system

Abstract

The system according to the embodiment aims to provide advice and measures against future dangers and risks by utilizing past experiences. [Solution] A system according to an embodiment includes a collection unit, a learning unit, a reception unit, a provision unit, and a suggestion unit. The collection unit collects personal stories. The learning unit trains an AI on the personal stories collected by the collection unit. The reception unit receives questions from users. The provision unit provides personal stories based on the questions received by the reception unit. The suggestion unit provides countermeasures and advice based on the personal stories provided by the provision 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 techniques have not done enough to effectively utilize the experiences of past disasters and wars to provide countermeasures against future dangers and risks.

[0005] The system according to the embodiment aims to provide advice and measures against future dangers and risks by utilizing past experiences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, a reception unit, a provision unit, and a suggestion unit. The collection unit collects experiences. The learning unit causes an AI to learn from the experiences collected by the collection unit. The reception unit receives questions from users. The provision unit provides experiences based on the questions received by the reception unit. The suggestion unit provides measures and advice based on the experiences provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can utilize past experiences to provide advice and countermeasures against future dangers and risks. [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) A system according to an embodiment of the present invention collects personal stories of past disasters and wars, trains AI to learn from them, and provides appropriate personal stories based on user questions and offers countermeasures and advice. This system collects personal stories, trains AI to learn from them, and provides appropriate personal stories based on user questions and offers countermeasures and advice. To prevent past events from fading away, an AI storyteller is created, allowing anyone to ask questions and hear personal stories at any time. For example, if a user asks, "What should I do if an earthquake occurs?", the AI ​​provides specific countermeasures and advice based on past earthquake experiences. Similarly, if a user asks, "I want to know about life during the war," the AI ​​provides past war experiences and explains the living conditions and countermeasures at the time. In this way, users can learn countermeasures for future dangers and risks based on past experiences. This system collects personal stories of past disasters and wars, trains AI to learn from them, and provides appropriate personal stories based on user questions and offers countermeasures and advice. For example, users can learn countermeasures for future dangers and risks based on past experiences. In addition, to prevent past events from fading away, we can create an AI that acts as a storyteller, allowing anyone to ask questions and hear about their experiences at any time.

[0029] The personal experience sharing system according to the embodiment includes a collection unit, a learning unit, a reception unit, a provision unit, and a suggestion unit. The collection unit collects personal experiences of past disasters and wars. The collection unit can collect the personal experiences through methods such as interviews, questionnaires, and online posting. The collection unit can also collect personal experiences in text, audio, and video formats. The learning unit trains an AI based on the collected personal experiences. The learning unit can learn the personal experiences using AI techniques such as neural networks and support vector machines. The learning unit can use learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning. The reception unit accepts user questions. The reception unit can accept user questions in formats such as text, audio, and multiple choice. The provision unit provides appropriate personal experiences based on the questions accepted by the reception unit. The provision unit can provide the personal experiences through methods such as real-time provision, batch provision, and push notification. The suggestion unit provides countermeasures and advice based on the personal experiences provided by the provision unit. The suggestion unit can provide measures and advice such as specific guidelines for action, risk avoidance measures, psychological support, etc. As a result, the experience story providing system according to the embodiment can collect and learn from experiences, provide appropriate experiences based on user questions, and provide measures and advice.

[0030] The collection unit may collect stories of past disasters or wars. The collection unit may collect stories of past disasters or wars by, for example, interviews, questionnaires, online postings, etc. The collection unit may collect stories in written, audio, video, or other formats. In this way, by collecting stories of past disasters or wars, data for proposing measures against future dangers and risks can be obtained.

[0031] The learning unit can make the AI ​​learn from the collected personal experiences. The learning unit can make the AI ​​learn from the collected personal experiences using AI technologies such as neural networks and support vector machines. The learning unit can use learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning. By making the AI ​​learn from the collected personal experiences, the AI ​​can provide appropriate countermeasures and advice.

[0032] The reception unit can receive questions from users. The reception unit can receive questions from users in a format such as text, voice, or multiple choice. By receiving the user's questions, the AI ​​can obtain information to provide appropriate experiences.

[0033] The providing unit can provide appropriate testimonials based on the questions received by the receiving unit. The providing unit can provide appropriate testimonials by methods such as real-time provision, batch provision, push notification, etc. In this way, by providing appropriate testimonials based on the questions received by the receiving unit, it is possible to provide useful information to the user.

[0034] The suggestion unit can provide countermeasures and advice based on the experiences provided by the provision unit. The suggestion unit can provide countermeasures and advice such as specific guidelines for action, risk avoidance measures, and psychological support. As a result, by providing countermeasures and advice based on the experiences provided by the provision unit, specific countermeasures and advice can be provided to the user.

[0035] The collection unit can evaluate the credibility of the testimonials at the time of collection and prioritize collecting highly reliable information. For example, the collection unit can check the background information of the providers of the testimonials at the time of collection and prioritize collecting highly reliable information. The collection unit can also cross-check the content of the testimonials at the time of collection and prioritize collecting matching information from multiple information sources. The collection unit can also refer to past credibility evaluations of the providers of the testimonials at the time of collection and prioritize collecting highly reliable information. In this way, by prioritizing the collection of highly reliable information, the reliability of the countermeasures and advice provided by the AI ​​can be improved.

[0036] The collection unit can adjust the collection method depending on the content of the experiences when collecting them. For example, if detailed experiences are provided when collecting them, the collection unit collects them in an interview format. Also, if brief experiences are provided when collecting them, the collection unit can collect them in a questionnaire format. Also, the collection unit can select a collection method depending on the level of detail of the experiences when collecting them, and collect them in an appropriate format. In this way, by adjusting the collection method depending on the level of detail of the experiences, it is possible to collect experiences in an appropriate format.

[0037] The collection department can apply different collection methods depending on the type of story when collecting them. For example, when collecting disaster stories, the collection department can conduct field surveys and interviews. When collecting war stories, the collection department can also collect historical materials and testimonies. The collection department can also select appropriate collection methods depending on the category of the story and collect them. In this way, by selecting appropriate collection methods depending on the category of the story, it is possible to collect stories effectively.

[0038] The collection unit can prioritize collecting highly relevant information by taking into consideration the geographical distribution of the experiences during collection. For example, the collection unit can prioritize collecting experiences of disasters that occurred in a specific region during collection. The collection unit can also prioritize collecting experiences of specific war regions during collection. The collection unit can also prioritize collecting information that is highly geographically relevant during collection. In this way, by prioritizing the collection of information that is highly geographically relevant, it is possible to provide measures and advice that are specific to the region.

[0039] The collection unit can collect testimonials taking into account information about the providers of the testimonials. For example, the collection unit can collect testimonials taking into account the age and gender of the testimonial providers. The collection unit can also collect testimonials taking into account the occupation and background of the testimonial providers. The collection unit can also collect appropriate testimonials taking into account attribute information about the testimonial providers. In this way, by collecting testimonials taking into account attribute information about the testimonial providers, it is possible to collect testimonials from a wider variety of perspectives.

[0040] The collection unit can determine the priority of collection based on the time of submission of the experiences at the time of collection. For example, the collection unit prioritizes collection of recent experiences at the time of collection. The collection unit can also prioritize collection of experiences submitted at a specific time at the time of collection. The collection unit can also weight collection based on the time of submission at the time of collection. In this way, weighting collection based on the time of submission makes it possible to prioritize collection of the most recent information.

[0041] The learning unit can evaluate the credibility of the testimonials during learning and prioritize highly reliable information for learning. For example, the learning unit can check the background information of the testimonial provider during learning and prioritize highly reliable information for learning. The learning unit can also cross-check the content of the testimonials during learning and prioritize matching information from multiple information sources for learning. The learning unit can also refer to past credibility evaluations of the testimonial provider during learning and prioritize highly reliable information for learning. In this way, by prioritizing highly reliable information for learning, the reliability of the countermeasures and advice provided by the AI ​​can be improved.

[0042] The learning unit can apply different learning algorithms depending on the category of the personal story during learning. For example, when learning personal stories about disasters, the learning unit applies a learning algorithm specific to disasters. Furthermore, when learning personal stories about wars, the learning unit can also apply a learning algorithm specific to wars. Furthermore, the learning unit can select an appropriate learning algorithm depending on the category of the personal story and perform learning. In this way, by selecting an appropriate learning algorithm depending on the category of the personal story, the personal stories can be effectively learned.

[0043] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit selects an optimal learning algorithm by referring to past learning data during learning. The learning unit can also adjust and optimize the learning algorithm based on past learning data during learning. The learning unit can also analyze past learning data during learning to improve the accuracy of the learning algorithm. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to past learning data.

[0044] The learning unit can weight the learning data based on the time of submission of the personal stories during learning. For example, the learning unit weights the learning data by prioritizing recent personal stories during learning. The learning unit can also weight the learning data by prioritizing personal stories submitted at a specific time during learning. The learning unit can also weight the learning data based on the time of submission during learning. In this way, by weighting the learning data based on the time of submission, it is possible to weight the most recent information during learning.

[0045] The learning unit can integrate information from different data sources during learning to expand the learning data. For example, the learning unit can integrate personal experiences from different data sources during learning to expand the learning data. The learning unit can also cross-check information from different data sources during learning to create highly reliable learning data. The learning unit can also integrate information from different data sources during learning to ensure diversity in the learning data. In this way, by integrating information from different data sources, diversity in the learning data can be ensured and the accuracy of learning can be improved.

[0046] The learning unit can perform learning while taking into account attribute information of the provider of the personal story. For example, the learning unit can perform learning while taking into account the age and gender of the provider of the personal story. The learning unit can also perform learning while taking into account the occupation and background of the provider of the personal story. The learning unit can also perform appropriate learning while taking into account attribute information of the provider of the personal story. In this way, by performing learning while taking into account attribute information of the provider of the personal story, it is possible to learn personal stories from a more diverse range of perspectives.

[0047] The reception unit can select the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, by referring to the user's past question history, questions can be received in the optimal method for the user.

[0048] The reception unit can filter questions based on the user's current areas of interest when receiving the questions. For example, the reception unit preferentially receives questions about disasters in which the user is currently interested. The reception unit can also preferentially receive questions about wars in which the user is currently interested. The reception unit can also filter and receive related questions based on the user's current areas of interest. In this way, by filtering questions based on the user's current areas of interest, highly relevant questions can be preferentially received.

[0049] The reception unit can select the optimal reception means depending on the user's input method when receiving the question. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. Furthermore, when the user inputs a question using text, the reception unit can also receive the question using text analysis technology. Furthermore, when the user inputs a question using an image, the reception unit can also receive the question using image recognition technology. In this way, by selecting the optimal reception means depending on the user's input method, it is possible to receive questions in a way that is easy for the user to use.

[0050] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information when receiving the questions. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is in a specific disaster area, the reception unit can also prioritize receiving questions related to that disaster. Furthermore, the reception unit can also prioritize receiving highly relevant questions by taking into account the user's geographical location information. In this way, by preferentially receiving highly relevant questions by taking into account the user's geographical location information, it is possible to provide region-specific measures and advice.

[0051] The reception unit can analyze the user's social media activity at the time of reception and receive related questions. The reception unit, for example, receives questions related to topics in which the user has shown interest on social media. The reception unit can also analyze the content of the user's posts on social media and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive questions based on the user's interests.

[0052] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. The reception unit can also optimize the reception procedure based on the user's past feedback. In this way, by reflecting the user's past feedback, it is possible to receive a question in the optimal method for the user.

[0053] The providing unit can adjust the level of detail to be provided based on the importance of the experience when providing the experience. For example, when providing an experience with high importance, the providing unit provides the experience with detailed information. Furthermore, when providing an experience with low importance, the providing unit can also provide the experience with concise information. Furthermore, the providing unit can adjust the level of detail to be provided based on the importance of the experience. In this way, by adjusting the level of detail to be provided based on the importance of the experience, it is possible to provide optimal information for the user.

[0054] The providing unit can apply different providing algorithms depending on the category of the personal story when providing the personal story. For example, when providing personal stories about disasters, the providing unit applies a providing algorithm specific to disasters. Furthermore, when providing personal stories about wars, the providing unit can also apply a providing algorithm specific to wars. Furthermore, the providing unit can select an appropriate providing algorithm depending on the category of the personal story and provide the personal story. In this way, by selecting an appropriate providing algorithm depending on the category of the personal story, it is possible to provide personal stories effectively.

[0055] The providing unit can improve the accuracy of the provision by referring to the user's past provision results at the time of provision. For example, the providing unit can refer to the user's past provision results at the time of provision and provide the most suitable experience stories. The providing unit can also adjust the provision algorithm based on the user's past provision results at the time of provision to improve accuracy. The providing unit can also analyze the user's past provision results at the time of provision to improve the accuracy of the provision. In this way, the accuracy of the provision can be improved by referring to the user's past provision results.

[0056] The providing unit can determine the priority of provision based on the time of submission of the testimonials at the time of provision. For example, the providing unit can prioritize providing the most recent testimonials at the time of provision. The providing unit can also prioritize providing testimonials submitted at a specific time at the time of provision. The providing unit can also determine the priority of provision based on the time of submission at the time of provision. In this way, by determining the priority of provision based on the time of submission, it is possible to provide the most recent information with an emphasis on it.

[0057] The providing unit can adjust the order of providing the stories based on the relevance of the stories at the time of providing. For example, the providing unit can provide highly relevant stories at the time of providing with priority. The providing unit can also postpone providing less relevant stories at the time of providing. The providing unit can also adjust the order of providing the stories based on the relevance of the stories at the time of providing. In this way, by adjusting the order of providing the stories based on the relevance of the stories, the stories can be provided in an order that is optimal for the user.

[0058] The providing unit can adjust the use of technical terms in the provided stories depending on the user's level of expertise when providing the stories. For example, if the user has technical expertise, the providing unit can provide stories that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can also provide stories that are explained in simple terms. Furthermore, the providing unit can adjust the use of technical terms in the provided stories depending on the user's level of expertise. In this way, by adjusting the use of technical terms depending on the user's level of expertise, it is possible to provide stories that are easy for the user to understand.

[0059] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the testimonial when making a suggestion. For example, when making a suggestion based on a testimonial with high importance, the suggestion unit makes a suggestion including detailed information. Furthermore, when making a suggestion based on a testimonial with low importance, the suggestion unit can also make a suggestion including concise information. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the testimonial. In this way, by adjusting the level of detail of the suggestion based on the importance of the testimonial, it is possible to provide optimal information for the user.

[0060] The suggestion unit can apply different suggestion algorithms depending on the category of the personal story when making a suggestion. For example, when making a suggestion based on a personal story about a disaster, the suggestion unit can apply a suggestion algorithm specific to the disaster. Furthermore, when making a suggestion based on a personal story about a war, the suggestion unit can also apply a suggestion algorithm specific to the war. Furthermore, the suggestion unit can select an appropriate suggestion algorithm depending on the category of the personal story and make a suggestion. In this way, by selecting an appropriate suggestion algorithm depending on the category of the personal story, suggestions can be made effectively.

[0061] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit refers to the user's past suggestion results when making a suggestion and makes an optimal suggestion. The suggestion unit can also adjust the suggestion algorithm based on the user's past suggestion results when making a suggestion to improve accuracy. The suggestion unit can also analyze the user's past suggestion results when making a suggestion to improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0062] The suggestion unit can determine the priority of the proposals based on the time of submission of the experiences at the time of making the proposals. For example, the suggestion unit makes the proposals based on the most recent experiences at the time of making the proposals. The suggestion unit can also make the proposals by prioritizing experiences submitted at a specific time at the time of making the proposals. The suggestion unit can also determine the priority of the proposals based on the time of submission at the time of making the proposals. In this way, by prioritizing the proposals based on the time of submission, it is possible to make proposals by prioritizing the latest information.

[0063] The suggestion unit can adjust the order of suggestions based on the relevance of the experiences at the time of suggestion. For example, the suggestion unit makes suggestions based on highly relevant experiences at the time of suggestion. The suggestion unit can also postpone less relevant experiences at the time of suggestion. The suggestion unit can also adjust the order of suggestions based on the relevance of the experiences at the time of suggestion. In this way, by adjusting the order of suggestions based on the relevance of the experiences, suggestions can be made in an order that is optimal for the user.

[0064] The suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit can make a proposal that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the suggestion unit can make a proposal that explains things in simple terms. Furthermore, the suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise. In this way, by adjusting the use of technical terms depending on the user's level of expertise, it is possible to make a proposal that is easy for the user to understand.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The collection unit can evaluate the credibility of the testimonials when collecting them and prioritize collecting highly reliable information. For example, the collection unit can check the background information of the testimonial providers when collecting them and prioritize collecting highly reliable information. The collection unit can also cross-check the content of the testimonials when collecting them and prioritize collecting matching information from multiple information sources. The collection unit can also refer to past credibility evaluations of the testimonial providers when collecting them and prioritize collecting highly reliable information. In this way, by prioritizing the collection of highly reliable information, the reliability of the countermeasures and advice provided by the AI ​​can be improved.

[0067] The collection unit can adjust the collection method depending on the content of the experience story when collecting it. For example, if a detailed experience story is to be provided when collecting it, the collection unit can collect it in an interview format. Also, if a brief experience story is to be provided when collecting it, the collection unit can collect it in a questionnaire format. Also, the collection unit can select a collection method depending on the level of detail of the experience story when collecting it, and collect it in an appropriate format. In this way, by adjusting the collection method depending on the level of detail of the experience story, it is possible to collect experience stories in an appropriate format.

[0068] The collection department can apply different collection methods depending on the type of story when collecting them. For example, when collecting disaster stories, they can conduct field surveys and interviews. When collecting war stories, the collection department can also collect historical materials and testimonies. The collection department can also select appropriate collection methods depending on the category of the story. This allows for effective collection of stories by selecting appropriate collection methods depending on the category of the story.

[0069] The collection unit can prioritize collecting highly relevant information when collecting stories, taking into account the geographical distribution of the stories. For example, the collection unit can prioritize collecting stories about disasters that occurred in a specific region when collecting stories. The collection unit can also prioritize collecting stories about specific war zones when collecting stories. The collection unit can also prioritize collecting information that is highly geographically relevant when collecting stories. This allows the collection of information that is highly geographically relevant to be prioritized, making it possible to provide region-specific measures and advice.

[0070] The learning unit can evaluate the credibility of testimonials during learning and prioritize highly reliable information for learning. For example, during learning, the learning unit can check the background information of the testimonial provider and prioritize highly reliable information for learning. The learning unit can also cross-check the content of testimonials during learning and prioritize matching information from multiple sources for learning. The learning unit can also refer to past credibility evaluations of testimonial providers during learning and prioritize highly reliable information for learning. In this way, by prioritizing highly reliable information for learning, the reliability of countermeasures and advice provided by the AI ​​can be improved.

[0071] The learning unit can apply different learning algorithms depending on the category of the personal story during learning. For example, when learning personal stories about disasters, a learning algorithm specific to disasters is applied. In addition, when learning personal stories about wars, the learning unit can also apply a learning algorithm specific to wars. In addition, the learning unit can select an appropriate learning algorithm depending on the category of the personal story and perform learning. In this way, by selecting an appropriate learning algorithm depending on the category of the personal story, the personal stories can be effectively learned.

[0072] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit selects an optimal learning algorithm by referring to past learning data during learning. The learning unit can also adjust and optimize the learning algorithm based on past learning data during learning. The learning unit can also analyze past learning data during learning to improve the accuracy of the learning algorithm. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to past learning data.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection team collects stories of past disasters and wars. The team can collect stories through interviews, questionnaires, online posts, etc. They can also collect stories in written, audio, or video format. Step 2: The learning unit trains the AI ​​from the collected experiences. The learning unit can use AI techniques such as neural networks and support vector machines to train the experiences. It can also use learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning. Step 3: The reception unit receives the user's question. The reception unit can receive the user's question in a text format, a voice format, a multiple-choice format, or the like. Step 4: The provider provides appropriate testimonials based on the questions received by the reception unit. The provider can provide testimonials in real time, batch, or by push notification. Step 5: The Proposal Department provides measures and advice based on the experiences provided by the Provision Department. The Proposal Department can provide measures and advice such as specific guidelines for action, risk avoidance measures, and psychological support.

[0075] (Example 2) A system according to an embodiment of the present invention collects personal stories of past disasters and wars, trains AI to learn from them, and provides appropriate personal stories based on user questions and offers countermeasures and advice. This system collects personal stories, trains AI to learn from them, and provides appropriate personal stories based on user questions and offers countermeasures and advice. To prevent past events from fading away, an AI storyteller is created, allowing anyone to ask questions and hear personal stories at any time. For example, if a user asks, "What should I do if an earthquake occurs?", the AI ​​provides specific countermeasures and advice based on past earthquake experiences. Similarly, if a user asks, "I want to know about life during the war," the AI ​​provides past war experiences and explains the living conditions and countermeasures at the time. In this way, users can learn countermeasures for future dangers and risks based on past experiences. This system collects personal stories of past disasters and wars, trains AI to learn from them, and provides appropriate personal stories based on user questions and offers countermeasures and advice. For example, users can learn countermeasures for future dangers and risks based on past experiences. In addition, to prevent past events from fading away, we can create an AI that acts as a storyteller, allowing anyone to ask questions and hear about their experiences at any time.

[0076] The personal experience sharing system according to the embodiment includes a collection unit, a learning unit, a reception unit, a provision unit, and a suggestion unit. The collection unit collects personal experiences of past disasters and wars. The collection unit can collect the personal experiences through methods such as interviews, questionnaires, and online posting. The collection unit can also collect personal experiences in text, audio, and video formats. The learning unit trains an AI based on the collected personal experiences. The learning unit can learn the personal experiences using AI techniques such as neural networks and support vector machines. The learning unit can use learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning. The reception unit accepts user questions. The reception unit can accept user questions in formats such as text, audio, and multiple choice. The provision unit provides appropriate personal experiences based on the questions accepted by the reception unit. The provision unit can provide the personal experiences through methods such as real-time provision, batch provision, and push notification. The suggestion unit provides countermeasures and advice based on the personal experiences provided by the provision unit. The suggestion unit can provide measures and advice such as specific guidelines for action, risk avoidance measures, psychological support, etc. As a result, the experience story providing system according to the embodiment can collect and learn from experiences, provide appropriate experiences based on user questions, and provide measures and advice.

[0077] The collection unit may collect stories of past disasters or wars. The collection unit may collect stories of past disasters or wars by, for example, interviews, questionnaires, online postings, etc. The collection unit may collect stories in written, audio, video, or other formats. In this way, by collecting stories of past disasters or wars, data for proposing measures against future dangers and risks can be obtained.

[0078] The learning unit can make the AI ​​learn from the collected personal experiences. The learning unit can make the AI ​​learn from the collected personal experiences using AI technologies such as neural networks and support vector machines. The learning unit can use learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning. By making the AI ​​learn from the collected personal experiences, the AI ​​can provide appropriate countermeasures and advice.

[0079] The reception unit can receive questions from users. The reception unit can receive questions from users in a format such as text, voice, or multiple choice. By receiving the user's questions, the AI ​​can obtain information to provide appropriate experiences.

[0080] The providing unit can provide appropriate testimonials based on the questions received by the receiving unit. The providing unit can provide appropriate testimonials by methods such as real-time provision, batch provision, push notification, etc. In this way, by providing appropriate testimonials based on the questions received by the receiving unit, it is possible to provide useful information to the user.

[0081] The suggestion unit can provide countermeasures and advice based on the experiences provided by the provision unit. The suggestion unit can provide countermeasures and advice such as specific guidelines for action, risk avoidance measures, and psychological support. As a result, by providing countermeasures and advice based on the experiences provided by the provision unit, specific countermeasures and advice can be provided to the user.

[0082] The collection unit can estimate the user's emotions and adjust the timing of collecting the stories based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to provide the stories when the user is in a relaxed state. Furthermore, if the user is excited, the collection unit can advance the collection timing to provide the stories immediately. Furthermore, if the user is sad, the collection unit can adjust the collection timing to provide the stories when the user is in a calm state. In this way, by adjusting the collection timing of the stories according to the user's emotions, the stories can be collected at the optimal timing for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The collection unit can evaluate the credibility of the testimonials at the time of collection and prioritize collecting highly reliable information. For example, the collection unit can check the background information of the providers of the testimonials at the time of collection and prioritize collecting highly reliable information. The collection unit can also cross-check the content of the testimonials at the time of collection and prioritize collecting matching information from multiple information sources. The collection unit can also refer to past credibility evaluations of the providers of the testimonials at the time of collection and prioritize collecting highly reliable information. In this way, by prioritizing the collection of highly reliable information, the reliability of the countermeasures and advice provided by the AI ​​can be improved.

[0084] The collection unit can adjust the collection method depending on the content of the experiences when collecting them. For example, if detailed experiences are provided when collecting them, the collection unit collects them in an interview format. Also, if brief experiences are provided when collecting them, the collection unit can collect them in a questionnaire format. Also, the collection unit can select a collection method depending on the level of detail of the experiences when collecting them, and collect them in an appropriate format. In this way, by adjusting the collection method depending on the level of detail of the experiences, it is possible to collect experiences in an appropriate format.

[0085] The collection department can apply different collection methods depending on the type of story when collecting them. For example, when collecting disaster stories, the collection department can conduct field surveys and interviews. When collecting war stories, the collection department can also collect historical materials and testimonies. The collection department can also select appropriate collection methods depending on the category of the story and collect them. In this way, by selecting appropriate collection methods depending on the category of the story, it is possible to collect stories effectively.

[0086] The collection unit can estimate the user's emotions and determine the order of the stories to be collected based on the estimated user emotions. For example, if the user is feeling anxious, the collection unit can prioritize collecting stories that provide a sense of security. Furthermore, if the user is excited, the collection unit can prioritize collecting interesting stories. Furthermore, if the user is sad, the collection unit can prioritize collecting comforting stories. In this way, by determining the priority of the stories to be collected according to the user's emotions, it is possible to collect the most suitable stories for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The collection unit can prioritize collecting highly relevant information by taking into consideration the geographical distribution of the experiences during collection. For example, the collection unit can prioritize collecting experiences of disasters that occurred in a specific region during collection. The collection unit can also prioritize collecting experiences of specific war regions during collection. The collection unit can also prioritize collecting information that is highly geographically relevant during collection. In this way, by prioritizing the collection of information that is highly geographically relevant, it is possible to provide measures and advice that are specific to the region.

[0088] The collection unit can collect testimonials taking into account information about the providers of the testimonials. For example, the collection unit can collect testimonials taking into account the age and gender of the testimonial providers. The collection unit can also collect testimonials taking into account the occupation and background of the testimonial providers. The collection unit can also collect appropriate testimonials taking into account attribute information about the testimonial providers. In this way, by collecting testimonials taking into account attribute information about the testimonial providers, it is possible to collect testimonials from a wider variety of perspectives.

[0089] The collection unit can determine the priority of collection based on the time of submission of the experiences at the time of collection. For example, the collection unit prioritizes collection of recent experiences at the time of collection. The collection unit can also prioritize collection of experiences submitted at a specific time at the time of collection. The collection unit can also weight collection based on the time of submission at the time of collection. In this way, weighting collection based on the time of submission makes it possible to prioritize collection of the most recent information.

[0090] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling anxious, the learning unit selects personal stories that provide a sense of security as training data. Furthermore, if the user is excited, the learning unit can select interesting personal stories as training data. Furthermore, if the user is sad, the learning unit can select comforting personal stories as training data. In this way, by selecting training data based on the user's emotions, it is possible to learn the most suitable personal stories for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The learning unit can evaluate the credibility of the testimonials during learning and prioritize highly reliable information for learning. For example, the learning unit can check the background information of the testimonial provider during learning and prioritize highly reliable information for learning. The learning unit can also cross-check the content of the testimonials during learning and prioritize matching information from multiple information sources for learning. The learning unit can also refer to past credibility evaluations of the testimonial provider during learning and prioritize highly reliable information for learning. In this way, by prioritizing highly reliable information for learning, the reliability of the countermeasures and advice provided by the AI ​​can be improved.

[0092] The learning unit can apply different learning algorithms depending on the category of the personal story during learning. For example, when learning personal stories about disasters, the learning unit applies a learning algorithm specific to disasters. Furthermore, when learning personal stories about wars, the learning unit can also apply a learning algorithm specific to wars. Furthermore, the learning unit can select an appropriate learning algorithm depending on the category of the personal story and perform learning. In this way, by selecting an appropriate learning algorithm depending on the category of the personal story, the personal stories can be effectively learned.

[0093] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit selects an optimal learning algorithm by referring to past learning data during learning. The learning unit can also adjust and optimize the learning algorithm based on past learning data during learning. The learning unit can also analyze past learning data during learning to improve the accuracy of the learning algorithm. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to past learning data.

[0094] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, if the user is feeling anxious, the learning unit can increase the frequency of learning to learn reassuring stories. Furthermore, if the user is excited, the learning unit can adjust the frequency of learning to learn interesting stories. Furthermore, if the user is sad, the learning unit can adjust the frequency of learning to learn comforting stories. In this way, by adjusting the frequency of learning based on the user's emotions, learning can be performed at an optimal time for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The learning unit can weight the learning data based on the time of submission of the personal stories during learning. For example, the learning unit weights the learning data by prioritizing recent personal stories during learning. The learning unit can also weight the learning data by prioritizing personal stories submitted at a specific time during learning. The learning unit can also weight the learning data based on the time of submission during learning. In this way, by weighting the learning data based on the time of submission, it is possible to weight the most recent information during learning.

[0096] The learning unit can integrate information from different data sources during learning to expand the learning data. For example, the learning unit can integrate personal experiences from different data sources during learning to expand the learning data. The learning unit can also cross-check information from different data sources during learning to create highly reliable learning data. The learning unit can also integrate information from different data sources during learning to ensure diversity in the learning data. In this way, by integrating information from different data sources, diversity in the learning data can be ensured and the accuracy of learning can be improved.

[0097] The learning unit can perform learning while taking into account attribute information of the provider of the personal story. For example, the learning unit can perform learning while taking into account the age and gender of the provider of the personal story. The learning unit can also perform learning while taking into account the occupation and background of the provider of the personal story. The learning unit can also perform appropriate learning while taking into account attribute information of the provider of the personal story. In this way, by performing learning while taking into account attribute information of the provider of the personal story, it is possible to learn personal stories from a more diverse range of perspectives.

[0098] The reception unit can estimate the user's emotions and adjust the method for accepting questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and quickly accept questions. By adjusting the method for accepting questions according to the user's emotions, questions can be accepted in a manner optimal for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The reception unit can select the optimal reception method by referring to the user's past question history when receiving a question. For example, the reception unit automatically displays questions that the user has frequently asked in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest questions that will be used in a specific time period based on the user's past question history. In this way, by referring to the user's past question history, questions can be received in the optimal method for the user.

[0100] The reception unit can filter questions based on the user's current areas of interest when receiving the questions. For example, the reception unit preferentially receives questions about disasters in which the user is currently interested. The reception unit can also preferentially receive questions about wars in which the user is currently interested. The reception unit can also filter and receive related questions based on the user's current areas of interest. In this way, by filtering questions based on the user's current areas of interest, highly relevant questions can be preferentially received.

[0101] The reception unit can select the optimal reception means depending on the user's input method when receiving the question. For example, when the user inputs a question by voice, the reception unit receives the question using voice recognition technology. Furthermore, when the user inputs a question using text, the reception unit can also receive the question using text analysis technology. Furthermore, when the user inputs a question using an image, the reception unit can also receive the question using image recognition technology. In this way, by selecting the optimal reception means depending on the user's input method, it is possible to receive questions in a way that is easy for the user to use.

[0102] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, if the user is feeling anxious, the reception unit can prioritize receiving questions that provide a sense of security. Furthermore, if the user is excited, the reception unit can prioritize receiving questions that pique the user's interest. Furthermore, if the user is sad, the reception unit can prioritize receiving questions that provide comfort. In this way, by determining the priority of questions according to the user's emotions, it is possible to prioritize receiving questions that are most suitable for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The reception unit can prioritize receiving highly relevant questions by taking into account the user's geographical location information when receiving the questions. For example, if the user is in a specific area, the reception unit can prioritize receiving questions related to that area. Furthermore, if the user is in a specific disaster area, the reception unit can also prioritize receiving questions related to that disaster. Furthermore, the reception unit can also prioritize receiving highly relevant questions by taking into account the user's geographical location information. In this way, by preferentially receiving highly relevant questions by taking into account the user's geographical location information, it is possible to provide region-specific measures and advice.

[0104] The reception unit can analyze the user's social media activity at the time of reception and receive related questions. The reception unit, for example, receives questions related to topics in which the user has shown interest on social media. The reception unit can also analyze the content of the user's posts on social media and receive related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive questions based on the user's interests.

[0105] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a question. The reception unit can, for example, suggest the optimal reception method based on feedback provided by the user in the past. The reception unit can also customize the reception interface by reflecting the user's past feedback. The reception unit can also optimize the reception procedure based on the user's past feedback. In this way, by reflecting the user's past feedback, it is possible to receive a question in the optimal method for the user.

[0106] The providing unit can estimate the user's emotions and adjust the method of providing the stories based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can provide reassuring stories. Furthermore, if the user is excited, the providing unit can provide interesting stories. Furthermore, if the user is sad, the providing unit can provide comforting stories. In this way, by adjusting the method of providing stories according to the user's emotions, it is possible to provide stories in a way that is optimal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] The providing unit can adjust the level of detail to be provided based on the importance of the experience when providing the experience. For example, when providing an experience with high importance, the providing unit provides the experience with detailed information. Furthermore, when providing an experience with low importance, the providing unit can also provide the experience with concise information. Furthermore, the providing unit can adjust the level of detail to be provided based on the importance of the experience. In this way, by adjusting the level of detail to be provided based on the importance of the experience, it is possible to provide optimal information for the user.

[0108] The providing unit can apply different providing algorithms depending on the category of the personal story when providing the personal story. For example, when providing personal stories about disasters, the providing unit applies a providing algorithm specific to disasters. Furthermore, when providing personal stories about wars, the providing unit can also apply a providing algorithm specific to wars. Furthermore, the providing unit can select an appropriate providing algorithm depending on the category of the personal story and provide the personal story. In this way, by selecting an appropriate providing algorithm depending on the category of the personal story, it is possible to provide personal stories effectively.

[0109] The providing unit can improve the accuracy of the provision by referring to the user's past provision results at the time of provision. For example, the providing unit can refer to the user's past provision results at the time of provision and provide the most suitable experience stories. The providing unit can also adjust the provision algorithm based on the user's past provision results at the time of provision to improve accuracy. The providing unit can also analyze the user's past provision results at the time of provision to improve the accuracy of the provision. In this way, the accuracy of the provision can be improved by referring to the user's past provision results.

[0110] The providing unit can estimate the user's emotions and adjust the length of the story to be provided based on the estimated user emotions. For example, if the user is feeling anxious, the providing unit can provide a short, to-the-point story. If the user is relaxed, the providing unit can also provide a longer story with detailed explanations. If the user is excited, the providing unit can also provide a story with visually stimulating effects. In this way, by adjusting the length of the story according to the user's emotions, it is possible to provide a story with an optimal length for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0111] The providing unit can determine the priority of provision based on the time of submission of the testimonials at the time of provision. For example, the providing unit can prioritize providing the most recent testimonials at the time of provision. The providing unit can also prioritize providing testimonials submitted at a specific time at the time of provision. The providing unit can also determine the priority of provision based on the time of submission at the time of provision. In this way, by determining the priority of provision based on the time of submission, it is possible to provide the most recent information with an emphasis on it.

[0112] The providing unit can adjust the order of providing the stories based on the relevance of the stories at the time of providing. For example, the providing unit can provide highly relevant stories at the time of providing with priority. The providing unit can also postpone providing less relevant stories at the time of providing. The providing unit can also adjust the order of providing the stories based on the relevance of the stories at the time of providing. In this way, by adjusting the order of providing the stories based on the relevance of the stories, the stories can be provided in an order that is optimal for the user.

[0113] The providing unit can adjust the use of technical terms in the provided stories depending on the user's level of expertise when providing the stories. For example, if the user has technical expertise, the providing unit can provide stories that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can also provide stories that are explained in simple terms. Furthermore, the providing unit can adjust the use of technical terms in the provided stories depending on the user's level of expertise. In this way, by adjusting the use of technical terms depending on the user's level of expertise, it is possible to provide stories that are easy for the user to understand.

[0114] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can make suggestions using an expression that gives a sense of security. Furthermore, if the user is excited, the suggestion unit can make suggestions using an expression that attracts interest. Furthermore, if the user is sad, the suggestion unit can make suggestions using an expression that provides comfort. In this way, by adjusting the way in which suggestions are expressed according to the user's emotions, suggestions can be made in a way that is optimal for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0115] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the testimonial when making a suggestion. For example, when making a suggestion based on a testimonial with high importance, the suggestion unit makes a suggestion including detailed information. Furthermore, when making a suggestion based on a testimonial with low importance, the suggestion unit can also make a suggestion including concise information. Furthermore, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the testimonial. In this way, by adjusting the level of detail of the suggestion based on the importance of the testimonial, it is possible to provide optimal information for the user.

[0116] The suggestion unit can apply different suggestion algorithms depending on the category of the personal story when making a suggestion. For example, when making a suggestion based on a personal story about a disaster, the suggestion unit can apply a suggestion algorithm specific to the disaster. Furthermore, when making a suggestion based on a personal story about a war, the suggestion unit can also apply a suggestion algorithm specific to the war. Furthermore, the suggestion unit can select an appropriate suggestion algorithm depending on the category of the personal story and make a suggestion. In this way, by selecting an appropriate suggestion algorithm depending on the category of the personal story, suggestions can be made effectively.

[0117] The suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results when making a suggestion. For example, the suggestion unit refers to the user's past suggestion results when making a suggestion and makes an optimal suggestion. The suggestion unit can also adjust the suggestion algorithm based on the user's past suggestion results when making a suggestion to improve accuracy. The suggestion unit can also analyze the user's past suggestion results when making a suggestion to improve the accuracy of the suggestion. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results.

[0118] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is feeling anxious, the suggestion unit can make short, to-the-point suggestions. If the user is relaxed, the suggestion unit can also make longer suggestions including detailed explanations. If the user is excited, the suggestion unit can also make suggestions with visually stimulating effects. By adjusting the length of the suggestions according to the user's emotions, it is possible to make suggestions with an optimal length for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0119] The suggestion unit can determine the priority of the proposals based on the time of submission of the experiences at the time of making the proposals. For example, the suggestion unit makes the proposals based on the most recent experiences at the time of making the proposals. The suggestion unit can also make the proposals by prioritizing experiences submitted at a specific time at the time of making the proposals. The suggestion unit can also determine the priority of the proposals based on the time of submission at the time of making the proposals. In this way, by prioritizing the proposals based on the time of submission, it is possible to make proposals by prioritizing the latest information.

[0120] The suggestion unit can adjust the order of suggestions based on the relevance of the experiences at the time of suggestion. For example, the suggestion unit makes suggestions based on highly relevant experiences at the time of suggestion. The suggestion unit can also postpone less relevant experiences at the time of suggestion. The suggestion unit can also adjust the order of suggestions based on the relevance of the experiences at the time of suggestion. In this way, by adjusting the order of suggestions based on the relevance of the experiences, suggestions can be made in an order that is optimal for the user.

[0121] The suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise when making a proposal. For example, if the user has technical knowledge, the suggestion unit can make a proposal that uses a lot of technical terms. Furthermore, if the user does not have technical knowledge, the suggestion unit can make a proposal that explains things in simple terms. Furthermore, the suggestion unit can adjust the use of technical terms in the proposal depending on the user's level of expertise. In this way, by adjusting the use of technical terms depending on the user's level of expertise, it is possible to make a proposal that is easy for the user to understand. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, learning unit, reception unit, provision unit, and suggestion 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 collection unit collects personal stories using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 by the control unit 46A. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and causes an AI to learn the collected personal stories. The reception unit receives questions from users using the touch panel 38A and microphone 38B of the smart device 14 and transmits the questions to the data processing device 12 by the control unit 46A. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate personal stories based on the user's questions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides measures and advice based on the provided personal stories. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, learning unit, reception unit, provision unit, and suggestion 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 collection unit collects testimonials using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 by the control unit 46A. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and causes the AI ​​to learn the collected testimonials. The reception unit receives a user's question using the microphone 238 of the smart glasses 214 and transmits the question to the data processing device 12 by the control unit 46A. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate testimonials based on the user's question. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides measures and advice based on the provided testimonials. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, reception unit, provision unit, and suggestion 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 collection unit collects personal stories using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the collected data to the data processing device 12 by the control unit 46A. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and causes the AI ​​to learn the collected personal stories. The reception unit receives questions from users using the microphone 238 of the headset-type terminal 314 and transmits them to the data processing device 12 by the control unit 46A. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate personal stories based on the user's questions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides measures and advice based on the provided personal stories. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, learning unit, reception unit, provision unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal stories using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 by the control unit 46A. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and causes the AI ​​to learn the collected personal stories. The reception unit receives questions from users using the microphone 238 of the robot 414 and transmits them to the data processing device 12 by the control unit 46A. The provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides appropriate personal stories based on the user's questions. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and provides measures and advice based on the provided personal stories.

[0122] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0123] The collection unit can estimate the user's emotions and adjust the timing of collecting the stories based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to provide the stories when the user is in a relaxed state. Furthermore, if the user is excited, the collection unit can also advance the collection timing to provide the stories immediately. Furthermore, if the user is sad, the collection unit can also adjust the collection timing to provide the stories when the user is in a calm state. In this way, by adjusting the timing of collecting the stories according to the user's emotions, the stories can be collected at the optimal timing for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0124] The collection unit can evaluate the credibility of the testimonials when collecting them and prioritize collecting highly reliable information. For example, the collection unit can check the background information of the testimonial providers when collecting them and prioritize collecting highly reliable information. The collection unit can also cross-check the content of the testimonials when collecting them and prioritize collecting matching information from multiple information sources. The collection unit can also refer to past credibility evaluations of the testimonial providers when collecting them and prioritize collecting highly reliable information. In this way, by prioritizing the collection of highly reliable information, the reliability of the countermeasures and advice provided by the AI ​​can be improved.

[0125] The collection unit can adjust the collection method depending on the content of the experience story when collecting it. For example, if a detailed experience story is to be provided when collecting it, the collection unit can collect it in an interview format. Also, if a brief experience story is to be provided when collecting it, the collection unit can collect it in a questionnaire format. Also, the collection unit can select a collection method depending on the level of detail of the experience story when collecting it, and collect it in an appropriate format. In this way, by adjusting the collection method depending on the level of detail of the experience story, it is possible to collect experience stories in an appropriate format.

[0126] The collection department can apply different collection methods depending on the type of story when collecting them. For example, when collecting disaster stories, they can conduct field surveys and interviews. When collecting war stories, the collection department can also collect historical materials and testimonies. The collection department can also select appropriate collection methods depending on the category of the story. This allows for effective collection of stories by selecting appropriate collection methods depending on the category of the story.

[0127] The collection unit can prioritize collecting highly relevant information when collecting stories, taking into account the geographical distribution of the stories. For example, the collection unit can prioritize collecting stories about disasters that occurred in a specific region when collecting stories. The collection unit can also prioritize collecting stories about specific war zones when collecting stories. The collection unit can also prioritize collecting information that is highly geographically relevant when collecting stories. This allows the collection of information that is highly geographically relevant to be prioritized, making it possible to provide region-specific measures and advice.

[0128] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is feeling anxious, it selects personal stories that give a sense of security as training data. In addition, if the user is excited, the learning unit can select interesting personal stories as training data. In addition, if the user is sad, the learning unit can select comforting personal stories as training data. In this way, by selecting training data based on the user's emotions, it is possible to learn the most suitable personal stories for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0129] The learning unit can evaluate the credibility of testimonials during learning and prioritize highly reliable information for learning. For example, during learning, the learning unit can check the background information of the testimonial provider and prioritize highly reliable information for learning. The learning unit can also cross-check the content of testimonials during learning and prioritize matching information from multiple sources for learning. The learning unit can also refer to past credibility evaluations of testimonial providers during learning and prioritize highly reliable information for learning. In this way, by prioritizing highly reliable information for learning, the reliability of countermeasures and advice provided by the AI ​​can be improved.

[0130] The learning unit can apply different learning algorithms depending on the category of the personal story during learning. For example, when learning personal stories about disasters, a learning algorithm specific to disasters is applied. In addition, when learning personal stories about wars, the learning unit can also apply a learning algorithm specific to wars. In addition, the learning unit can select an appropriate learning algorithm depending on the category of the personal story and perform learning. In this way, by selecting an appropriate learning algorithm depending on the category of the personal story, the personal stories can be effectively learned.

[0131] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit selects an optimal learning algorithm by referring to past learning data during learning. The learning unit can also adjust and optimize the learning algorithm based on past learning data during learning. The learning unit can also analyze past learning data during learning to improve the accuracy of the learning algorithm. In this way, the accuracy of learning can be improved by optimizing the learning algorithm by referring to past learning data.

[0132] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, if the user is feeling anxious, the learning frequency can be increased to allow the user to learn reassuring stories. Furthermore, if the user is excited, the learning unit can adjust the frequency of learning to allow the user to learn interesting stories. Furthermore, if the user is sad, the learning unit can adjust the frequency of learning to allow the user to learn comforting stories. Adjusting the learning frequency based on the user's emotions allows learning to occur at the optimal time for the user. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0133] The processing flow of the second embodiment will be briefly explained below.

[0134] Step 1: The collection team collects stories of past disasters and wars. The team can collect stories through interviews, questionnaires, online posts, etc. They can also collect stories in written, audio, or video format. Step 2: The learning unit trains the AI ​​from the collected experiences. The learning unit can use AI techniques such as neural networks and support vector machines to train the experiences. It can also use learning algorithms such as supervised learning, unsupervised learning, and reinforcement learning. Step 3: The reception unit receives the user's question. The reception unit can receive the user's question in a text format, a voice format, a multiple-choice format, or the like. Step 4: The provider provides appropriate testimonials based on the questions received by the reception unit. The provider can provide testimonials in real time, batch, or by push notification. Step 5: The Proposal Department provides measures and advice based on the experiences provided by the Provision Department. The Proposal Department can provide measures and advice such as specific guidelines for action, risk avoidance measures, and psychological support.

[0135] 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.

[0136] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0140] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[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 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.

[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 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.

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

[0153] 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.

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

[0156] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0186] 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.

[0187] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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).

[0192] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0193] 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."

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] [Explanation of symbols]

[0207] 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 collection department that collects testimonials, a learning unit that causes an AI to learn the experiences collected by the collection unit; a reception unit that receives questions from users; a providing unit that provides testimonials based on the questions received by the receiving unit; a suggestion unit that provides measures and advice based on the experiences provided by the providing unit; Equipped with A system characterized by:

2. The collecting unit Collecting stories of past disasters or wars 2. The system of claim 1.

3. The learning unit Letting AI learn from collected experiences 2. The system of claim 1.

4. The reception unit Accepting user questions 2. The system of claim 1.

5. The providing unit Providing appropriate testimonials based on the questions received by the reception unit 2. The system of claim 1.

6. The proposal unit Provide measures and advice based on the experiences provided by the provider 2. The system of claim 1.

7. The collecting unit Estimate user emotions and adjust the timing of collecting testimonials based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Evaluate the credibility of testimonials when collecting them and prioritize information collection 2. The system of claim 1.

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