generates viewpoints and thoughts based on the information analyzed by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Gathering characters' perspectives and emotions 2. The system of claim 1.

3. The analysis unit Analyzing collected information to connect different perspectives and emotions 2. The system of claim 1.

4. The generation unit Providing generated perspectives and thoughts to users 2. The system of claim 1.

5. The collecting unit Gather information based on the characters' settings 2. The system of claim 1.

6. The setting unit Estimate the emotions of the characters and adjust the settings based on those emotions 2. The system of claim 1.

7. The setting unit Analyze the characters' past behavior history and select the appropriate setting method 2. The system of claim 1.

8. The setting unit Customize settings based on your character's current situation and interests 2. The system of claim 1.

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