System

Abstract

An object of a system according to an embodiment is to provide appropriate childcare advice only by a user inputting a problem or a situation related to childcare.SOLUTION: A system includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a problem or a situation related to child-raising from a user. The generation unit analyzes the information received by the reception unit and generates specific child-care advice. The providing unit provides the advice generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to provide specific child-rearing advice quickly and appropriately.

[0005] The system according to the embodiment aims to provide appropriate child-rearing advice to a user simply by inputting a problem or situation related to child-rearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives input from a user about problems or situations related to child rearing. The generation unit analyzes the information received by the reception unit and generates specific child rearing advice. The provision unit provides the advice generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate child-rearing advice simply by the user inputting a problem or situation related to child-rearing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A child-rearing advice providing system according to an embodiment of the present invention is a system that provides child-rearing advice appropriate to a user's child-rearing situation simply by describing the user's child-rearing situation. In the child-rearing advice providing system, a user inputs a child-rearing problem or situation in natural language, and an LLM analyzes the input information to generate appropriate child-rearing advice and provide it to the user. For example, the child-rearing advice providing system inputs a specific problem such as "my child cries at night." Next, the child-rearing advice providing system analyzes the input information using the LLM to generate appropriate child-rearing advice. For example, for the problem "my child cries at night," the LLM provides specific advice on causes of and countermeasures for the night-rearing. Next, the child-rearing advice providing system provides the generated advice to the user. For example, specific advice such as establishing a bedtime routine or reviewing the room environment as a countermeasure for the night-rearing may be provided. This allows the child-rearing advice providing system to quickly and accurately solve the user's child-rearing problems. Furthermore, even users with limited knowledge about child-rearing can easily obtain appropriate advice, thereby reducing the burden of child-rearing. This allows the child-rearing advice providing system to quickly and accurately solve the user's child-rearing problems. Furthermore, even users who lack knowledge about child-rearing can easily obtain appropriate advice, thereby reducing the burden of child-rearing.

[0029] A child-rearing advice providing system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input from a user about problems and situations related to child-rearing. The information input by the user includes, but is not limited to, specific problems and situations such as diet, sleep, and discipline. The receiving unit can analyze the information input by the user using, for example, natural language processing technology. The receiving unit can also anonymize the information input by the user to prevent identification of individuals. For example, the receiving unit can achieve anonymization by deleting personal information and encrypting the data. The generating unit uses LLM to analyze the information received by the receiving unit and generate specific child-rearing advice. The generating unit learns from, for example, specialized books and papers on child-rearing and information from recognized child-rearing websites. The generating unit can also use data supervised by experts. For example, the generating unit generates advice based on data supervised by experts such as doctors and child-rearing counselors. The generating unit can also estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit provides advice in gentle language. The provision unit provides the advice generated by the generation unit to the user. The provision unit can provide the advice in the form of, for example, text, audio, or video. The provision unit can also estimate the user's emotions and adjust the way the advice is provided based on the estimated user's emotions. For example, if the user is in a hurry, the provision unit provides concise and to-the-point advice. As a result, the child-rearing advice providing system according to the embodiment can quickly and accurately solve the user's child-rearing problems.

[0030] The generation unit can learn based on information from specialized books and papers on childcare and authorized childcare websites. Information from specialized books and papers on childcare and authorized childcare websites includes, for example, the names of specific authors, institutions, and websites, but is not limited to these examples. The generation unit learns based on, for example, specialized books on childcare. For example, the generation unit analyzes the contents of specialized books on childcare and learns knowledge about childcare. The generation unit can also learn based on papers on childcare. For example, the generation unit generates advice based on the latest research results on childcare. The generation unit can also learn based on information from authorized childcare websites. For example, the generation unit collects information from reliable childcare websites and learns knowledge about childcare. As a result, the generation unit learns based on reliable information, thereby improving the accuracy of advice.

[0031] The generation unit can use data supervised by an expert. Data supervised by an expert includes, but is not limited to, the qualifications and supervision process of experts such as doctors and childcare counselors. The generation unit uses, for example, data supervised by a doctor. For example, the generation unit generates advice based on data related to childcare supervised by a doctor. The generation unit can also use data supervised by a childcare counselor. For example, the generation unit generates advice based on data supervised by a childcare counselor. The generation unit can also improve the reliability of the advice by using data supervised by an expert. As a result, the generation unit's use of data supervised by an expert improves the reliability of the advice.

[0032] The reception unit can anonymize the information input by the user and process it so that individuals cannot be identified. Specific methods and standards for anonymization include, but are not limited to, deleting personal information and encrypting data, for example. The reception unit achieves anonymization by deleting personal information, for example, the reception unit deletes personal information such as the user's name and address, and performs anonymization. The reception unit can also achieve anonymization by encrypting data. For example, the reception unit encrypts the information input by the user and processes it so that individuals cannot be identified. In this way, the reception unit anonymizes the information input by the user and processes it so that individuals cannot be identified, thereby protecting the user's privacy.

[0033] The generation unit may provide specific advice for the problem of "my child cries at night" such as improving a bedtime routine, reviewing the room environment, or consulting a specialist. The content and format of the specific advice may include, but are not limited to, guidelines for behavior, suggestions for environmental settings, and how to consult a specialist. For example, the generation unit may advise improving a bedtime routine. For example, the generation unit may suggest improving a bedtime routine so that the child does not cry at night. The generation unit may also advise improving the room environment. For example, the generation unit may suggest reviewing the room environment, such as the room temperature and lighting. The generation unit may also advise consulting a specialist. For example, if the child continues to cry at night, the generation unit may suggest consulting a specialist. In this way, the generation unit may provide specific childcare advice, thereby effectively resolving the user's childcare problem.

[0034] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit selects the optimal reception method for a specific time period from the user's past input history. The reception unit can also automatically generate related questions by referring to the content the user has input in the past. For example, the reception unit generates related questions based on the content the user has input in the past. This allows the reception unit to select the optimal reception method based on the user's past input history, thereby enabling efficient information collection.

[0035] When receiving input information, the reception unit can perform filtering based on the user's current child-rearing situation and areas of interest. For example, the reception unit preferentially receives only information related to child-rearing issues the user is currently facing. For example, the reception unit filters related questions and information based on the user's areas of interest. The reception unit can also select information for providing appropriate advice according to the user's child-rearing situation. For example, the reception unit selects appropriate information based on the user's child-rearing situation. This allows the reception unit to receive appropriate information based on the user's current child-rearing situation and areas of interest.

[0036] When receiving input information, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit analyzes the input information using voice recognition technology. For example, if the user selects text input, the reception unit analyzes the input information using natural language processing technology. Furthermore, if the user selects image input, the reception unit can also analyze the input information using image recognition technology. For example, if the user selects image input, the reception unit analyzes the input information using image recognition technology. This allows the reception unit to select the optimal reception means depending on the user's input method, thereby enabling efficient information collection.

[0037] When receiving input information, the reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit prioritizes receiving childcare information related to that area. For example, if the user is traveling, the reception unit provides appropriate childcare information based on the user's current location. The reception unit can also prioritize receiving information related to childcare issues specific to the area based on the user's geographical location information. For example, the reception unit prioritizes receiving information related to childcare issues specific to the area based on the user's geographical location information. In this way, the reception unit can respond to childcare issues specific to the area by preferentially receiving highly relevant information in consideration of the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit receives related information based on, for example, posts about childcare shared by the user on social media. For example, the reception unit analyzes the user's social media activity and preferentially receives childcare information that is of interest to the user. The reception unit can also receive related childcare information by referring to the activity of the user's friends on social media. For example, the reception unit receives related childcare information by referring to the activity of the user's friends on social media. In this way, the reception unit can efficiently collect related information by analyzing the user's social media activity.

[0039] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input information. The reception unit, for example, proposes the optimal reception method based on feedback provided by the user in the past. For example, the reception unit preferentially selects a specific reception method based on the user's past feedback. The reception unit can also continuously improve the reception method by reflecting the user's feedback. For example, the reception unit continuously improves the reception method by reflecting the user's feedback. In this way, the reception unit can provide the optimal reception method by reflecting the user's past feedback.

[0040] The generation unit can adjust the level of detail of the advice based on the importance of the child-rearing problem when generating advice. For example, the generation unit provides detailed advice for a serious child-rearing problem. For example, the generation unit provides concise advice for a minor child-rearing problem. The generation unit can also provide detailed advice for a problem in which the user is particularly interested. For example, the generation unit provides detailed advice for a problem in which the user is particularly interested. In this way, the generation unit can adjust the level of detail of the advice based on the importance of the child-rearing problem, thereby providing appropriate advice.

[0041] When generating advice, the generation unit can apply different generation algorithms depending on the category of the childcare problem. For example, the generation unit applies a sleep-specialized algorithm to a problem related to sleep. For example, the generation unit applies a nutrition-specialized algorithm to a problem related to diet. The generation unit can also apply a behavior-specialized algorithm to a problem related to behavior. For example, the generation unit applies a behavior-specialized algorithm to a problem related to behavior. In this way, the generation unit can apply an appropriate generation algorithm depending on the category of the childcare problem, thereby providing more accurate advice.

[0042] When generating advice, the generation unit can improve the accuracy of the advice by referring to the results of past advice given to the user. The generation unit provides optimal advice based on, for example, the results of advice given to the user in the past. For example, the generation unit selects effective advice from the results of past advice given to the user. The generation unit can also reflect user feedback to continuously improve the accuracy of the advice. For example, the generation unit reflects user feedback to continuously improve the accuracy of the advice. In this way, the generation unit can improve the accuracy of the advice by referring to the results of past advice given to the user.

[0043] When generating advice, the generation unit can determine the priority of advice based on the time when a childcare problem occurred. For example, the generation unit provides advice preferentially for problems with high urgency. For example, the generation unit provides advice in stages for long-term problems. The generation unit can also provide advice at an appropriate time for problems related to seasons or time periods. For example, the generation unit provides advice at an appropriate time for problems related to seasons or time periods. In this way, by the generation unit determining the priority of advice based on the time when a childcare problem occurred, advice can be provided at an appropriate time.

[0044] When generating advice, the generation unit can adjust the order of advice based on the relevance of the child-rearing problem. For example, the generation unit provides direct solutions first, followed by supplementary advice. For example, when multiple problems are related, the generation unit provides advice in order of the most important problem. The generation unit can also adjust the order of advice based on the user's level of interest. For example, the generation unit adjusts the order of advice based on the user's level of interest. In this way, the generation unit can provide effective advice by adjusting the order of advice based on the relevance of the child-rearing problem.

[0045] When generating advice, the generation unit may adjust the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, the generation unit provides detailed advice using technical terms. For example, if the user is a beginner, the generation unit provides easy-to-understand advice in simple terms. The generation unit may also select appropriate terms to provide advice according to the user's level of expertise. For example, the generation unit selects appropriate terms to provide advice according to the user's level of expertise. In this way, the generation unit can provide easy-to-understand advice by selecting appropriate terms according to the user's level of expertise.

[0046] When providing advice, the providing unit can select the optimal providing method by referring to the user's past feedback. The providing unit suggests the optimal providing method, for example, based on feedback provided by the user in the past. For example, the providing unit preferentially selects a specific providing method from the user's past feedback. The providing unit can also reflect the user's feedback and continuously improve the providing method. For example, the providing unit reflects the user's feedback and continuously improves the providing method. In this way, the providing unit can select the optimal providing method by referring to the user's past feedback.

[0047] When providing advice, the providing unit can customize the content of the advice based on the user's current child-rearing situation. For example, the providing unit prioritizes providing advice related to child-rearing problems the user is currently facing. For example, the providing unit customizes appropriate advice according to the user's child-rearing situation. The providing unit can also provide optimal advice taking into account the user's current situation. For example, the providing unit provides optimal advice taking into account the user's current situation. This allows the providing unit to provide appropriate advice based on the user's current child-rearing situation.

[0048] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. For example, when a user provides feedback on the advice provided, the providing unit improves the advice providing method based on the feedback. For example, the providing unit analyzes the user's feedback and continuously reviews the optimal advice providing method. The providing unit can also customize the content of the advice and the method of providing the advice by reflecting user feedback. For example, the providing unit customizes the content of the advice and the method of providing the advice by reflecting user feedback. In this way, the providing unit can continuously improve the advice providing method by reflecting user feedback.

[0049] When providing advice, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the providing unit can prioritize providing childcare advice related to that area. For example, if the user is traveling, the providing unit can provide appropriate childcare advice based on the user's current location. The providing unit can also prioritize providing advice on childcare issues specific to the area based on the user's geographical location information. For example, the providing unit prioritizes providing advice on childcare issues specific to the area based on the user's geographical location information. In this way, the providing unit can provide advice that addresses childcare issues specific to the area by taking into consideration the user's geographical location information.

[0050] When providing advice, the providing unit can analyze the user's social media activity and customize the content of the advice to be provided. For example, the providing unit provides relevant advice based on child-rearing posts shared by the user on social media. For example, the providing unit analyzes the user's social media activity and prioritizes providing child-rearing advice that is of interest to the user. The providing unit can also provide relevant child-rearing advice by taking into account the activity of the user's friends on social media. For example, the providing unit provides relevant child-rearing advice by taking into account the activity of the user's friends on social media. In this way, the providing unit can provide relevant advice by analyzing the user's social media activity.

[0051] When providing advice, the providing unit can customize the provision method by reflecting the user's past feedback. The providing unit, for example, suggests the optimal provision method based on feedback provided by the user in the past. For example, the providing unit preferentially selects a specific provision method from the user's past feedback. The providing unit can also reflect the user's feedback and continuously improve the provision method. For example, the providing unit reflects the user's feedback and continuously improves the provision method. In this way, the providing unit can provide the optimal provision method by reflecting the user's past feedback.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The child-rearing advice providing system may further include a behavior analysis unit that collects the user's past behavioral data related to child-rearing and analyzes behavioral patterns. The behavior analysis unit records, for example, what kind of child-rearing advice the user received in the past and what actions the user took based on that advice. For example, if the user redesigns the room environment to address nighttime crying, the results are recorded and reflected in the next advice. The behavior analysis unit can also analyze the user's behavioral patterns and generate data for providing optimal advice. For example, if the user tends to report child-rearing problems during a specific time period, advice tailored to that time period can be provided. This allows the child-rearing advice providing system to utilize the user's past behavioral data to provide more effective advice.

[0054] The child-rearing advice providing system may further include a community analysis unit that collects community data related to child-rearing among users and reflects the opinions of the community. The community analysis unit, for example, analyzes posts from child-rearing forums and social media groups in which the user participates to extract common child-rearing problems and solutions. For example, if there are many posts about nighttime crying, the community analysis unit may reflect those solutions in the advice. The community analysis unit may also refer to advice the user has received from other parents. For example, it may suggest to the user parenting methods that other parents have found successful. This allows the child-rearing advice providing system to utilize the knowledge of the community and provide advice from a wider variety of perspectives.

[0055] The child-rearing advice providing system may further include a health analysis unit that collects health data related to the user's child-rearing and provides advice that takes into account the user's health condition. The health analysis unit, for example, collects health data of the user's child (body temperature, sleep time, dietary content, etc.) and provides advice based on the user's health condition. For example, if the child has a cold, it may suggest appropriate care methods or consulting a doctor. The health analysis unit may also take into account the user's health condition. For example, if the user is tired, it may suggest a reasonable child-rearing method. This allows the child-rearing advice providing system to provide advice that takes into account the health conditions of the user and the child.

[0056] The child-rearing advice providing system may further include an education analysis unit that collects the user's education data related to child-rearing and provides advice based on the user's education policy. The education analysis unit, for example, records the user's education policy and provides advice based on that policy. For example, if the user has an education policy that emphasizes independence for children, the education analysis unit provides advice in line with that policy. The education analysis unit may also refer to educational advice the user has received in the past. For example, if advice the user has received in the past was effective, the education analysis unit may suggest that advice again. This allows the child-rearing advice providing system to provide advice based on the user's education policy.

[0057] The child-rearing advice providing system can further include a cultural analysis unit that provides advice that takes into account the user's cultural background regarding child-rearing. The cultural analysis unit, for example, takes into account the user's cultural background (nationality, religion, traditions, etc.) and provides advice based on that background. For example, it may suggest child-rearing methods that are important in a particular culture. Furthermore, if the user is interested in other cultures, the cultural analysis unit can also introduce child-rearing methods based on those cultures. For example, if the user is interested in child-rearing methods in other countries, it may suggest those methods. This allows the child-rearing advice providing system to provide advice that takes into account the user's cultural background.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reception unit accepts input from the user about problems and situations related to childcare. The information entered by the user includes specific problems and situations such as eating, sleeping, and discipline. The reception unit uses natural language processing technology to analyze the information entered by the user, and then deletes personal information and encrypts the data to make it anonymous. Step 2: The generator uses LLM to analyze the information received by the receiver and generate specific childcare advice. The generator learns from specialized books and papers on childcare, as well as information from authorized childcare websites, and uses data supervised by experts. It also estimates the user's emotions and adjusts the way the advice is presented based on the estimated emotions. Step 3: The providing unit provides the advice generated by the generating unit to the user. The providing unit provides the advice in the form of text, audio, video, etc., and adjusts the way of providing the advice by estimating the user's feelings. For example, if the user is in a hurry, the providing unit provides concise and to-the-point advice.

[0060] (Example 2) A child-rearing advice providing system according to an embodiment of the present invention is a system that provides child-rearing advice appropriate to a user's child-rearing situation simply by describing the user's child-rearing situation. In the child-rearing advice providing system, a user inputs a child-rearing problem or situation in natural language, and an LLM analyzes the input information to generate appropriate child-rearing advice and provide it to the user. For example, the child-rearing advice providing system inputs a specific problem such as "my child cries at night." Next, the child-rearing advice providing system analyzes the input information using the LLM to generate appropriate child-rearing advice. For example, for the problem "my child cries at night," the LLM provides specific advice on causes of and countermeasures for the night-rearing. Next, the child-rearing advice providing system provides the generated advice to the user. For example, specific advice such as establishing a bedtime routine or reviewing the room environment as a countermeasure for the night-rearing may be provided. This allows the child-rearing advice providing system to quickly and accurately solve the user's child-rearing problems. Furthermore, even users with limited knowledge about child-rearing can easily obtain appropriate advice, thereby reducing the burden of child-rearing. This allows the child-rearing advice providing system to quickly and accurately solve the user's child-rearing problems. Furthermore, even users who lack knowledge about child-rearing can easily obtain appropriate advice, thereby reducing the burden of child-rearing.

[0061] A child-rearing advice providing system according to an embodiment includes a receiving unit, a generating unit, and a providing unit. The receiving unit receives input from a user about problems and situations related to child-rearing. The information input by the user includes, but is not limited to, specific problems and situations such as diet, sleep, and discipline. The receiving unit can analyze the information input by the user using, for example, natural language processing technology. The receiving unit can also anonymize the information input by the user to prevent identification of individuals. For example, the receiving unit can achieve anonymization by deleting personal information and encrypting the data. The generating unit uses LLM to analyze the information received by the receiving unit and generate specific child-rearing advice. The generating unit learns from, for example, specialized books and papers on child-rearing and information from recognized child-rearing websites. The generating unit can also use data supervised by experts. For example, the generating unit generates advice based on data supervised by experts such as doctors and child-rearing counselors. The generating unit can also estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit provides advice in gentle language. The provision unit provides the advice generated by the generation unit to the user. The provision unit can provide the advice in the form of, for example, text, audio, or video. The provision unit can also estimate the user's emotions and adjust the way the advice is provided based on the estimated user's emotions. For example, if the user is in a hurry, the provision unit provides concise and to-the-point advice. As a result, the child-rearing advice providing system according to the embodiment can quickly and accurately solve the user's child-rearing problems.

[0062] The generation unit can learn based on information from specialized books and papers on childcare and authorized childcare websites. Information from specialized books and papers on childcare and authorized childcare websites includes, for example, the names of specific authors, institutions, and websites, but is not limited to these examples. The generation unit learns based on, for example, specialized books on childcare. For example, the generation unit analyzes the contents of specialized books on childcare and learns knowledge about childcare. The generation unit can also learn based on papers on childcare. For example, the generation unit generates advice based on the latest research results on childcare. The generation unit can also learn based on information from authorized childcare websites. For example, the generation unit collects information from reliable childcare websites and learns knowledge about childcare. As a result, the generation unit learns based on reliable information, thereby improving the accuracy of advice.

[0063] The generation unit can use data supervised by an expert. Data supervised by an expert includes, but is not limited to, the qualifications and supervision process of experts such as doctors and childcare counselors. The generation unit uses, for example, data supervised by a doctor. For example, the generation unit generates advice based on data related to childcare supervised by a doctor. The generation unit can also use data supervised by a childcare counselor. For example, the generation unit generates advice based on data supervised by a childcare counselor. The generation unit can also improve the reliability of the advice by using data supervised by an expert. As a result, the generation unit's use of data supervised by an expert improves the reliability of the advice.

[0064] The reception unit can anonymize the information input by the user and process it so that individuals cannot be identified. Specific methods and standards for anonymization include, but are not limited to, deleting personal information and encrypting data, for example. The reception unit achieves anonymization by deleting personal information, for example, the reception unit deletes personal information such as the user's name and address, and performs anonymization. The reception unit can also achieve anonymization by encrypting data. For example, the reception unit encrypts the information input by the user and processes it so that individuals cannot be identified. In this way, the reception unit anonymizes the information input by the user and processes it so that individuals cannot be identified, thereby protecting the user's privacy.

[0065] The generation unit may provide specific advice for the problem of "my child cries at night" such as improving a bedtime routine, reviewing the room environment, or consulting a specialist. The content and format of the specific advice may include, but are not limited to, guidelines for behavior, suggestions for environmental settings, and how to consult a specialist. For example, the generation unit may advise improving a bedtime routine. For example, the generation unit may suggest improving a bedtime routine so that the child does not cry at night. The generation unit may also advise improving the room environment. For example, the generation unit may suggest reviewing the room environment, such as the room temperature and lighting. The generation unit may also advise consulting a specialist. For example, if the child continues to cry at night, the generation unit may suggest consulting a specialist. In this way, the generation unit may provide specific childcare advice, thereby effectively resolving the user's childcare problem.

[0066] The reception unit can estimate the user's emotions and adjust the timing of receiving input information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit delays the timing of prompting the user to input, providing a relaxing environment. For example, if the user is relaxed, the reception unit advances the timing of prompting the user to input, allowing for smooth information collection. Furthermore, if the user is in a hurry, the reception unit can immediately accept input and quickly start analysis. For example, if the user is in a hurry, the reception unit quickly accepts input and starts analysis. This allows the reception unit to accept input information at an appropriate timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0067] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit selects the optimal reception method for a specific time period from the user's past input history. The reception unit can also automatically generate related questions by referring to the content the user has input in the past. For example, the reception unit generates related questions based on the content the user has input in the past. This allows the reception unit to select the optimal reception method based on the user's past input history, thereby enabling efficient information collection.

[0068] When receiving input information, the reception unit can perform filtering based on the user's current child-rearing situation and areas of interest. For example, the reception unit preferentially receives only information related to child-rearing issues the user is currently facing. For example, the reception unit filters related questions and information based on the user's areas of interest. The reception unit can also select information for providing appropriate advice according to the user's child-rearing situation. For example, the reception unit selects appropriate information based on the user's child-rearing situation. This allows the reception unit to receive appropriate information based on the user's current child-rearing situation and areas of interest.

[0069] When receiving input information, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user selects voice input, the reception unit analyzes the input information using voice recognition technology. For example, if the user selects text input, the reception unit analyzes the input information using natural language processing technology. Furthermore, if the user selects image input, the reception unit can also analyze the input information using image recognition technology. For example, if the user selects image input, the reception unit analyzes the input information using image recognition technology. This allows the reception unit to select the optimal reception means depending on the user's input method, thereby enabling efficient information collection.

[0070] The reception unit can estimate the user's emotions and determine the priority of information to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit prioritizes receiving information of high importance. For example, when the user is relaxed, the reception unit prioritizes receiving detailed information. The reception unit can also prioritize receiving information requiring a prompt response when the user is in a hurry. For example, when the user is in a hurry, the reception unit prioritizes receiving information requiring a prompt response. In this way, the reception unit prioritizes information according to the user's emotions, thereby allowing important information to be received preferentially. Emotion estimation is realized using an emotion estimation function using, for example, 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.

[0071] When receiving input information, the reception unit can prioritize receiving highly relevant information in consideration of the user's geographical location information. For example, if the user lives in a specific area, the reception unit prioritizes receiving childcare information related to that area. For example, if the user is traveling, the reception unit provides appropriate childcare information based on the user's current location. The reception unit can also prioritize receiving information related to childcare issues specific to the area based on the user's geographical location information. For example, the reception unit prioritizes receiving information related to childcare issues specific to the area based on the user's geographical location information. In this way, the reception unit can respond to childcare issues specific to the area by preferentially receiving highly relevant information in consideration of the user's geographical location information.

[0072] The reception unit can analyze the user's social media activity and receive related information when receiving input information. The reception unit receives related information based on, for example, posts about childcare shared by the user on social media. For example, the reception unit analyzes the user's social media activity and preferentially receives childcare information that is of interest to the user. The reception unit can also receive related childcare information by referring to the activity of the user's friends on social media. For example, the reception unit receives related childcare information by referring to the activity of the user's friends on social media. In this way, the reception unit can efficiently collect related information by analyzing the user's social media activity.

[0073] The reception unit can customize the reception method by reflecting the user's past feedback when receiving input information. The reception unit, for example, proposes the optimal reception method based on feedback provided by the user in the past. For example, the reception unit preferentially selects a specific reception method based on the user's past feedback. The reception unit can also continuously improve the reception method by reflecting the user's feedback. For example, the reception unit continuously improves the reception method by reflecting the user's feedback. In this way, the reception unit can provide the optimal reception method by reflecting the user's past feedback.

[0074] The generation unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit provides advice in gentle language. For example, if the user is relaxed, the generation unit provides advice including detailed explanations. The generation unit can also provide concise and to-the-point advice if the user is in a hurry. For example, if the user is in a hurry, the generation unit provides concise and to-the-point advice. In this way, the generation unit can adjust the way the advice is expressed according to the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0075] The generation unit can adjust the level of detail of the advice based on the importance of the child-rearing problem when generating advice. For example, the generation unit provides detailed advice for a serious child-rearing problem. For example, the generation unit provides concise advice for a minor child-rearing problem. The generation unit can also provide detailed advice for a problem in which the user is particularly interested. For example, the generation unit provides detailed advice for a problem in which the user is particularly interested. In this way, the generation unit can adjust the level of detail of the advice based on the importance of the child-rearing problem, thereby providing appropriate advice.

[0076] When generating advice, the generation unit can apply different generation algorithms depending on the category of the childcare problem. For example, the generation unit applies a sleep-specialized algorithm to a problem related to sleep. For example, the generation unit applies a nutrition-specialized algorithm to a problem related to diet. The generation unit can also apply a behavior-specialized algorithm to a problem related to behavior. For example, the generation unit applies a behavior-specialized algorithm to a problem related to behavior. In this way, the generation unit can apply an appropriate generation algorithm depending on the category of the childcare problem, thereby providing more accurate advice.

[0077] When generating advice, the generation unit can improve the accuracy of the advice by referring to the results of past advice given to the user. The generation unit provides optimal advice based on, for example, the results of advice given to the user in the past. For example, the generation unit selects effective advice from the results of past advice given to the user. The generation unit can also reflect user feedback to continuously improve the accuracy of the advice. For example, the generation unit reflects user feedback to continuously improve the accuracy of the advice. In this way, the generation unit can improve the accuracy of the advice by referring to the results of past advice given to the user.

[0078] The generation unit can estimate the user's emotions and adjust the length of advice based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit provides short, to-the-point advice. For example, if the user is relaxed, the generation unit provides longer advice with detailed explanations. The generation unit can also provide concise, quick advice if the user is in a hurry. For example, if the user is in a hurry, the generation unit provides concise, quick advice. In this way, the generation unit can adjust the length of advice according to the user's emotions, thereby providing more appropriate advice. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] When generating advice, the generation unit can determine the priority of advice based on the time when a childcare problem occurred. For example, the generation unit provides advice preferentially for problems with high urgency. For example, the generation unit provides advice in stages for long-term problems. The generation unit can also provide advice at an appropriate time for problems related to seasons or time periods. For example, the generation unit provides advice at an appropriate time for problems related to seasons or time periods. In this way, by the generation unit determining the priority of advice based on the time when a childcare problem occurred, advice can be provided at an appropriate time.

[0080] When generating advice, the generation unit can adjust the order of advice based on the relevance of the child-rearing problem. For example, the generation unit provides direct solutions first, followed by supplementary advice. For example, when multiple problems are related, the generation unit provides advice in order of the most important problem. The generation unit can also adjust the order of advice based on the user's level of interest. For example, the generation unit adjusts the order of advice based on the user's level of interest. In this way, the generation unit can provide effective advice by adjusting the order of advice based on the relevance of the child-rearing problem.

[0081] When generating advice, the generation unit may adjust the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, the generation unit provides detailed advice using technical terms. For example, if the user is a beginner, the generation unit provides easy-to-understand advice in simple terms. The generation unit may also select appropriate terms to provide advice according to the user's level of expertise. For example, the generation unit selects appropriate terms to provide advice according to the user's level of expertise. In this way, the generation unit can provide easy-to-understand advice by selecting appropriate terms according to the user's level of expertise.

[0082] The providing unit can estimate the user's emotions and adjust the way in which advice is provided based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit provides advice in gentle language. For example, if the user is relaxed, the providing unit provides advice including detailed explanations. The providing unit can also provide concise and to-the-point advice if the user is in a hurry. For example, if the user is in a hurry, the providing unit provides concise and to-the-point advice. In this way, the providing unit can adjust the way in which advice is provided according to the user's emotions, thereby providing more appropriate advice. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] When providing advice, the providing unit can select the optimal providing method by referring to the user's past feedback. The providing unit suggests the optimal providing method, for example, based on feedback provided by the user in the past. For example, the providing unit preferentially selects a specific providing method from the user's past feedback. The providing unit can also reflect the user's feedback and continuously improve the providing method. For example, the providing unit reflects the user's feedback and continuously improves the providing method. In this way, the providing unit can select the optimal providing method by referring to the user's past feedback.

[0084] When providing advice, the providing unit can customize the content of the advice based on the user's current child-rearing situation. For example, the providing unit prioritizes providing advice related to child-rearing problems the user is currently facing. For example, the providing unit customizes appropriate advice according to the user's child-rearing situation. The providing unit can also provide optimal advice taking into account the user's current situation. For example, the providing unit provides optimal advice taking into account the user's current situation. This allows the providing unit to provide appropriate advice based on the user's current child-rearing situation.

[0085] The providing unit can improve the advice providing method by reflecting user feedback when providing advice. For example, when a user provides feedback on the advice provided, the providing unit improves the advice providing method based on the feedback. For example, the providing unit analyzes the user's feedback and continuously reviews the optimal advice providing method. The providing unit can also customize the content of the advice and the method of providing the advice by reflecting user feedback. For example, the providing unit customizes the content of the advice and the method of providing the advice by reflecting user feedback. In this way, the providing unit can continuously improve the advice providing method by reflecting user feedback.

[0086] The providing unit can estimate the user's emotions and determine the order in which advice is provided based on the estimated user's emotions. For example, when the user is feeling stressed, the providing unit prioritizes providing more important advice. For example, when the user is relaxed, the providing unit prioritizes providing more detailed advice. The providing unit can also prioritize providing advice that requires a quick response when the user is in a hurry. For example, when the user is in a hurry, the providing unit prioritizes providing advice that requires a quick response. In this way, the providing unit can prioritize providing important advice by determining the order in which advice is provided according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] When providing advice, the providing unit can select the optimal providing method by taking into consideration the user's geographical location information. For example, if the user lives in a specific area, the providing unit can prioritize providing childcare advice related to that area. For example, if the user is traveling, the providing unit can provide appropriate childcare advice based on the user's current location. The providing unit can also prioritize providing advice on childcare issues specific to the area based on the user's geographical location information. For example, the providing unit prioritizes providing advice on childcare issues specific to the area based on the user's geographical location information. In this way, the providing unit can provide advice that addresses childcare issues specific to the area by taking into consideration the user's geographical location information.

[0088] When providing advice, the providing unit can analyze the user's social media activity and customize the content of the advice to be provided. For example, the providing unit provides relevant advice based on child-rearing posts shared by the user on social media. For example, the providing unit analyzes the user's social media activity and prioritizes providing child-rearing advice that is of interest to the user. The providing unit can also provide relevant child-rearing advice by taking into account the activity of the user's friends on social media. For example, the providing unit provides relevant child-rearing advice by taking into account the activity of the user's friends on social media. In this way, the providing unit can provide relevant advice by analyzing the user's social media activity.

[0089] When providing advice, the providing unit can customize the provision method by reflecting the user's past feedback. The providing unit, for example, suggests the optimal provision method based on feedback provided by the user in the past. For example, the providing unit preferentially selects a specific provision method from the user's past feedback. The providing unit can also reflect the user's feedback and continuously improve the provision method. For example, the providing unit reflects the user's feedback and continuously improves the provision method. In this way, the providing unit can provide the optimal provision method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation 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 reception unit receives input of child-rearing problems and situations from a user using the reception device 38 of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using the LLM to generate specific child-rearing advice. The provision unit provides the generated 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 above-described reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives input of child-rearing problems and situations from a user using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using an LLM to generate specific child-rearing advice. The provision unit provides the generated 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 above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives input of child-rearing problems and situations from the user using the microphone 238 of the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using the LLM to generate specific child-rearing advice. The provision unit provides the generated 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 above-mentioned reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives input of child-rearing problems and situations from the user using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information received by the reception unit using the LLM to generate specific child-rearing advice. The provision unit provides the generated advice to the user using, for example, the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The child-rearing advice providing system may further include a behavior analysis unit that collects the user's past behavioral data related to child-rearing and analyzes behavioral patterns. The behavior analysis unit records, for example, what kind of child-rearing advice the user received in the past and what actions the user took based on that advice. For example, if the user redesigns the room environment to address nighttime crying, the results are recorded and reflected in the next advice. The behavior analysis unit can also analyze the user's behavioral patterns and generate data for providing optimal advice. For example, if the user tends to report child-rearing problems during a specific time period, advice tailored to that time period can be provided. This allows the child-rearing advice providing system to utilize the user's past behavioral data to provide more effective advice.

[0092] The child-rearing advice providing system may further include a community analysis unit that collects community data related to child-rearing among users and reflects the opinions of the community. The community analysis unit, for example, analyzes posts from child-rearing forums and social media groups in which the user participates to extract common child-rearing problems and solutions. For example, if there are many posts about nighttime crying, the community analysis unit may reflect those solutions in the advice. The community analysis unit may also refer to advice the user has received from other parents. For example, it may suggest to the user parenting methods that other parents have found successful. This allows the child-rearing advice providing system to utilize the knowledge of the community and provide advice from a wider variety of perspectives.

[0093] The child-rearing advice providing system may further include a health analysis unit that collects health data related to the user's child-rearing and provides advice that takes into account the user's health condition. The health analysis unit, for example, collects health data of the user's child (body temperature, sleep time, dietary content, etc.) and provides advice based on the user's health condition. For example, if the child has a cold, it may suggest appropriate care methods or consulting a doctor. The health analysis unit may also take into account the user's health condition. For example, if the user is tired, it may suggest a reasonable child-rearing method. This allows the child-rearing advice providing system to provide advice that takes into account the health conditions of the user and the child.

[0094] The child-rearing advice providing system may further include an education analysis unit that collects the user's education data related to child-rearing and provides advice based on the user's education policy. The education analysis unit, for example, records the user's education policy and provides advice based on that policy. For example, if the user has an education policy that emphasizes independence for children, the education analysis unit provides advice in line with that policy. The education analysis unit may also refer to educational advice the user has received in the past. For example, if advice the user has received in the past was effective, the education analysis unit may suggest that advice again. This allows the child-rearing advice providing system to provide advice based on the user's education policy.

[0095] The child-rearing advice providing system can further include a cultural analysis unit that provides advice that takes into account the user's cultural background regarding child-rearing. The cultural analysis unit, for example, takes into account the user's cultural background (nationality, religion, traditions, etc.) and provides advice based on that background. For example, it may suggest child-rearing methods that are important in a particular culture. Furthermore, if the user is interested in other cultures, the cultural analysis unit can also introduce child-rearing methods based on those cultures. For example, if the user is interested in child-rearing methods in other countries, it may suggest those methods. This allows the child-rearing advice providing system to provide advice that takes into account the user's cultural background.

[0096] The child-rearing advice providing system may further include an emotion analysis unit that estimates the user's emotions and adjusts the content of advice based on the estimated user emotions. For example, if the user is feeling stressed, the emotion analysis unit may suggest ways to relax. For example, if the user is tired, the emotion analysis unit may suggest easy-to-follow child-rearing methods. The emotion analysis unit may also provide positive feedback if the user is happy. For example, if the user shares a successful experience, the emotion analysis unit may praise the user's efforts. This allows the child-rearing advice providing system to provide advice that is appropriate for the user's emotions.

[0097] The child-rearing advice providing system can further include a timing adjustment unit that estimates the user's emotions and adjusts the timing of advice based on the estimated user emotions. For example, the timing adjustment unit delays the timing of providing advice when the user is feeling stressed. For example, the timing adjustment unit advances the timing of providing advice when the user is relaxed. The timing adjustment unit can also provide advice quickly when the user is in a hurry. For example, when the user is in a hurry, the timing adjustment unit provides concise advice that is to the point. This allows the child-rearing advice providing system to provide advice at a timing that suits the user's emotions.

[0098] The child-rearing advice providing system may further include a format adjustment unit that estimates the user's emotions and adjusts the format of the advice based on the estimated user emotions. For example, the format adjustment unit provides visual advice when the user is stressed. For example, it provides detailed text advice when the user is relaxed. The format adjustment unit may also provide audio advice when the user is in a hurry. For example, it provides concise audio advice when the user is in a hurry. This allows the child-rearing advice providing system to provide advice in a format that suits the user's emotions.

[0099] The child-rearing advice providing system can further include a priority adjustment unit that estimates the user's emotions and adjusts the priority of advice based on the estimated user emotions. For example, the priority adjustment unit prioritizes providing important advice when the user is stressed. For example, if the user is relaxed, it prioritizes providing detailed advice. The priority adjustment unit can also prioritize providing advice that requires a quick response when the user is in a hurry. For example, if the user is in a hurry, it prioritizes providing advice that requires a quick response. This allows the child-rearing advice providing system to provide advice in order of priority according to the user's emotions.

[0100] The child-rearing advice providing system may further include a content customization unit that estimates the user's emotions and customizes the content of the advice based on the estimated user emotions. For example, if the user is feeling stressed, the content customization unit may suggest ways to relax. For example, if the user is happy, the content customization unit may provide positive feedback. The content customization unit may also provide concise and to-the-point advice if the user is in a hurry. For example, if the user is in a hurry, the content customization unit may provide concise and to-the-point advice. This allows the child-rearing advice providing system to provide advice with content that matches the user's emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The reception unit accepts input from the user about problems and situations related to childcare. The information entered by the user includes specific problems and situations such as eating, sleeping, and discipline. The reception unit uses natural language processing technology to analyze the information entered by the user, and then deletes personal information and encrypts the data to make it anonymous. Step 2: The generator uses LLM to analyze the information received by the receiver and generate specific childcare advice. The generator learns from specialized books and papers on childcare, as well as information from authorized childcare websites, and uses data supervised by experts. It also estimates the user's emotions and adjusts the way the advice is presented based on the estimated emotions. Step 3: The providing unit provides the advice generated by the generating unit to the user. The providing unit provides the advice in the form of text, audio, video, etc., and adjusts the way of providing the advice by estimating the user's feelings. For example, if the user is in a hurry, the providing unit provides concise and to-the-point advice.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] 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.

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

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 reception unit that receives input of problems and situations related to childcare from a user; a generation unit that analyzes the information received by the reception unit and generates specific child-rearing advice; a providing unit that provides the advice generated by the generating unit. A system characterized by:

2. The generation unit Learn from specialized books and papers on childcare, as well as information from authorized childcare websites 2. The system of claim 1.

3. The generation unit Use expert-curated data 2. The system of claim 1.

4. The reception unit The information entered by the user is anonymized and processed so that individuals cannot be identified.

2. The system of claim 1.

5. The reception unit Estimates user emotions and adjusts the timing of accepting input information based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past input history and select the optimal reception method 2. The system of claim 1.

7. The reception unit When accepting input information, it filters it based on the user's current parenting situation and areas of interest.

2. The system of claim 1.

Images