system

Abstract

The system according to the embodiment aims to provide appropriate advice in multiple languages ​​to users in response to their living situations and problems. [Solution] A system according to an embodiment includes a collection unit, an estimation unit, a generation unit, a translation unit, and a provision unit. The collection unit collects information on a user's living situation and problems. The estimation unit estimates the user's situation based on the information collected by the collection unit. The generation unit generates advice based on the situation estimated by the estimation unit. The translation unit translates the advice generated by the generation unit. The provision unit provides the advice translated by the translation unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide appropriate advice in multiple languages ​​to users about their living situations and problems.

[0005] The system according to the embodiment aims to provide appropriate advice in multiple languages ​​to users in response to their living situations and problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an estimation unit, a generation unit, a translation unit, and a provision unit. The collection unit collects information on the user's living situation and problems. The estimation unit estimates the user's situation based on the information collected by the collection unit. The generation unit generates advice based on the situation estimated by the estimation unit. The translation unit translates the advice generated by the generation unit. The provision unit provides the advice translated by the translation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate advice in multiple languages ​​to users regarding their living situations and problems. [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) In an embodiment of the present invention, an advice providing system uses a situation estimation AI to analyze a user's living situation and problems, and generates advice from historical figures and experts based on the analysis. Furthermore, this advice is provided in multiple languages ​​by a real-time interpretation AI in a language that the user can easily understand. First, information about the user's living situation and problems is input. For example, if the user inputs "I'm stressed at work," the information is sent to the situation estimation AI. The situation estimation AI analyzes the input information and estimates the user's situation. For example, it estimates that the user is experiencing work-related stress. Next, based on the situation estimated by the situation estimation AI, advice from historical figures and experts is generated. For example, if the user is experiencing work-related stress, advice from the historical figure Albert Einstein is generated. This advice is customized to the user's situation. The generated advice is then sent to the real-time interpretation AI and made multilingual. For example, if the user can easily understand Japanese, the advice is translated into Japanese. The real-time interpretation AI translates the advice into an appropriate language based on the user's language settings. Finally, the translated advice is provided to the user. Users can receive advice tailored to their situation in a language they can easily understand. For example, if the user can easily understand Japanese, advice will be provided in Japanese. This system allows users to receive appropriate advice for their living situation and problems. In addition, multilingual support means it can also accommodate users who speak different languages. This allows the advice providing system to provide appropriate advice in multiple languages ​​for the user's living situation and problems.

[0029] An advice providing system according to an embodiment includes a collection unit, an estimation unit, a generation unit, a translation unit, and a provision unit. The collection unit collects information about a user's living situation and problems. The collection unit can, for example, collect text data and voice data input by the user. The collection unit can also acquire information about the user's living situation from a sensor or an external database. For example, the collection unit can acquire location information and activity data from the user's smartphone. The estimation unit estimates the user's situation based on the information collected by the collection unit. The estimation unit can, for example, analyze the user's situation using a machine learning algorithm. The estimation unit analyzes the user's input data and collected data to identify the problems and situations the user is facing. For example, if the user inputs "I'm stressed at work," the estimation unit analyzes the information and estimates that the user is experiencing work stress. The generation unit generates advice based on the situation estimated by the estimation unit. For example, the generation unit can generate advice from historical figures or experts using a generation AI. The generation unit generates advice customized to the user's situation. For example, if the user is feeling stressed at work, the generation unit generates advice from the historical figure Einstein. The translation unit translates the advice generated by the generation unit based on the user's language setting. The translation unit can translate the advice into multiple languages, for example, using real-time interpretation AI. The translation unit translates the advice into a language that is easy for the user to understand. For example, if the user can easily understand Japanese, the translation unit translates the advice into Japanese. The provision unit provides the user with the advice translated by the translation unit. The provision unit can, for example, display the advice on the user's device. The provision unit provides the advice in a format that is easy for the user to receive. For example, the provision unit sends a notification to the user's smartphone and displays the advice. As a result, the advice provision system according to the embodiment can provide appropriate advice in multiple languages ​​for the user's living situation or problems.

[0030] The collection unit can analyze the user's past behavioral history and select the most effective information collection method. The collection unit can, for example, collect information based on keywords that the user frequently searched for in the past. The collection unit can also, for example, analyze the user's past browsing history and collect related information. The collection unit can also, for example, refer to the user's past purchase history to collect related information. This makes it possible to collect information based on the user's past behavioral history. The behavioral history is analyzed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and cause the generation AI to select the optimal information collection method.

[0031] When collecting information, the collection unit can select information based on the user's current activity status. For example, if the user is at work, the collection unit prioritizes collecting work-related information. For example, if the user is on vacation, the collection unit can also collect information related to relaxation. For example, if the user is exercising, the collection unit can also collect health-related information. This makes it possible to collect information according to the user's current activity status. The activity status is analyzed using, for example, data from a sensor or an external database. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity status data to a generation AI and have the generation AI select the information.

[0032] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. For example, when the user is traveling, the collection unit can also prioritize collecting information related to the travel destination. For example, when the user is at home, the collection unit can also prioritize collecting information around the user's home. This enables information collection based on the user's geographical location information. The collection of geographical location information is performed using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0033] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. For example, the collection unit can analyze the content of posts from accounts the user follows and collect related information. For example, the collection unit can also collect related information based on the activities of groups and communities the user participates in. This makes it possible to collect information based on the user's social media activity. The analysis of social media activity is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related information.

[0034] The estimation unit can adjust the accuracy of the estimation based on the importance of the collected information during estimation. The estimation unit, for example, performs detailed estimation based on information of high importance. The estimation unit can also perform simple estimation based on information of low importance. The estimation unit can also perform estimation with an appropriate level of detail based on information of medium importance. This enables detailed estimation according to the importance of the information. The importance is evaluated using criteria such as frequency, impact, and urgency. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input importance data of the collected information to the generation AI and cause the generation AI to adjust the accuracy of the estimation.

[0035] The estimation unit can apply different estimation methods depending on the category of information during estimation. For example, the estimation unit applies a health-specific estimation algorithm to information related to health. For example, the estimation unit can also apply a work-specific estimation algorithm to information related to work. For example, the estimation unit can also apply a human relationship-specific estimation algorithm to information related to human relationships. This enables estimation according to the category of information. Category classification is performed using criteria such as health, economy, and education. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input information category data to the generation AI and cause the generation AI to apply the estimation method.

[0036] During estimation, the estimation unit can determine the order of estimation based on the time of submission of information. For example, the estimation unit prioritizes the most recent information. For example, the estimation unit can also estimate older information with a lower priority. For example, the estimation unit can estimate information of moderate recency with an appropriate priority. This enables estimation according to the time of submission of information. The evaluation of the submission time is performed using criteria such as the submission date, submission time, and submission frequency. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without AI. For example, the estimation unit can input information submission time data to the generation AI and have the generation AI determine the order of estimation.

[0037] The estimation unit can determine the order of estimation based on the relevance of information during estimation. For example, the estimation unit prioritizes estimation of information with high relevance. For example, the estimation unit can also postpone estimation of information with low relevance. For example, the estimation unit can also estimate information with moderate relevance in an appropriate order. This enables estimation according to the relevance of information. The evaluation of relevance is performed using criteria such as a common theme or related keywords. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input information relevance data to a generation AI and have the generation AI determine the order of estimation.

[0038] When generating advice, the generation unit can adjust the accuracy of the advice based on the estimated importance of the situation. For example, the generation unit generates detailed advice for a situation of high importance. For example, the generation unit can also generate simple advice for a situation of low importance. For example, the generation unit can also generate advice with an appropriate level of detail for a situation of medium importance. This enables the level of detail of the advice to be adjusted according to the importance of the situation. The importance is evaluated using criteria such as frequency, impact, and urgency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input estimated situation importance data to the generation AI and cause the generation AI to adjust the accuracy of the advice.

[0039] When generating advice, the generation unit can apply different advice generation methods depending on the category of the situation. For example, the generation unit applies a health-specific advice generation algorithm to a health-related situation. For example, the generation unit can also apply a work-specific advice generation algorithm to a work-related situation. For example, the generation unit can also apply a human relationship-specific advice generation algorithm to a human relationship-related situation. This makes it possible to generate advice according to the category of the situation. Categories are classified using criteria such as health, economy, and education. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input situation category data into the generation AI and cause the generation AI to apply the advice generation method.

[0040] When generating advice, the generation unit can determine the order of advice based on the submission time of the estimated situation. For example, the generation unit generates advice with priority for the most recent situation. For example, the generation unit can also generate advice with a lower priority for an older situation. For example, the generation unit can also generate advice with a moderate priority for a situation of moderate recency. This enables prioritization of advice according to the submission time of the situation. The submission time is evaluated using criteria such as the submission date, submission time, and submission frequency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission time data of the estimated situation to the generation AI and cause the generation AI to determine the order of advice.

[0041] When generating advice, the generation unit can determine the order of advice based on the relevance of the estimated situation. For example, the generation unit generates advice preferentially for situations with high relevance. For example, the generation unit can also generate advice later for situations with low relevance. For example, the generation unit can also generate advice in an appropriate order for situations with medium relevance. This makes it possible to order advice according to the relevance of the situation. The evaluation of relevance is performed using criteria such as a common theme or related keywords. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the estimated situation to the generation AI and cause the generation AI to determine the order of advice.

[0042] The translation unit can adjust the accuracy of the translation based on the importance of the advice during translation. For example, the translation unit provides a detailed translation for advice of high importance. For example, the translation unit can also provide a simple translation for advice of low importance. For example, the translation unit can also provide a translation with an appropriate level of detail for advice of medium importance. This enables the level of detail of the translation to be adjusted according to the importance of the advice. The importance is evaluated using criteria such as frequency, impact, and urgency. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input importance data of the advice to the generation AI and cause the generation AI to adjust the accuracy of the translation.

[0043] The translation unit can apply different translation methods depending on the category of advice during translation. For example, the translation unit can apply a translation algorithm dedicated to health to advice about health. For example, the translation unit can also apply a translation algorithm dedicated to work to advice about work. For example, the translation unit can also apply a translation algorithm dedicated to human relationships to advice about human relationships. This enables translation according to the category of advice. Categories are classified using criteria such as health, economy, education, etc. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input advice category data into a generation AI and cause the generation AI to apply a translation method.

[0044] During translation, the translation unit can determine the order of translation based on the submission time of the advice. For example, the translation unit prioritizes translating the most recent advice. For example, the translation unit can also translate older advice with a lower priority. For example, the translation unit can translate advice that is of moderate recency with a moderate priority. This enables prioritization of translation according to the submission time of the advice. The submission time is evaluated using criteria such as the submission date, submission time, and submission frequency. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data on the submission time of the advice to the generation AI and have the generation AI determine the order of translation.

[0045] During translation, the translation unit can determine the order of translation based on the relevance of the advice. For example, the translation unit prioritizes translation of advice with high relevance. For example, the translation unit can also translate advice with low relevance later. For example, the translation unit can translate advice with medium relevance in an appropriate order. This enables the order of translation to be determined according to the relevance of the advice. The evaluation of relevance is performed using criteria such as a common theme or related keywords. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input relevance data of the advice to a generation AI and have the generation AI determine the order of translation.

[0046] When providing advice, the providing unit can select the most effective delivery method by referring to the user's past advice receiving history. The providing unit, for example, provides advice in a format that the user has previously preferred. The providing unit can also provide advice by avoiding a format that the user has previously rejected. The providing unit can, for example, analyze the user's past receiving history and select the optimal delivery method. This makes it possible to provide advice based on the user's past receiving history. The analysis of the receiving history is performed, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's receiving history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0047] When providing advice, the providing unit can adjust the providing means based on the user's current activity status. For example, when the user is at work, the providing unit can provide short and to-the-point advice. For example, when the user is on vacation, the providing unit can also provide detailed advice. For example, when the user is exercising, the providing unit can also provide advice by voice. This makes it possible to provide advice according to the user's current activity status. The activity status is analyzed using data from a sensor or an external database, for example. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into the generating AI and cause the generating AI to adjust the providing means.

[0048] When providing advice, the providing unit can select the most effective delivery method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide advice related to that area. For example, if the user is traveling, the providing unit can also provide advice related to the travel destination. For example, if the user is at home, the providing unit can also provide information about the area around the user's home. This makes it possible to provide advice based on the user's geographical location information. The geographical location information is collected using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0049] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit can, for example, provide relevant advice based on information shared by the user on social media. The providing unit can also, for example, analyze the content posted by accounts the user follows and provide relevant advice. The providing unit can also, for example, provide relevant advice based on the activity content of groups or communities the user participates in. This makes it possible to provide advice based on the user's social media activity. The analysis of social media activity is performed, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of providing the advice.

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

[0051] The providing unit can refer to the user's past advice receiving history and select the most effective delivery format. For example, it can provide advice in a format that the user has previously preferred. It can also provide advice by avoiding formats that the user has previously rejected. It can also analyze the user's past receiving history and select the optimal delivery format. This makes it possible to provide advice based on the user's past receiving history. The analysis of the receiving history is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's receiving history data into the generating AI and have the generating AI select the optimal delivery format.

[0052] The providing unit can adjust the means for providing advice based on the user's current activity status. For example, if the user is at work, short, to-the-point advice can be provided. If the user is on vacation, detailed advice can be provided. If the user is exercising, advice can be provided via voice. This makes it possible to provide advice according to the user's current activity status. The activity status is analyzed using, for example, data from a sensor or an external database. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into the generating AI and have the generating AI adjust the means for providing advice.

[0053] The providing unit can select the most effective delivery method based on the user's geographical location information. For example, if the user is in a specific area, advice related to that area can be provided. If the user is traveling, advice related to the travel destination can be provided. If the user is at home, information about the area around the user's home can be provided. This makes it possible to provide advice based on the user's geographical location information. The geographical location information is collected using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generating AI and cause the generating AI to select the optimal delivery method.

[0054] The providing unit can analyze the user's social media activity and provide relevant advice. For example, it can provide relevant advice based on information shared by the user on social media. It can also analyze the content posted by accounts the user follows and provide relevant advice. It can also provide relevant advice based on the activities of groups and communities the user participates in. This makes it possible to provide advice based on the user's social media activity. The analysis of social media activity is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generating AI and have the generating AI suggest a means of providing advice.

[0055] When providing advice, the providing unit can select the most effective method of providing advice by referring to the user's past advice receiving history. For example, the advice can be provided in a format that the user has previously preferred. The advice can also be provided by avoiding a format that the user has previously rejected. The user's past receiving history can also be analyzed to select the optimal method of providing advice. This makes it possible to provide advice based on the user's past receiving history. The analysis of the receiving history is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's receiving history data into the generating AI and have the generating AI select the optimal method of providing advice.

[0056] When providing advice, the providing unit can adjust the means of providing advice based on the user's current activity status. For example, if the user is at work, short, to-the-point advice can be provided. If the user is on vacation, detailed advice can be provided. If the user is exercising, advice can be provided by voice. This makes it possible to provide advice according to the user's current activity status. The activity status is analyzed using, for example, data from a sensor or an external database. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into the generating AI and have the generating AI adjust the means of providing advice.

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

[0058] Step 1: The collection unit collects information about the user's living situation and problems. The collection unit can collect text data and voice data entered by the user. The collection unit can also obtain information about the user's living situation from sensors or external databases. For example, the collection unit can obtain location information and activity data from the user's smartphone. Step 2: The estimation unit estimates the user's situation based on the information collected by the collection unit. The estimation unit uses a machine learning algorithm to analyze the user's situation and identify the problems and situations the user is facing. For example, if the user inputs "I'm very stressed at work," the estimation unit analyzes the information and estimates that the user is experiencing work stress. Step 3: The generator generates advice based on the situation estimated by the estimation unit. The generator uses AI to generate advice from historical figures and experts, providing advice customized to the user's situation. For example, if the user is feeling stressed at work, the generator generates advice from Einstein. Step 4: The translation unit translates the advice generated by the generation unit based on the user's language settings. The translation unit uses real-time interpretation AI to translate the advice into multiple languages ​​and into a language that the user can easily understand. For example, if the user can easily understand Japanese, the advice is translated into Japanese. Step 5: The providing unit provides the advice translated by the translation unit to the user. The providing unit displays the advice on the user's device and provides the advice in a format that is easy for the user to receive. For example, the providing unit sends a notification to the user's smartphone and displays the advice.

[0059] (Example 2) In an embodiment of the present invention, an advice providing system uses a situation estimation AI to analyze a user's living situation and problems, and generates advice from historical figures and experts based on the analysis. Furthermore, this advice is provided in multiple languages ​​by a real-time interpretation AI in a language that the user can easily understand. First, information about the user's living situation and problems is input. For example, if the user inputs "I'm stressed at work," the information is sent to the situation estimation AI. The situation estimation AI analyzes the input information and estimates the user's situation. For example, it estimates that the user is experiencing work-related stress. Next, based on the situation estimated by the situation estimation AI, advice from historical figures and experts is generated. For example, if the user is experiencing work-related stress, advice from the historical figure Albert Einstein is generated. This advice is customized to the user's situation. The generated advice is then sent to the real-time interpretation AI and made multilingual. For example, if the user can easily understand Japanese, the advice is translated into Japanese. The real-time interpretation AI translates the advice into an appropriate language based on the user's language settings. Finally, the translated advice is provided to the user. Users can receive advice tailored to their situation in a language they can easily understand. For example, if the user can easily understand Japanese, advice will be provided in Japanese. This system allows users to receive appropriate advice for their living situation and problems. In addition, multilingual support means it can also accommodate users who speak different languages. This allows the advice providing system to provide appropriate advice in multiple languages ​​for the user's living situation and problems.

[0060] An advice providing system according to an embodiment includes a collection unit, an estimation unit, a generation unit, a translation unit, and a provision unit. The collection unit collects information about a user's living situation and problems. The collection unit can, for example, collect text data and voice data input by the user. The collection unit can also acquire information about the user's living situation from a sensor or an external database. For example, the collection unit can acquire location information and activity data from the user's smartphone. The estimation unit estimates the user's situation based on the information collected by the collection unit. The estimation unit can, for example, analyze the user's situation using a machine learning algorithm. The estimation unit analyzes the user's input data and collected data to identify the problems and situations the user is facing. For example, if the user inputs "I'm stressed at work," the estimation unit analyzes the information and estimates that the user is experiencing work stress. The generation unit generates advice based on the situation estimated by the estimation unit. For example, the generation unit can generate advice from historical figures or experts using a generation AI. The generation unit generates advice customized to the user's situation. For example, if the user is feeling stressed at work, the generation unit generates advice from the historical figure Einstein. The translation unit translates the advice generated by the generation unit based on the user's language setting. The translation unit can translate the advice into multiple languages, for example, using real-time interpretation AI. The translation unit translates the advice into a language that is easy for the user to understand. For example, if the user can easily understand Japanese, the translation unit translates the advice into Japanese. The provision unit provides the user with the advice translated by the translation unit. The provision unit can, for example, display the advice on the user's device. The provision unit provides the advice in a format that is easy for the user to receive. For example, the provision unit sends a notification to the user's smartphone and displays the advice. As a result, the advice provision system according to the embodiment can provide appropriate advice in multiple languages ​​for the user's living situation or problems.

[0061] The collection unit can analyze the user's emotions and adjust the type of information to be collected based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting information related to stress. For example, if the user is relaxed, the collection unit can also collect information related to relaxation. For example, if the user is excited, the collection unit can also collect information related to excitement. This enables information collection according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion analysis.

[0062] The collection unit can analyze the user's past behavioral history and select the most effective information collection method. The collection unit can, for example, collect information based on keywords that the user frequently searched for in the past. The collection unit can also, for example, analyze the user's past browsing history and collect related information. The collection unit can also, for example, refer to the user's past purchase history to collect related information. This makes it possible to collect information based on the user's past behavioral history. The behavioral history is analyzed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavioral history data into a generation AI and cause the generation AI to select the optimal information collection method.

[0063] When collecting information, the collection unit can select information based on the user's current activity status. For example, if the user is at work, the collection unit prioritizes collecting work-related information. For example, if the user is on vacation, the collection unit can also collect information related to relaxation. For example, if the user is exercising, the collection unit can also collect health-related information. This makes it possible to collect information according to the user's current activity status. The activity status is analyzed using, for example, data from a sensor or an external database. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity status data to a generation AI and have the generation AI select the information.

[0064] The collection unit can analyze the user's emotions and determine the priority of information to be collected based on the analyzed user's emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting information that helps relieve stress. For example, if the user is relaxed, the collection unit can also prioritize collecting information to maintain relaxation. For example, if the user is excited, the collection unit can also prioritize collecting information to alleviate excitement. This makes it possible to determine the priority of information according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, for example, with 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 such examples. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0065] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collecting information related to that area. For example, when the user is traveling, the collection unit can also prioritize collecting information related to the travel destination. For example, when the user is at home, the collection unit can also prioritize collecting information around the user's home. This enables information collection based on the user's geographical location information. The collection of geographical location information is performed using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0066] When collecting information, the collection unit can analyze the user's social media activity and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. For example, the collection unit can analyze the content of posts from accounts the user follows and collect related information. For example, the collection unit can also collect related information based on the activities of groups and communities the user participates in. This makes it possible to collect information based on the user's social media activity. The analysis of social media activity is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related information.

[0067] The estimation unit can analyze the user's emotions and adjust the situation estimation algorithm based on the analyzed user emotions. For example, if the user is feeling stressed, the estimation unit prioritizes estimation of stress-related situations. For example, if the user is relaxed, the estimation unit can also estimate relaxation-related situations. For example, if the user is excited, the estimation unit can also estimate excitement-related situations. This enables situation estimation according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the situation estimation algorithm.

[0068] The estimation unit can adjust the accuracy of the estimation based on the importance of the collected information during estimation. The estimation unit, for example, performs detailed estimation based on information of high importance. The estimation unit can also perform simple estimation based on information of low importance. The estimation unit can also perform estimation with an appropriate level of detail based on information of medium importance. This enables detailed estimation according to the importance of the information. The importance is evaluated using criteria such as frequency, impact, and urgency. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input importance data of the collected information to the generation AI and cause the generation AI to adjust the accuracy of the estimation.

[0069] The estimation unit can apply different estimation methods depending on the category of information during estimation. For example, the estimation unit applies a health-specific estimation algorithm to information related to health. For example, the estimation unit can also apply a work-specific estimation algorithm to information related to work. For example, the estimation unit can also apply a human relationship-specific estimation algorithm to information related to human relationships. This enables estimation according to the category of information. Category classification is performed using criteria such as health, economy, and education. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input information category data to the generation AI and cause the generation AI to apply the estimation method.

[0070] The estimation unit can analyze the user's emotions and adjust the display method of the estimation results based on the analyzed user emotions. For example, if the user is nervous, the estimation unit can provide a simple, highly visible display method. For example, if the user is relaxed, the estimation unit can also provide a display method including detailed information. For example, if the user is in a hurry, the estimation unit can also provide a display method that focuses on the main points. This enables the display of estimation results according to the user's emotions. Emotion analysis 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. Some or all of the above-mentioned processing in the estimation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the estimation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the estimation results.

[0071] During estimation, the estimation unit can determine the order of estimation based on the time of submission of information. For example, the estimation unit prioritizes the most recent information. For example, the estimation unit can also estimate older information with a lower priority. For example, the estimation unit can estimate information of moderate recency with an appropriate priority. This enables estimation according to the time of submission of information. The evaluation of the submission time is performed using criteria such as the submission date, submission time, and submission frequency. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without AI. For example, the estimation unit can input information submission time data to the generation AI and have the generation AI determine the order of estimation.

[0072] The estimation unit can determine the order of estimation based on the relevance of information during estimation. For example, the estimation unit prioritizes estimation of information with high relevance. For example, the estimation unit can also postpone estimation of information with low relevance. For example, the estimation unit can also estimate information with moderate relevance in an appropriate order. This enables estimation according to the relevance of information. The evaluation of relevance is performed using criteria such as a common theme or related keywords. Some or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input information relevance data to a generation AI and have the generation AI determine the order of estimation.

[0073] The generation unit can analyze the user's emotions and adjust the way the advice is presented based on the analyzed user's emotions. For example, if the user is feeling stressed, the generation unit generates advice using gentle language. For example, if the user is relaxed, the generation unit can also generate detailed advice. For example, if the user is excited, the generation unit can also generate advice including encouraging words. This makes it possible to present advice according to the user's emotions. The emotion analysis is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the advice is presented.

[0074] When generating advice, the generation unit can adjust the accuracy of the advice based on the estimated importance of the situation. For example, the generation unit generates detailed advice for a situation of high importance. For example, the generation unit can also generate simple advice for a situation of low importance. For example, the generation unit can also generate advice with an appropriate level of detail for a situation of medium importance. This enables the level of detail of the advice to be adjusted according to the importance of the situation. The importance is evaluated using criteria such as frequency, impact, and urgency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input estimated situation importance data to the generation AI and cause the generation AI to adjust the accuracy of the advice.

[0075] When generating advice, the generation unit can apply different advice generation methods depending on the category of the situation. For example, the generation unit applies a health-specific advice generation algorithm to a health-related situation. For example, the generation unit can also apply a work-specific advice generation algorithm to a work-related situation. For example, the generation unit can also apply a human relationship-specific advice generation algorithm to a human relationship-related situation. This makes it possible to generate advice according to the category of the situation. Categories are classified using criteria such as health, economy, and education. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input situation category data into the generation AI and cause the generation AI to apply the advice generation method.

[0076] The generation unit can analyze the user's emotions and adjust the length of the advice based on the analyzed user's emotions. For example, if the user is in a hurry, the generation unit generates short, to-the-point advice. For example, if the user is relaxed, the generation unit can generate longer advice including detailed explanations. For example, if the user is excited, the generation unit can generate advice with visually stimulating effects. This enables the length of advice to be adjusted according to the user's emotions. The emotion analysis is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the advice.

[0077] When generating advice, the generation unit can determine the order of advice based on the submission time of the estimated situation. For example, the generation unit generates advice with priority for the most recent situation. For example, the generation unit can also generate advice with a lower priority for an older situation. For example, the generation unit can also generate advice with a moderate priority for a situation of moderate recency. This enables prioritization of advice according to the submission time of the situation. The submission time is evaluated using criteria such as the submission date, submission time, and submission frequency. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input submission time data of the estimated situation to the generation AI and cause the generation AI to determine the order of advice.

[0078] When generating advice, the generation unit can determine the order of advice based on the relevance of the estimated situation. For example, the generation unit generates advice preferentially for situations with high relevance. For example, the generation unit can also generate advice later for situations with low relevance. For example, the generation unit can also generate advice in an appropriate order for situations with medium relevance. This makes it possible to order advice according to the relevance of the situation. The evaluation of relevance is performed using criteria such as a common theme or related keywords. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input relevance data of the estimated situation to the generation AI and cause the generation AI to determine the order of advice.

[0079] The translation unit can analyze the user's emotions and adjust the translation expression based on the analyzed user's emotions. For example, if the user is stressed, the translation unit can translate using gentle language. For example, if the user is relaxed, the translation unit can provide a detailed translation. For example, if the user is excited, the translation unit can provide a translation including words of encouragement. This enables the translation to be expressed according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, for example, with 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 such examples. Some or all of the above-mentioned processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0080] The translation unit can adjust the accuracy of the translation based on the importance of the advice during translation. For example, the translation unit provides a detailed translation for advice of high importance. For example, the translation unit can also provide a simple translation for advice of low importance. For example, the translation unit can also provide a translation with an appropriate level of detail for advice of medium importance. This enables the level of detail of the translation to be adjusted according to the importance of the advice. The importance is evaluated using criteria such as frequency, impact, and urgency. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input importance data of the advice to the generation AI and cause the generation AI to adjust the accuracy of the translation.

[0081] The translation unit can apply different translation methods depending on the category of advice during translation. For example, the translation unit can apply a translation algorithm dedicated to health to advice about health. For example, the translation unit can also apply a translation algorithm dedicated to work to advice about work. For example, the translation unit can also apply a translation algorithm dedicated to human relationships to advice about human relationships. This enables translation according to the category of advice. Categories are classified using criteria such as health, economy, education, etc. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input advice category data into a generation AI and cause the generation AI to apply a translation method.

[0082] The translation unit can analyze the user's emotions and adjust the length of the translation based on the analyzed user's emotions. For example, if the user is in a hurry, the translation unit can provide a short, to-the-point translation. For example, if the user is relaxed, the translation unit can provide a longer translation with detailed explanations. For example, if the user is excited, the translation unit can provide a translation with visually stimulating effects. This allows the length of the translation to be adjusted according to the user's emotions. Emotion analysis is achieved using an emotion estimation function, for example, 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 such examples. Some or all of the above-mentioned processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the translation.

[0083] During translation, the translation unit can determine the order of translation based on the submission time of the advice. For example, the translation unit prioritizes translating the most recent advice. For example, the translation unit can also translate older advice with a lower priority. For example, the translation unit can translate advice that is of moderate recency with a moderate priority. This enables prioritization of translation according to the submission time of the advice. The submission time is evaluated using criteria such as the submission date, submission time, and submission frequency. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input data on the submission time of the advice to the generation AI and have the generation AI determine the order of translation.

[0084] During translation, the translation unit can determine the order of translation based on the relevance of the advice. For example, the translation unit prioritizes translation of advice with high relevance. For example, the translation unit can also translate advice with low relevance later. For example, the translation unit can translate advice with medium relevance in an appropriate order. This enables the order of translation to be determined according to the relevance of the advice. The evaluation of relevance is performed using criteria such as a common theme or related keywords. Some or all of the above-mentioned processing in the translation unit may be performed using, for example, AI, or may be performed without using AI. For example, the translation unit can input relevance data of the advice to a generation AI and have the generation AI determine the order of translation.

[0085] The providing unit can analyze the user's emotions and adjust the method of providing advice based on the analyzed user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice using gentle language. For example, if the user is relaxed, the providing unit can also provide detailed advice. For example, if the user is excited, the providing unit can also provide advice including encouraging words. This makes it possible to provide advice according to the user's emotions. The emotion analysis is realized using an emotion estimation function, for example, with 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 such examples. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the method of providing advice.

[0086] When providing advice, the providing unit can select the most effective delivery method by referring to the user's past advice receiving history. The providing unit, for example, provides advice in a format that the user has previously preferred. The providing unit can also provide advice by avoiding a format that the user has previously rejected. The providing unit can, for example, analyze the user's past receiving history and select the optimal delivery method. This makes it possible to provide advice based on the user's past receiving history. The analysis of the receiving history is performed, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's receiving history data into the generation AI and cause the generation AI to select the optimal delivery method.

[0087] When providing advice, the providing unit can adjust the providing means based on the user's current activity status. For example, when the user is at work, the providing unit can provide short and to-the-point advice. For example, when the user is on vacation, the providing unit can also provide detailed advice. For example, when the user is exercising, the providing unit can also provide advice by voice. This makes it possible to provide advice according to the user's current activity status. The activity status is analyzed using data from a sensor or an external database, for example. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into the generating AI and cause the generating AI to adjust the providing means.

[0088] The providing unit can analyze the user's emotions and determine the priority of providing advice based on the analyzed user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing advice that helps relieve stress. For example, if the user is relaxed, the providing unit can also prioritize providing advice to maintain relaxation. For example, if the user is excited, the providing unit can also prioritize providing advice to calm the user. This enables the priority of providing advice according to the user's emotions. The emotion analysis is realized using an emotion estimation function, for example, with 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 such examples. Some or all of the above-mentioned processing in the providing unit can be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of providing advice.

[0089] When providing advice, the providing unit can select the most effective delivery method based on the user's geographical location information. For example, if the user is in a specific area, the providing unit can provide advice related to that area. For example, if the user is traveling, the providing unit can also provide advice related to the travel destination. For example, if the user is at home, the providing unit can also provide information about the area around the user's home. This makes it possible to provide advice based on the user's geographical location information. The geographical location information is collected using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal delivery method.

[0090] When providing advice, the providing unit can analyze the user's social media activity and suggest a means of providing the advice. The providing unit can, for example, provide relevant advice based on information shared by the user on social media. The providing unit can also, for example, analyze the content posted by accounts the user follows and provide relevant advice. The providing unit can also, for example, provide relevant advice based on the activity content of groups or communities the user participates in. This makes it possible to provide advice based on the user's social media activity. The analysis of social media activity is performed, for example, using a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest a means of providing the advice. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, estimation unit, generation unit, translation unit, and provision unit, 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 information about the user's living situation and problems using the camera 42 and microphone 38B of the smart device 14. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to estimate the user's situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the estimated situation. The translation unit is realized, for example, by the control unit 46A of the smart device 14 and translates the generated advice based on the user's language setting. The provision unit provides the translated advice to the user using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, estimation unit, generation unit, translation unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about the user's living situation and problems using the camera 42 and microphone 238 of the smart glasses 214. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to estimate the user's situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the estimated situation. The translation unit is realized, for example, by the control unit 46A of the smart glasses 214 and translates the generated advice based on the user's language setting. The provision unit provides the translated advice to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, estimation unit, generation unit, translation unit, and provision 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 information about the user's living situation and problems using the camera 42 and microphone 238 of the headset-type terminal 314. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to estimate the user's situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the estimated situation. The translation unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and translates the generated advice based on the user's language setting. The provision unit provides the translated advice to the user using, for example, the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, estimation unit, generation unit, translation unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information on the user's living situation and problems using the camera 42 and microphone 238 of the robot 414. The estimation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information to estimate the user's situation. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on the estimated situation. The translation unit is realized, for example, by the control unit 46A of the robot 414 and translates the generated advice based on the user's language setting. The provision unit provides the translated advice to the user using, for example, the speaker 240 of the robot 414.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The providing unit can analyze the user's emotions and adjust the timing of providing advice based on the analyzed user's emotions. For example, if the user is feeling stressed, advice can be provided at a time when the user is able to relax. If the user is relaxed, advice can be provided at an appropriate time to help the user maintain that state. If the user is excited, advice can be provided at an appropriate time to help the user calm down. This makes it possible to provide advice at the optimal time according to the user's emotions. Emotion analysis is achieved 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 such examples. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of providing advice.

[0093] The providing unit can refer to the user's past advice receiving history and select the most effective delivery format. For example, it can provide advice in a format that the user has previously preferred. It can also provide advice by avoiding formats that the user has previously rejected. It can also analyze the user's past receiving history and select the optimal delivery format. This makes it possible to provide advice based on the user's past receiving history. The analysis of the receiving history is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's receiving history data into the generating AI and have the generating AI select the optimal delivery format.

[0094] The providing unit can adjust the means for providing advice based on the user's current activity status. For example, if the user is at work, short, to-the-point advice can be provided. If the user is on vacation, detailed advice can be provided. If the user is exercising, advice can be provided via voice. This makes it possible to provide advice according to the user's current activity status. The activity status is analyzed using, for example, data from a sensor or an external database. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into the generating AI and have the generating AI adjust the means for providing advice.

[0095] The providing unit can analyze the user's emotions and determine the priority of providing advice based on the analyzed user's emotions. For example, if the user is feeling stressed, advice that helps relieve stress can be provided preferentially. If the user is relaxed, advice to maintain relaxation can be provided preferentially. If the user is excited, advice to calm the excitement can be provided preferentially. This enables the priority of providing advice according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of providing advice.

[0096] The providing unit can select the most effective delivery method based on the user's geographical location information. For example, if the user is in a specific area, advice related to that area can be provided. If the user is traveling, advice related to the travel destination can be provided. If the user is at home, information about the area around the user's home can be provided. This makes it possible to provide advice based on the user's geographical location information. The geographical location information is collected using, for example, GPS data or Wi-Fi location information. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information to the generating AI and cause the generating AI to select the optimal delivery method.

[0097] The providing unit can analyze the user's social media activity and provide relevant advice. For example, it can provide relevant advice based on information shared by the user on social media. It can also analyze the content posted by accounts the user follows and provide relevant advice. It can also provide relevant advice based on the activities of groups and communities the user participates in. This makes it possible to provide advice based on the user's social media activity. The analysis of social media activity is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into a generating AI and have the generating AI suggest a means of providing advice.

[0098] The providing unit can analyze the user's emotions and adjust the way in which advice is provided based on the analyzed user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in gentle language. If the user is relaxed, the providing unit can provide detailed advice. If the user is excited, the providing unit can provide advice including encouraging words. This makes it possible to provide advice according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way in which advice is provided.

[0099] When providing advice, the providing unit can select the most effective method of providing advice by referring to the user's past advice receiving history. For example, the advice can be provided in a format that the user has previously preferred. The advice can also be provided by avoiding a format that the user has previously rejected. The user's past receiving history can also be analyzed to select the optimal method of providing advice. This makes it possible to provide advice based on the user's past receiving history. The analysis of the receiving history is performed using, for example, a machine learning algorithm. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's receiving history data into the generating AI and have the generating AI select the optimal method of providing advice.

[0100] When providing advice, the providing unit can adjust the means of providing advice based on the user's current activity status. For example, if the user is at work, short, to-the-point advice can be provided. If the user is on vacation, detailed advice can be provided. If the user is exercising, advice can be provided by voice. This makes it possible to provide advice according to the user's current activity status. The activity status is analyzed using, for example, data from a sensor or an external database. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's activity status data into the generating AI and have the generating AI adjust the means of providing advice.

[0101] The providing unit can analyze the user's emotions and determine the priority of providing advice based on the analyzed user's emotions. For example, if the user is feeling stressed, advice that helps relieve stress can be provided preferentially. If the user is relaxed, advice to maintain relaxation can be provided preferentially. If the user is excited, advice to calm the excitement can be provided preferentially. This enables the priority of providing advice according to the user's emotions. Emotion analysis is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to determine the priority of providing advice.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects information about the user's living situation and problems. The collection unit can collect text data and voice data entered by the user. The collection unit can also obtain information about the user's living situation from sensors or external databases. For example, the collection unit can obtain location information and activity data from the user's smartphone. Step 2: The estimation unit estimates the user's situation based on the information collected by the collection unit. The estimation unit uses a machine learning algorithm to analyze the user's situation and identify the problems and situations the user is facing. For example, if the user inputs "I'm very stressed at work," the estimation unit analyzes the information and estimates that the user is experiencing work stress. Step 3: The generator generates advice based on the situation estimated by the estimation unit. The generator uses AI to generate advice from historical figures and experts, providing advice customized to the user's situation. For example, if the user is feeling stressed at work, the generator generates advice from Einstein. Step 4: The translation unit translates the advice generated by the generation unit based on the user's language settings. The translation unit uses real-time interpretation AI to translate the advice into multiple languages ​​and into a language that the user can easily understand. For example, if the user can easily understand Japanese, the advice is translated into Japanese. Step 5: The providing unit provides the advice translated by the translation unit to the user. The providing unit displays the advice on the user's device and provides the advice in a format that is easy for the user to receive. For example, the providing unit sends a notification to the user's smartphone and displays the advice.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 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 unit that collects information about the user's living situation and problems, an estimation unit that estimates a user's situation based on the information collected by the collection unit; a generator that generates advice based on the situation estimated by the estimation unit; a translation unit that translates the advice generated by the generation unit; a providing unit that provides the user with the advice translated by the translation unit. A system characterized by:

2. The collecting unit Analyzing user sentiment and adjusting the type of information collected based on the analyzed user sentiment 2. The system of claim 1.

3. The collecting unit Analyze users' past behavioral history and select the most effective method of collecting information 2. The system of claim 1.

4. The collecting unit When collecting information, filter it based on the user's current activity.

2. The system of claim 1.

5. The collecting unit Analyze user emotions and prioritize the information to be collected based on the analyzed user emotions.

2. The system of claim 1.

6. The collecting unit When collecting information, prioritize collection of highly relevant information based on the user's geographic location information.

2. The system of claim 1.

7. The collecting unit When collecting information, we analyze your social media activity and collect related information.

2. The system of claim 1.

8. The estimation unit Analyzes user emotions and adjusts situation estimation algorithms based on the analyzed user emotions.

2. The system of claim 1.

9. The estimation unit During estimation, adjust the accuracy of the estimation based on the importance of the collected information 2. The system of claim 1.

10. The estimation unit When estimating, apply different estimation methods depending on the category of information 2. The system of claim 1.

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