system

Abstract

The system according to the embodiment aims to provide appropriate advice regarding child-rearing. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a customization unit, and a selection unit. The reception unit receives questions about child-rearing from a user. The generation unit analyzes the questions received by the reception unit and generates advice. The customization unit customizes the advice generated by the generation unit according to the user's situation. The selection unit selects the most appropriate advice from the advice customized by the customization unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to obtain appropriate parenting advice, especially for first-time parents.

[0005] The system according to the embodiment aims to provide appropriate advice regarding child-rearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a customization unit, and a selection unit. The reception unit receives questions about child-rearing from a user. The generation unit analyzes the questions received by the reception unit and generates advice. The customization unit customizes the advice generated by the generation unit according to the user's situation. The selection unit selects the most appropriate advice from the advice customized by the customization unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate advice regarding 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 support system according to an embodiment of the present invention allows users to input questions or concerns about child-rearing, and AI analyzes the questions or concerns and provides appropriate advice. This system provides customized advice based on the user's situation, the child's personality, age, and other factors. This allows users to select the most appropriate advice from the AI's advice and apply it to their child-rearing needs. For example, when a user inputs a specific question such as, "My child cries at night. What should I do?", the AI ​​analyzes the question and generates optimal advice based on past data and expert knowledge. For example, the system may provide advice such as, "If your child cries at night, it's important to create a relaxing environment before bedtime." The provided advice is customized based on the user's situation, the child's personality, age, and other factors. For example, different advice may be provided for the same problem of night crying depending on the child's age and personality. This allows users to receive advice that is best suited to their situation. This system allows users to select the most appropriate advice from the AI's advice and apply it to their child-rearing needs. For example, by selecting the most appropriate advice from multiple pieces of advice and implementing it, users can resolve their child-rearing concerns. This service may also contribute to addressing the declining birthrate. AI support for parenting concerns reduces the burden on parents and makes parenting more enjoyable. This is expected to reduce anxiety about having children and contribute to measures to combat the declining birthrate. This allows the child-rearing support system to provide appropriate advice in response to users' questions about child-rearing, and to select customized advice.

[0029] A child-rearing support system according to an embodiment includes a reception unit, a generation unit, a customization unit, and a selection unit. The reception unit receives questions about child-rearing from a user. Questions from the user include, but are not limited to, child-rearing methods, health management, and educational principles. The reception unit, for example, receives specific questions entered by the user in text format. The reception unit can also receive questions in audio format. For example, the user may enter a question via voice and convert it into text for acceptance. The generation unit analyzes the questions received by the reception unit and generates advice. The generation unit generates advice based on, for example, past data and expert knowledge. For example, the generation unit analyzes past question history and statistical data to generate optimal advice. The generation unit can also generate advice based on expert opinions and academic papers. The customization unit customizes the advice generated by the generation unit according to the user's situation. The customization unit customizes the advice based on, for example, the user's profile information and past behavioral history. For example, the customization unit adjusts the advice based on the user's family environment and financial situation. The customization unit can also customize advice based on the personality and age of the child. The selection unit selects the most appropriate advice from the advice customized by the customization unit. The selection unit selects the optimal advice from multiple pieces of advice, for example, based on user feedback or algorithmic evaluation. For example, the selection unit suggests the optimal advice based on a history of advice previously selected by the user. The selection unit can also select advice based on the user's current situation and emotions. As a result, the child-rearing support system according to the embodiment can provide appropriate advice in response to a user's questions about child-rearing and select customized advice.

[0030] The generation unit can generate advice based on past data or expert knowledge. The generation unit, for example, analyzes past question history and statistical data to generate optimal advice. For example, the generation unit retrieves past question history from a database and generates new advice based on advice for similar questions. The generation unit can also generate advice based on expert opinions and academic papers. For example, the generation unit collects expert opinions and generates advice based on them. The generation unit can also analyze academic papers and generate advice based on the latest research results. This makes it possible to provide more reliable advice by utilizing past data and expert knowledge. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate advice using an AI model that inputs past data and expert knowledge and outputs advice.

[0031] The customization unit can customize the advice based on the user's situation, the child's personality, and age. The customization unit customizes the advice based on, for example, the user's profile information and past behavioral history. For example, the customization unit adjusts the advice based on the user's family environment and financial situation. The customization unit can also customize the advice based on the child's personality and age. For example, the customization unit may understand the child's personality based on a personality diagnostic test or parental observations and provide advice accordingly. The customization unit may also obtain the child's age based on user input information or a birth certificate and provide advice accordingly. This makes it possible to provide advice customized according to the user's situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can customize the advice using an AI model that inputs the user's profile information and past behavioral history and outputs advice.

[0032] The selection unit can select the most appropriate advice from multiple pieces of advice. For example, the selection unit selects the optimal advice from multiple pieces of advice based on user feedback or algorithmic evaluation. For example, the selection unit suggests the optimal advice based on a history of advice previously selected by the user. The selection unit can also select advice based on the user's current situation and emotions. For example, the selection unit estimates the user's emotions and provides detailed selection options if the user is relaxed, and provides simple selection options if the user is stressed. This allows the user to select the optimal advice. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select advice using an AI model that inputs multiple pieces of advice and outputs the optimal advice.

[0033] The reception unit can receive a specific question input by the user. For example, the reception unit receives the specific question input by the user in text format. The reception unit can also receive questions in voice format. For example, the user inputs a question by voice, which is converted into text and accepted. This allows the user to input a specific question. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, when receiving the specific question input by the user in text format, the reception unit can convert the voice into text using voice recognition technology.

[0034] The generation unit can generate specific advice regarding a child's night crying. The generation unit generates, for example, specific advice regarding a child's night crying. For example, the generation unit provides advice such as, if a child cries at night, it is important to create a relaxing environment before going to bed. The generation unit can also identify the cause of a child's night crying and provide advice accordingly. For example, if the cause of a child's night crying is a change in the environment, the generation unit provides advice to stabilize the environment. This makes it possible to provide specific advice regarding a child's night crying. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate advice using an AI model that inputs data regarding a child's night crying and outputs advice.

[0035] The reception unit can analyze the user's past question history and select the most appropriate reception method. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the user in the past. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception unit can also adjust the reception of questions to be accepted during specific time periods based on the user's past question history. This makes it possible to select the optimal reception method based on the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the user's past question history and selects the optimal reception method.

[0036] The reception unit can filter questions based on the user's current living situation or areas of interest when receiving a question. For example, when the user inputs their current living situation, the reception unit preferentially receives questions related to that information. The reception unit can also filter related questions based on the user's areas of interest and preferentially receive them. Furthermore, the reception unit can suggest an appropriate question format based on the user's living situation or areas of interest. This allows questions to be filtered based on the user's living situation or areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter questions using an AI model that inputs the user's living situation or areas of interest and filters questions.

[0037] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving questions related to that area. The reception unit can also filter related questions based on the user's geographical location information and prioritize receiving the questions. Furthermore, when the user is moving, the reception unit can also prioritize receiving related questions based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive questions using an AI model that receives the user's geographical location information as input and prioritizes receiving highly relevant questions.

[0038] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity and automatically suggest related questions. The reception unit can also preferentially receive related questions based on information shared by the user on social media. Furthermore, the reception unit can identify areas of interest from the user's social media activity and receive related questions. This makes it possible to receive related questions based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can receive questions using an AI model that uses the user's social media activity as input and receives related questions.

[0039] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the question. For example, the generation unit provides detailed advice for questions with high importance. The generation unit can also provide concise advice for questions with low importance. Furthermore, the generation unit can adjust the length and content of the advice based on the importance of the question. This makes it possible to adjust the level of detail of the advice based on the importance of the question. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the advice using an AI model that uses the importance of the question as an input and adjusts the level of detail of the advice.

[0040] When generating advice, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can apply an algorithm based on the knowledge of medical experts to a question about a child's health. The generation unit can also apply an algorithm based on the knowledge of education experts to a question about a child's education. Furthermore, the generation unit can apply an algorithm based on psychological knowledge to a question about a child's behavior. This makes it possible to apply an optimal generation algorithm depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate advice using an AI model that inputs the question category and applies an optimal generation algorithm.

[0041] When generating advice, the generation unit can determine the priority of the advice based on the time when the question was submitted. The generation unit can determine the priority of the advice based on, for example, the time period when the question was submitted. The generation unit can also determine the priority of the advice based on the date when the question was submitted. Furthermore, the generation unit can also determine the priority of the advice based on the timing when the question was submitted. In this way, the priority of the advice can be determined based on the time when the question was submitted. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can determine the priority of the advice using an AI model that uses the time when the question was submitted as input and determines the priority of the advice.

[0042] When generating advice, the generation unit can adjust the order of advice based on the relevance of the question. For example, the generation unit prioritizes providing the most relevant advice based on the relevance of the question. The generation unit can also adjust the order of advice based on the relevance of the question. Furthermore, the generation unit can group and provide related advice based on the relevance of the question. This makes it possible to adjust the order of advice based on the relevance of the question. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of advice using an AI model that uses the relevance of the question as input and adjusts the order of advice.

[0043] During customization, the customization unit can analyze the user's past behavioral history and select the most appropriate customization method. For example, the customization unit can suggest optimal customization options based on the user's past behavioral history. The customization unit can also prioritize suggesting customization methods previously selected by the user. Furthermore, the customization unit can analyze specific patterns from the user's past behavioral history and select the optimal customization method. This allows the optimal customization method to be selected based on the user's past behavioral history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can select the customization method using an AI model that inputs the user's past behavioral history and selects the optimal customization method.

[0044] During customization, the customization unit can customize the customization means based on the user's current living situation. For example, the customization unit can suggest optimal customization options based on the user's current living situation. The customization unit can also adjust the customization means according to the user's living situation. Furthermore, the customization unit can also suggest customization means taking the user's current living situation into consideration. This allows the customization means to be adjusted based on the user's current living situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can adjust the customization means using an AI model that uses the user's current living situation as input and adjusts the customization means.

[0045] During customization, the customization unit can select the most appropriate customization method based on the user's geographical location information. For example, the customization unit can suggest optimal customization options based on the user's geographical location information. Furthermore, if the user is in a specific region, the customization unit can also suggest a customization method related to that region. Furthermore, the customization unit can adjust the customization method taking into account the user's geographical location information. This allows the optimal customization method to be selected based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can select the customization method using an AI model that inputs the user's geographical location information and selects the optimal customization method.

[0046] During customization, the customization unit can analyze the user's social media activity and suggest customization options. For example, the customization unit can analyze the user's social media activity and suggest related customization options. The customization unit can also suggest an optimal customization method based on information shared by the user on social media. Furthermore, the customization unit can identify areas of interest from the user's social media activity and suggest customization options. This allows customization options to be suggested based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can suggest customization options using an AI model that inputs the user's social media activity and suggests customization options.

[0047] At the time of selection, the selection unit can analyze the user's past selection history and select the optimal selection method. The selection unit, for example, suggests optimal selection options based on the user's past selection history. The selection unit can also preferentially suggest methods previously selected by the user. Furthermore, the selection unit can analyze specific patterns from the user's past selection history and select the optimal selection method. This allows the optimal selection method to be selected based on the user's past selection history. Some or all of the above-described processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can select the selection method using an AI model that inputs the user's past selection history and selects the optimal selection method.

[0048] The selection unit can customize the selection means based on the user's current living situation at the time of selection. The selection unit, for example, suggests an optimal selection option based on the user's current living situation. The selection unit can also adjust the selection means according to the user's living situation. Furthermore, the selection unit can also suggest the selection means taking the user's current living situation into consideration. This allows the selection means to be adjusted based on the user's current living situation. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can adjust the selection means using an AI model that uses the user's current living situation as input and adjusts the selection means.

[0049] The selection unit can select the most appropriate selection method based on the user's geographical location information when making a selection. For example, the selection unit can suggest optimal selection options based on the user's geographical location information. Furthermore, if the user is in a specific area, the selection unit can suggest a selection method related to that area. Furthermore, the selection unit can adjust the selection method taking into account the user's geographical location information. This allows the optimal selection method to be selected based on the user's geographical location information. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can select the selection method using an AI model that inputs the user's geographical location information and selects the optimal selection method.

[0050] At the time of selection, the selection unit can analyze the user's social media activity to suggest a means of selection. For example, the selection unit can analyze the user's social media activity and suggest related selection options. The selection unit can also suggest an optimal selection method based on information shared by the user on social media. Furthermore, the selection unit can identify areas of interest from the user's social media activity and suggest a means of selection. This allows the selection means to be suggested based on the user's social media activity. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can suggest a means of selection using an AI model that inputs the user's social media activity and suggests a means of selection.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When accepting a user's question, the acceptance unit can analyze the user's past question history and automatically suggest related questions. For example, if the user has asked many questions about nighttime crying in the past, the acceptance unit can suggest related questions. The acceptance unit can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. Furthermore, the acceptance unit can adjust the acceptance of questions to specific time periods based on the user's past question history. This allows the optimal acceptance method to be selected based on the user's past question history.

[0053] The customization unit can adjust the customization means based on the user's current lifestyle. For example, if the user is busy, concise and easy-to-follow advice can be provided. Alternatively, if the user is relaxed, advice with detailed explanations can be provided. Furthermore, the frequency and timing of advice can be adjusted according to the user's lifestyle. This allows the user to receive advice tailored to their lifestyle.

[0054] The reception unit can prioritize reception of highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, questions related to that area are prioritized. Also, based on the user's geographical location information, related questions can be filtered and prioritized. Furthermore, if the user is moving, related questions can be prioritized based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information.

[0055] When generating advice, the generator can adjust the level of detail of the advice based on the importance of the question. For example, detailed advice can be provided for questions with high importance. Concise advice can also be provided for questions with low importance. Furthermore, the length and content of the advice can be adjusted according to the importance of the question. This makes it possible to adjust the level of detail of the advice according to the importance of the question.

[0056] When making a selection, the selection unit can analyze the user's past selection history and select the optimal selection method. For example, the selection unit can suggest the optimal selection option based on the user's past selection history. It can also preferentially suggest methods that the user has previously selected. Furthermore, it can analyze specific patterns from the user's past selection history and select the optimal selection method. This allows the optimal selection method to be selected based on the user's past selection history.

[0057] During customization, the customization unit can analyze the user's social media activity to suggest customization methods. For example, the customization unit can analyze the user's social media activity and suggest related customization options. The customization unit can also suggest the optimal customization method based on information shared by the user on social media. Furthermore, the customization unit can identify areas of interest from the user's social media activity and suggest customization methods. This allows the customization unit to suggest customization methods based on the user's social media activity.

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

[0059] Step 1: The reception unit receives questions about child-rearing from the user. Questions from the user include child-rearing methods, health management, educational principles, etc. The reception unit not only receives specific questions entered by the user in text format, but can also receive questions in voice format. For example, the user can enter a question by voice, which is converted into text and accepted. Step 2: The generator analyzes the question received by the receiver and generates advice. The generator generates advice based on past data and expert knowledge. For example, it analyzes past question history and statistical data to generate optimal advice. It can also generate advice based on expert opinions and academic papers. Step 3: The customization unit customizes the advice generated by the generation unit according to the user's situation. The customization unit customizes the advice based on the user's profile information and past behavioral history. For example, the customization unit adjusts the advice based on the user's family environment, financial situation, and the personality and age of the child. Step 4: The selection unit selects the most appropriate advice from among the advice customized by the customization unit. The selection unit selects the most appropriate advice from among multiple pieces of advice based on user feedback and algorithmic evaluation. For example, the selection unit selects advice based on the user's history of previously selected advice, their current situation, and their emotions.

[0060] (Example 2) A child-rearing support system according to an embodiment of the present invention allows users to input questions or concerns about child-rearing, and AI analyzes the questions or concerns and provides appropriate advice. This system provides customized advice based on the user's situation, the child's personality, age, and other factors. This allows users to select the most appropriate advice from the AI's advice and apply it to their child-rearing needs. For example, when a user inputs a specific question such as, "My child cries at night. What should I do?", the AI ​​analyzes the question and generates optimal advice based on past data and expert knowledge. For example, the system may provide advice such as, "If your child cries at night, it's important to create a relaxing environment before bedtime." The provided advice is customized based on the user's situation, the child's personality, age, and other factors. For example, different advice may be provided for the same problem of night crying depending on the child's age and personality. This allows users to receive advice that is best suited to their situation. This system allows users to select the most appropriate advice from the AI's advice and apply it to their child-rearing needs. For example, by selecting the most appropriate advice from multiple pieces of advice and implementing it, users can resolve their child-rearing concerns. This service may also contribute to addressing the declining birthrate. AI support for parenting concerns reduces the burden on parents and makes parenting more enjoyable. This is expected to reduce anxiety about having children and contribute to measures to combat the declining birthrate. This allows the child-rearing support system to provide appropriate advice in response to users' questions about child-rearing, and to select customized advice.

[0061] A child-rearing support system according to an embodiment includes a reception unit, a generation unit, a customization unit, and a selection unit. The reception unit receives questions about child-rearing from a user. Questions from the user include, but are not limited to, child-rearing methods, health management, and educational principles. The reception unit, for example, receives specific questions entered by the user in text format. The reception unit can also receive questions in audio format. For example, the user may enter a question via voice and convert it into text for acceptance. The generation unit analyzes the questions received by the reception unit and generates advice. The generation unit generates advice based on, for example, past data and expert knowledge. For example, the generation unit analyzes past question history and statistical data to generate optimal advice. The generation unit can also generate advice based on expert opinions and academic papers. The customization unit customizes the advice generated by the generation unit according to the user's situation. The customization unit customizes the advice based on, for example, the user's profile information and past behavioral history. For example, the customization unit adjusts the advice based on the user's family environment and financial situation. The customization unit can also customize advice based on the personality and age of the child. The selection unit selects the most appropriate advice from the advice customized by the customization unit. The selection unit selects the optimal advice from multiple pieces of advice, for example, based on user feedback or algorithmic evaluation. For example, the selection unit suggests the optimal advice based on a history of advice previously selected by the user. The selection unit can also select advice based on the user's current situation and emotions. As a result, the child-rearing support system according to the embodiment can provide appropriate advice in response to a user's questions about child-rearing and select customized advice.

[0062] The generation unit can generate advice based on past data or expert knowledge. The generation unit, for example, analyzes past question history and statistical data to generate optimal advice. For example, the generation unit retrieves past question history from a database and generates new advice based on advice for similar questions. The generation unit can also generate advice based on expert opinions and academic papers. For example, the generation unit collects expert opinions and generates advice based on them. The generation unit can also analyze academic papers and generate advice based on the latest research results. This makes it possible to provide more reliable advice by utilizing past data and expert knowledge. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate advice using an AI model that inputs past data and expert knowledge and outputs advice.

[0063] The customization unit can customize the advice based on the user's situation, the child's personality, and age. The customization unit customizes the advice based on, for example, the user's profile information and past behavioral history. For example, the customization unit adjusts the advice based on the user's family environment and financial situation. The customization unit can also customize the advice based on the child's personality and age. For example, the customization unit may understand the child's personality based on a personality diagnostic test or parental observations and provide advice accordingly. The customization unit may also obtain the child's age based on user input information or a birth certificate and provide advice accordingly. This makes it possible to provide advice customized according to the user's situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can customize the advice using an AI model that inputs the user's profile information and past behavioral history and outputs advice.

[0064] The selection unit can select the most appropriate advice from multiple pieces of advice. For example, the selection unit selects the optimal advice from multiple pieces of advice based on user feedback or algorithmic evaluation. For example, the selection unit suggests the optimal advice based on a history of advice previously selected by the user. The selection unit can also select advice based on the user's current situation and emotions. For example, the selection unit estimates the user's emotions and provides detailed selection options if the user is relaxed, and provides simple selection options if the user is stressed. This allows the user to select the optimal advice. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can select advice using an AI model that inputs multiple pieces of advice and outputs the optimal advice.

[0065] The reception unit can receive a specific question input by the user. For example, the reception unit receives the specific question input by the user in text format. The reception unit can also receive questions in voice format. For example, the user inputs a question by voice, which is converted into text and accepted. This allows the user to input a specific question. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, when receiving the specific question input by the user in text format, the reception unit can convert the voice into text using voice recognition technology.

[0066] The generation unit can generate specific advice regarding a child's night crying. The generation unit generates, for example, specific advice regarding a child's night crying. For example, the generation unit provides advice such as, if a child cries at night, it is important to create a relaxing environment before going to bed. The generation unit can also identify the cause of a child's night crying and provide advice accordingly. For example, if the cause of a child's night crying is a change in the environment, the generation unit provides advice to stabilize the environment. This makes it possible to provide specific advice regarding a child's night crying. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate advice using an AI model that inputs data regarding a child's night crying and outputs advice.

[0067] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit adjusts the time to receive questions so that the AI ​​can relax. Furthermore, if the user is relaxed, the reception unit can allow the AI ​​to immediately receive questions and respond quickly. Furthermore, if the user is in a hurry, the reception unit can allow the AI ​​to receive questions preferentially and begin analyzing them quickly. This allows the timing of question reception to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can adjust the timing of question reception using an AI model that receives user emotion data as input and adjusts the timing of question reception.

[0068] The reception unit can analyze the user's past question history and select the most appropriate reception method. For example, the reception unit automatically suggests related questions based on the content of questions frequently asked by the user in the past. The reception unit can also preferentially suggest question formats (text, voice, etc.) that the user has used in the past. Furthermore, the reception unit can also adjust the reception of questions to be accepted during specific time periods based on the user's past question history. This makes it possible to select the optimal reception method based on the user's past question history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can select the reception method using an AI model that inputs the user's past question history and selects the optimal reception method.

[0069] The reception unit can filter questions based on the user's current living situation or areas of interest when receiving a question. For example, when the user inputs their current living situation, the reception unit preferentially receives questions related to that information. The reception unit can also filter related questions based on the user's areas of interest and preferentially receive them. Furthermore, the reception unit can suggest an appropriate question format based on the user's living situation or areas of interest. This allows questions to be filtered based on the user's living situation or areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can filter questions using an AI model that inputs the user's living situation or areas of interest and filters questions.

[0070] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can have the AI ​​analyze the question with priority. Furthermore, if the user is relaxed, the reception unit can have the AI ​​receive the question with normal priority. Furthermore, if the user is in a hurry, the reception unit can have the AI ​​receive the question with top priority. This allows the priority of questions to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input user emotion data and use an AI model that determines the priority of questions to determine the priority of questions.

[0071] When receiving a question, the reception unit can prioritize receiving highly relevant questions based on the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving questions related to that area. The reception unit can also filter related questions based on the user's geographical location information and prioritize receiving the questions. Furthermore, when the user is moving, the reception unit can also prioritize receiving related questions based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can receive questions using an AI model that receives the user's geographical location information as input and prioritizes receiving highly relevant questions.

[0072] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity and automatically suggest related questions. The reception unit can also preferentially receive related questions based on information shared by the user on social media. Furthermore, the reception unit can identify areas of interest from the user's social media activity and receive related questions. This makes it possible to receive related questions based on the user's social media activity. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can receive questions using an AI model that uses the user's social media activity as input and receives related questions.

[0073] The generation unit can 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 relaxed, the generation unit can provide advice in a gentle tone. Furthermore, if the user is stressed, the generation unit can provide concise and clear advice. Furthermore, if the user is in a hurry, the generation unit can provide quick and specific advice. This allows the way the advice is presented to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the way the advice is presented using an AI model that inputs the user's emotion data and adjusts the way the advice is presented.

[0074] When generating advice, the generation unit can adjust the level of detail of the advice based on the importance of the question. For example, the generation unit provides detailed advice for questions with high importance. The generation unit can also provide concise advice for questions with low importance. Furthermore, the generation unit can adjust the length and content of the advice based on the importance of the question. This makes it possible to adjust the level of detail of the advice based on the importance of the question. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the level of detail of the advice using an AI model that uses the importance of the question as an input and adjusts the level of detail of the advice.

[0075] When generating advice, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit can apply an algorithm based on the knowledge of medical experts to a question about a child's health. The generation unit can also apply an algorithm based on the knowledge of education experts to a question about a child's education. Furthermore, the generation unit can apply an algorithm based on psychological knowledge to a question about a child's behavior. This makes it possible to apply an optimal generation algorithm depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can generate advice using an AI model that inputs the question category and applies an optimal generation algorithm.

[0076] The generation unit can estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can provide short, to-the-point advice. Furthermore, if the user is relaxed, the generation unit can provide longer advice with detailed explanations. Furthermore, if the user is stressed, the generation unit can provide concise, clear advice. This allows the length of the advice to be adjusted according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the length of the advice using an AI model that inputs the user's emotion data and adjusts the length of the advice.

[0077] When generating advice, the generation unit can determine the priority of the advice based on the time when the question was submitted. The generation unit can determine the priority of the advice based on, for example, the time period when the question was submitted. The generation unit can also determine the priority of the advice based on the date when the question was submitted. Furthermore, the generation unit can also determine the priority of the advice based on the timing when the question was submitted. In this way, the priority of the advice can be determined based on the time when the question was submitted. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can determine the priority of the advice using an AI model that uses the time when the question was submitted as input and determines the priority of the advice.

[0078] When generating advice, the generation unit can adjust the order of advice based on the relevance of the question. For example, the generation unit prioritizes providing the most relevant advice based on the relevance of the question. The generation unit can also adjust the order of advice based on the relevance of the question. Furthermore, the generation unit can group and provide related advice based on the relevance of the question. This makes it possible to adjust the order of advice based on the relevance of the question. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can adjust the order of advice using an AI model that uses the relevance of the question as input and adjusts the order of advice.

[0079] The customization unit can estimate the user's emotions and adjust the customization method based on the estimated user emotions. For example, the customization unit can provide detailed customization options when the user is relaxed. The customization unit can also provide simple customization options when the user is stressed. Furthermore, the customization unit can provide quick customization options when the user is in a hurry. This allows the customization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can adjust the customization method using an AI model that uses the user's emotion data as input and adjusts the customization method.

[0080] During customization, the customization unit can analyze the user's past behavioral history and select the most appropriate customization method. For example, the customization unit can suggest optimal customization options based on the user's past behavioral history. The customization unit can also prioritize suggesting customization methods previously selected by the user. Furthermore, the customization unit can analyze specific patterns from the user's past behavioral history and select the optimal customization method. This allows the optimal customization method to be selected based on the user's past behavioral history. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can select the customization method using an AI model that inputs the user's past behavioral history and selects the optimal customization method.

[0081] During customization, the customization unit can customize the customization means based on the user's current living situation. For example, the customization unit can suggest optimal customization options based on the user's current living situation. The customization unit can also adjust the customization means according to the user's living situation. Furthermore, the customization unit can also suggest customization means taking the user's current living situation into consideration. This allows the customization means to be adjusted based on the user's current living situation. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can adjust the customization means using an AI model that uses the user's current living situation as input and adjusts the customization means.

[0082] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated user's emotions. For example, the customization unit can prioritize customization when the user is stressed. The customization unit can also perform customization with normal priority when the user is relaxed. Furthermore, the customization unit can also perform customization with the highest priority when the user is in a hurry. This allows the priority of customization to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the customization unit can be performed using, for example, AI, or without AI. For example, the customization unit can determine the priority of customization using an AI model that inputs user emotion data and determines the priority of customization.

[0083] During customization, the customization unit can select the most appropriate customization method based on the user's geographical location information. For example, the customization unit can suggest optimal customization options based on the user's geographical location information. Furthermore, if the user is in a specific region, the customization unit can also suggest a customization method related to that region. Furthermore, the customization unit can adjust the customization method taking into account the user's geographical location information. This allows the optimal customization method to be selected based on the user's geographical location information. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can select the customization method using an AI model that inputs the user's geographical location information and selects the optimal customization method.

[0084] During customization, the customization unit can analyze the user's social media activity and suggest customization options. For example, the customization unit can analyze the user's social media activity and suggest related customization options. The customization unit can also suggest an optimal customization method based on information shared by the user on social media. Furthermore, the customization unit can identify areas of interest from the user's social media activity and suggest customization options. This allows customization options to be suggested based on the user's social media activity. Some or all of the above-described processing in the customization unit may be performed using, for example, AI, or may be performed without using AI. For example, the customization unit can suggest customization options using an AI model that inputs the user's social media activity and suggests customization options.

[0085] The selection unit can estimate the user's emotion and adjust the selection method based on the estimated user's emotion. For example, the selection unit can provide detailed selection options when the user is relaxed. The selection unit can also provide simple selection options when the user is stressed. Furthermore, the selection unit can provide quick selection options when the user is in a hurry. This allows the selection method to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or without AI. For example, the selection unit can adjust the selection method using an AI model that inputs user emotion data and adjusts the selection method.

[0086] At the time of selection, the selection unit can analyze the user's past selection history and select the optimal selection method. The selection unit, for example, suggests optimal selection options based on the user's past selection history. The selection unit can also preferentially suggest methods previously selected by the user. Furthermore, the selection unit can analyze specific patterns from the user's past selection history and select the optimal selection method. This allows the optimal selection method to be selected based on the user's past selection history. Some or all of the above-described processing in the selection unit may be performed, for example, using AI or may be performed without using AI. For example, the selection unit can select the selection method using an AI model that inputs the user's past selection history and selects the optimal selection method.

[0087] The selection unit can customize the selection means based on the user's current living situation at the time of selection. The selection unit, for example, suggests an optimal selection option based on the user's current living situation. The selection unit can also adjust the selection means according to the user's living situation. Furthermore, the selection unit can also suggest the selection means taking the user's current living situation into consideration. This allows the selection means to be adjusted based on the user's current living situation. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can adjust the selection means using an AI model that uses the user's current living situation as input and adjusts the selection means.

[0088] The selection unit can estimate the user's emotions and determine a selection priority based on the estimated user's emotions. For example, the selection unit prioritizes selection when the user is stressed. The selection unit can also prioritize selection when the user is relaxed. Furthermore, the selection unit can prioritize selection when the user is in a hurry. This allows the selection priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can determine the selection priority using an AI model that receives user emotion data as input and determines the selection priority.

[0089] The selection unit can select the most appropriate selection method based on the user's geographical location information when making a selection. For example, the selection unit can suggest optimal selection options based on the user's geographical location information. Furthermore, if the user is in a specific area, the selection unit can suggest a selection method related to that area. Furthermore, the selection unit can adjust the selection method taking into account the user's geographical location information. This allows the optimal selection method to be selected based on the user's geographical location information. Some or all of the above-described processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can select the selection method using an AI model that inputs the user's geographical location information and selects the optimal selection method.

[0090] At the time of selection, the selection unit can analyze the user's social media activity to suggest a means of selection. For example, the selection unit can analyze the user's social media activity and suggest related selection options. The selection unit can also suggest an optimal selection method based on information shared by the user on social media. Furthermore, the selection unit can identify areas of interest from the user's social media activity and suggest a means of selection. This allows the selection means to be suggested based on the user's social media activity. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can suggest a means of selection using an AI model that inputs the user's social media activity and suggests a means of selection. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, customization unit, and selection 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 is realized by the reception device 38 of the smart device 14 and receives questions about child-rearing from the user in text or voice format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on past data and expert knowledge. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and customizes advice based on the user's profile information and past behavioral history. The selection unit is realized, for example, by the control unit 46A of the smart device 14 and selects the most appropriate advice from the customized advice. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, customization unit, and selection 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 is realized by the microphone 238 of the smart glasses 214 and receives questions about child-rearing from the user in audio format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on past data and expert knowledge. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and customizes advice based on the user's profile information and past behavioral history. The selection unit is realized, for example, by the control unit 46A of the smart glasses 214 and selects the most appropriate advice from the customized advice. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, customization unit, and selection 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 is realized by the microphone 238 of the headset-type terminal 314 and receives questions about child-rearing from the user in audio format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on past data and expert knowledge. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and customizes advice based on the user's profile information and past behavioral history. The selection unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and selects the most appropriate advice from the customized advice. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, customization unit, and selection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions about child-rearing from the user in audio format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates advice based on past data and expert knowledge. The customization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and customizes advice based on the user's profile information and past behavioral history. The selection unit is realized, for example, by the control unit 46A of the robot 414 and selects the most appropriate advice from the customized advice.

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

[0092] When accepting a user's question, the acceptance unit can analyze the user's past question history and automatically suggest related questions. For example, if the user has asked many questions about nighttime crying in the past, the acceptance unit can suggest related questions. The acceptance unit can also prioritize suggesting question formats (text, voice, etc.) that the user has used in the past. Furthermore, the acceptance unit can adjust the acceptance of questions to specific time periods based on the user's past question history. This allows the optimal acceptance method to be selected based on the user's past question history.

[0093] The generation unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can provide advice in a gentle tone. If the user is stressed, the generation unit can provide concise and clear advice. If the user is in a hurry, the generation unit can provide quick and specific advice. In this way, the generation unit can adjust the way in which advice is expressed depending on the user's emotions.

[0094] The customization unit can adjust the customization means based on the user's current lifestyle. For example, if the user is busy, concise and easy-to-follow advice can be provided. Alternatively, if the user is relaxed, advice with detailed explanations can be provided. Furthermore, the frequency and timing of advice can be adjusted according to the user's lifestyle. This allows the user to receive advice tailored to their lifestyle.

[0095] The selection unit can estimate the user's emotion and adjust the selection method based on the estimated user's emotion. For example, if the user is relaxed, detailed selection options can be provided. If the user is stressed, simple selection options can be provided. Furthermore, if the user is in a hurry, options that allow for quick selection can be provided. In this way, the selection method can be adjusted according to the user's emotion.

[0096] The reception unit can prioritize reception of highly relevant questions based on the user's geographical location information. For example, if the user is in a specific area, questions related to that area are prioritized. Also, based on the user's geographical location information, related questions can be filtered and prioritized. Furthermore, if the user is moving, related questions can be prioritized based on the user's current location. This allows highly relevant questions to be prioritized based on the user's geographical location information.

[0097] When generating advice, the generator can adjust the level of detail of the advice based on the importance of the question. For example, detailed advice can be provided for questions with high importance. Concise advice can also be provided for questions with low importance. Furthermore, the length and content of the advice can be adjusted according to the importance of the question. This makes it possible to adjust the level of detail of the advice according to the importance of the question.

[0098] The customization unit can estimate the user's emotion and adjust the customization method based on the estimated user's emotion. For example, if the user is relaxed, detailed customization options can be provided. If the user is stressed, simple customization options can be provided. Furthermore, if the user is in a hurry, options that allow quick customization can be provided. In this way, the customization method can be adjusted according to the user's emotion.

[0099] When making a selection, the selection unit can analyze the user's past selection history and select the optimal selection method. For example, the selection unit can suggest the optimal selection option based on the user's past selection history. It can also preferentially suggest methods that the user has previously selected. Furthermore, it can analyze specific patterns from the user's past selection history and select the optimal selection method. This allows the optimal selection method to be selected based on the user's past selection history.

[0100] The generator can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, if the user is in a hurry, short and to the point advice can be provided. If the user is relaxed, longer advice with detailed explanations can be provided. Furthermore, if the user is stressed, concise and clear advice can be provided. In this way, the length of the advice can be adjusted according to the user's emotions.

[0101] During customization, the customization unit can analyze the user's social media activity to suggest customization methods. For example, the customization unit can analyze the user's social media activity and suggest related customization options. The customization unit can also suggest the optimal customization method based on information shared by the user on social media. Furthermore, the customization unit can identify areas of interest from the user's social media activity and suggest customization methods. This allows the customization unit to suggest customization methods based on the user's social media activity.

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

[0103] Step 1: The reception unit receives questions about child-rearing from the user. Questions from the user include child-rearing methods, health management, educational principles, etc. The reception unit not only receives specific questions entered by the user in text format, but can also receive questions in voice format. For example, the user can enter a question by voice, which is converted into text and accepted. Step 2: The generator analyzes the question received by the receiver and generates advice. The generator generates advice based on past data and expert knowledge. For example, it analyzes past question history and statistical data to generate optimal advice. It can also generate advice based on expert opinions and academic papers. Step 3: The customization unit customizes the advice generated by the generation unit according to the user's situation. The customization unit customizes the advice based on the user's profile information and past behavioral history. For example, the customization unit adjusts the advice based on the user's family environment, financial situation, and the personality and age of the child. Step 4: The selection unit selects the most appropriate advice from among the advice customized by the customization unit. The selection unit selects the most appropriate advice from among multiple pieces of advice based on user feedback and algorithmic evaluation. For example, the selection unit selects advice based on the user's history of previously selected advice, their current situation, and their emotions.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0125] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0138] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0148] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0155] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0157] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0158] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0159] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0160] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0162] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0163] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0164] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0165] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0166] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0167] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0168] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0169] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0170] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0171] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0172] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0173] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0174] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions about child-rearing from users; a generation unit that analyzes the question received by the reception unit and generates advice; a customization unit that customizes the advice generated by the generation unit according to a user's situation; a selection unit that selects the most appropriate advice from among the advice customized by the customization unit. A system characterized by:

2. The generation unit Generate advice based on historical data or expert knowledge 2. The system of claim 1.

3. The customization unit Customize advice based on the user's situation, the child's personality, and their age 2. The system of claim 1.

4. The selection unit Choose the most appropriate advice from multiple options 2. The system of claim 1.

5. The reception unit Accept specific questions entered by the user 2. The system of claim 1.

6. The generation unit Generate specific advice for children's night crying 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past question history and select the most appropriate reception method 2. The system of claim 1.

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