system

Abstract

The system according to the embodiment aims to efficiently process instructions and consultations regarding childcare and to automatically make the necessary records and orders. [Solution] The system according to the embodiment includes a reception unit, an analysis unit, a recording unit, and an ordering unit. The reception unit receives instructions or inquiries from parents. The analysis unit analyzes and processes the instructions or inquiries received by the reception unit. The recording unit creates a childcare record based on the content analyzed by the analysis unit. The ordering unit searches online stores based on the content analyzed by the analysis unit and carries out the ordering process.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately handle childcare instructions and consultations efficiently and automate the necessary records and orders, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently process instructions and consultations regarding childcare and to automatically make the necessary records and orders. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a recording unit, and an ordering unit. The reception unit receives instructions or inquiries from parents. The analysis unit analyzes and processes the instructions or inquiries received by the reception unit. The recording unit creates a childcare record based on the content analyzed by the analysis unit. The ordering unit searches online stores based on the content analyzed by the analysis unit and performs the ordering process. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently process instructions and consultations regarding childcare and automatically make the necessary records and orders. [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 childcare support system according to an embodiment of the present invention provides support to mothers and fathers facing childcare challenges, offering solutions and keeping necessary records. This system is compatible with multiple devices, including smartphones and smart speakers. Parents simply speak to the system to provide instructions and advice, and the system performs recording, searches, applications, and orders. The system also provides suggestions and prompts to parents as needed. For example, parents may speak to their smartphones or smart speakers to provide instructions or advice. For example, they may issue commands such as "Keep a record of today's childcare" or "Order baby formula." These commands are input into a generation AI, which then analyzes the input and performs appropriate processing. For example, when keeping a childcare record, the generation AI analyzes the parent's statements and automatically creates a childcare record. When ordering formula, the generation AI searches for an appropriate online store and completes the order. Furthermore, the generation AI analyzes the parent's concerns and offers appropriate solutions. For example, when a parent consults the system about a baby's nighttime crying, the generation AI proposes solutions based on past data and expert advice. The system will also make suggestions and calls to parents as needed. For example, it will provide regular childcare advice tailored to the baby's growth and remind them of vaccination schedules. This system reduces the burden on parents in childcare and allows them to raise their children more efficiently. For example, by automatically creating childcare records, parents can save time and focus on raising their children. In addition, the generative AI can suggest appropriate solutions, making it easier for parents to resolve their childcare concerns. In this way, the childcare support system reduces the burden on parents in childcare and allows them to raise their children more efficiently.

[0029] A childcare support system according to an embodiment includes a reception unit, an analysis unit, a recording unit, and an order unit. The reception unit receives instructions or inquiries from parents. Examples of instructions or inquiries from parents include, but are not limited to, childcare instructions and health-related inquiries. The reception unit receives instructions or inquiries from parents via, for example, a smartphone or smart speaker. The analysis unit analyzes the instructions or inquiries received by the reception unit and performs appropriate processing. The analysis unit analyzes the parent's instructions or inquiries using, for example, text analysis or voice analysis. The analysis unit can analyze the parent's instructions or inquiries using a generation AI and perform appropriate processing. The recording unit creates a childcare record based on the content analyzed by the analysis unit. The childcare record includes, for example, information on the baby's diet, sleep, excretion, and body temperature, but is not limited to, examples. The recording unit records, for example, the baby's dietary content and amount, sleep duration, number and status of excretion, and body temperature measurement results. The recording unit can automatically create the childcare record using AI. The ordering unit searches online stores based on the content analyzed by the analysis unit and performs the ordering process. The ordering unit orders, for example, childcare products such as baby milk and diapers from online stores. The ordering unit can use AI to search for appropriate online stores and automatically perform the ordering process. As a result, the childcare support system according to the embodiment can efficiently accept and analyze instructions and inquiries from parents, and automatically record and place orders. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs instructions and inquiries from parents into the generation AI, and the generation AI outputs the analysis results. Some or all of the above-described processing in the recording unit may be performed using an AI, or may be performed without using an AI. For example, the recording unit causes an AI to create a childcare record. Some or all of the above-described processing in the ordering unit may be performed using an AI, or may be performed without using an AI. For example, the ordering unit causes an AI to search online stores and perform the ordering process.

[0030] The analysis unit can analyze the parent's consultation content and present an appropriate solution. The analysis unit, for example, analyzes the parent's consultation content using text analysis or voice analysis. For example, if a parent complains that "my baby cries a lot at night," the analysis unit uses the generation AI to present a solution based on past data and expert advice. The analysis unit can analyze the parent's consultation content and present an appropriate solution using the generation AI. For example, the analysis unit inputs a prompt to the generation AI, such as "Please tell me how to solve my baby's night crying problem," and the generation AI outputs a solution based on past data and expert advice. This makes it possible to present an appropriate solution based on the parent's consultation content. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the parent's consultation content to the generation AI, and the generation AI outputs a solution.

[0031] The analysis unit can propose a solution based on past data or expert advice. The analysis unit, for example, proposes a solution using past data. For example, the analysis unit can propose a solution based on past consultation history and statistical data. The analysis unit can also propose a solution based on expert advice. For example, the analysis unit proposes a solution based on a doctor's opinion or guidelines from a childcare expert. The analysis unit can use a generation AI to propose a solution based on past data and expert advice. For example, the analysis unit inputs past consultation history and statistical data into the generation AI, which then outputs a solution. This makes it possible to propose a more accurate solution based on past data and expert advice. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs past data and expert advice into the generation AI, which then outputs a solution.

[0032] The recording unit can record information about the baby's diet, sleep, excretion, and body temperature. For example, the recording unit records the content and amount of the baby's diet. For example, the recording unit records what the baby ate and how much it ate. The recording unit can also record the baby's sleep time. For example, the recording unit records what time the baby went to bed and what time the baby woke up. The recording unit can also record the number of times the baby excretes and the condition of the excretion. For example, the recording unit can record how many times the baby excretes and the condition of the excretion. The recording unit can also record the results of measuring the baby's body temperature. For example, the recording unit measures the baby's body temperature and records the results. This makes it possible to record detailed information about the baby. Some or all of the above-mentioned processes in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit inputs information about the baby's diet, sleep, excretion, and body temperature into AI, which then creates a record.

[0033] The ordering unit can search for an appropriate online store and carry out the order process. The ordering unit, for example, orders childcare products such as baby milk and diapers from an online store. For example, the ordering unit searches for an appropriate online store and orders milk and diapers. The ordering unit can also select an online store based on product quality, delivery speed, user reviews, etc. For example, the ordering unit selects an online store with high user reviews and carries out the order process. The ordering unit can also use AI to search for an appropriate online store and automatically carry out the order process. For example, the ordering unit has AI perform the online store search and order process. This allows the online store to be searched and the order process to be carried out automatically. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit has AI perform the online store search and order process.

[0034] The analysis unit can periodically provide childcare advice tailored to the baby's growth. The analysis unit, for example, provides dietary advice tailored to the baby's growth. For example, the analysis unit advises on the content and amount of food according to the baby's age. The analysis unit can also provide sleep advice tailored to the baby's growth. For example, the analysis unit advises on sleep times and how to put the baby to sleep according to the baby's age. The analysis unit can also provide health management advice tailored to the baby's growth. For example, the analysis unit advises on vaccination schedules and health check methods according to the baby's age. This makes it possible to periodically provide childcare advice tailored to the baby's growth. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs childcare advice tailored to the baby's growth into the generation AI, and the generation AI outputs the advice.

[0035] The analysis unit can remind users of vaccination schedules. For example, the analysis unit reminds users of a baby's vaccination schedule. For example, the analysis unit reminds users of the date and location of the baby's vaccination. The analysis unit can also adjust the timing of vaccination reminders. For example, the analysis unit can remind users one week or one day before the vaccination date. This allows users to be reminded of the vaccination schedule. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the vaccination schedule into the generation AI, and the generation AI outputs the reminder.

[0036] The reception unit can analyze the parent's past instructions and consultation history and select the optimal reception method. For example, the reception unit prioritizes receiving instructions and consultations that the parent has frequently made in the past. For example, if the parent frequently "orders milk" in the past, the reception unit uses a generation AI to analyze the parent's past instructions and consultation history and prioritize milk orders. The reception unit can also predict instructions and consultations that will be made during specific time periods based on the parent's past history and adjust the reception method accordingly. For example, if the parent frequently instructs the parent to "take the baby's temperature in the middle of the night," the reception unit can analyze the parent's past history using a generation AI and prioritize temperature measurement instructions in the middle of the night. The reception unit can also suggest the most efficient reception method based on the parent's past history. For example, if the parent frequently "creates a childcare record" in the past, the reception unit can analyze the parent's past history using a generation AI and suggest an efficient way to create the childcare record. This enables efficient response by selecting the optimal reception method based on the parent's past history. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the parent's past instructions and consultation history into the generation AI, which then outputs the optimal reception method.

[0037] When receiving instructions or inquiries, the reception unit can filter them based on the parent's current living situation and areas of interest. For example, the reception unit prioritizes instructions and inquiries that are highly relevant to the parent's current living situation. For example, if a parent says, "I'm worried about my baby's health," the reception unit uses a generating AI to analyze the parent's living situation and prioritize health-related instructions and inquiries. The reception unit can also filter and accept specific instructions and inquiries based on the parent's areas of interest. For example, if a parent says, "I want to know the latest information about childcare," the reception unit can use a generating AI to analyze the parent's areas of interest and prioritize childcare-related instructions and inquiries. The reception unit can also suggest the optimal reception method, taking the parent's living situation and areas of interest into consideration. For example, if a parent says, "I'm busy, so I want to know an easy way to do this," the reception unit can use a generating AI to analyze the parent's living situation and suggest an easy method. This allows for more relevant responses by filtering according to the parent's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's living situation and areas of interest into the generation AI, and the generation AI performs filtering.

[0038] When receiving instructions or inquiries, the reception unit can prioritize receiving highly relevant content by taking into account the parent's geographical location information. For example, if the parent is in a specific area, the reception unit will prioritize receiving instructions or inquiries related to that area. For example, if the parent says, "Please tell me about a nearby hospital," the reception unit will use the generation AI to analyze the parent's geographical location information and prioritize receiving instructions or inquiries related to that area. The reception unit can also suggest optimal instructions or inquiries based on the parent's current location. For example, if the parent says, "Please tell me about the nearest pharmacy from where I am now," the reception unit will use the generation AI to analyze the parent's geographical location information and suggest optimal instructions or inquiries. The reception unit can also filter and accept highly relevant content by taking into account the parent's geographical location information. For example, if the parent says, "I want to play in a nearby park," the reception unit will use the generation AI to analyze the parent's geographical location information and filter and accept highly relevant content. This enables more relevant responses by taking the parent's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's geographical location information to the generation AI, and the generation AI filters out highly relevant content.

[0039] The reception unit can analyze the parent's social media activity when receiving instructions or consultations and accept relevant content. For example, the reception unit analyzes the parent's current interests from their social media activity and prioritizes accepting related instructions and consultations. For example, if a parent says, "I've been sharing a lot of information about child-rearing lately," the reception unit can use generative AI to analyze the parent's social media activity and prioritize accepting instructions and consultations related to child-rearing. The reception unit can also suggest optimal instructions and consultations based on the parent's social media activity. For example, if a parent says, "I've been sharing a lot of information about health lately," the reception unit can use generative AI to analyze the parent's social media activity and suggest instructions and consultations related to health. The reception unit can also filter and accept highly relevant content, taking the parent's social media activity into consideration. For example, if a parent says, "I've been sharing a lot of information about travel lately," the reception unit can use generative AI to analyze the parent's social media activity and filter and accept instructions and consultations related to travel. This enables more relevant responses by analyzing the parent's social media activity. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's social media activity into the generation AI, and the generation AI analyzes the related content.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction or consultation. For example, the analysis unit performs a detailed analysis for highly important instructions or consultations. For example, if a parent says, "I'm worried about my baby's health," the analysis unit uses the generation AI to analyze the importance and perform a detailed analysis. The analysis unit can also perform a concise analysis for less important instructions or consultations. For example, if a parent says, "Please order milk for my baby," the analysis unit uses the generation AI to analyze the importance and perform a concise analysis. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the instruction or consultation. For example, if a parent says, "Please keep a record of my baby's growth," the analysis unit uses the generation AI to analyze the importance and perform the analysis at an appropriate level of detail. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the instruction or consultation. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the importance of instructions or consultations into the generation AI, which then adjusts the level of detail in the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the instruction or consultation. For example, the analysis unit applies a dedicated analysis algorithm to instructions or consultations regarding childcare records. For example, if a parent says, "Please keep a record of my baby's meals," the analysis unit uses the generation AI to apply an analysis algorithm related to childcare records. The analysis unit can also apply a different analysis algorithm to instructions or consultations regarding milk orders. For example, if a parent says, "Please order milk for my baby," the analysis unit uses the generation AI to apply an analysis algorithm related to milk orders. The analysis unit can also select and apply the optimal analysis algorithm depending on the content of the parent's consultation. For example, if a parent says, "I'm worried about my baby's health," the analysis unit uses the generation AI to apply an analysis algorithm related to health. This enables more accurate analysis by applying an analysis algorithm depending on the category of the instruction or consultation. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the category of the instruction or consultation into the generation AI, which then applies the optimal analysis algorithm.

[0042] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of instructions and inquiries. For example, the analysis unit prioritizes analysis of recently submitted instructions and inquiries. For example, if a parent says, "My baby's temperature was high this morning," the analysis unit uses the generation AI to analyze the time of submission and prioritizes analysis of recent instructions and inquiries. The analysis unit can also postpone analysis of older instructions and inquiries. For example, if a parent says, "I would like to order milk for my baby last week," the analysis unit uses the generation AI to analyze the time of submission and prioritize analysis of older instructions and inquiries. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. For example, if a parent says, "I was worried about my baby's health yesterday," the analysis unit uses the generation AI to analyze the time of submission and dynamically adjust the priority. This enables efficient analysis by determining the priority of analysis based on the time of submission. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the timing of submission of instructions and consultations into the generation AI, which then determines the priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of instructions and inquiries. For example, the analysis unit prioritizes analysis of highly relevant instructions and inquiries. For example, if a parent says, "I'm worried about my baby's health," the analysis unit uses the generation AI to analyze the relevance and prioritizes analysis of instructions and inquiries related to health. The analysis unit can also postpone analysis of less relevant instructions and inquiries. For example, if a parent says, "Please order milk for my baby," the analysis unit uses the generation AI to analyze the relevance and postpone analysis of instructions and inquiries related to ordering milk. The analysis unit can also dynamically adjust the order of analysis based on the relevance of instructions and inquiries. For example, if a parent says, "Please keep a record of my baby's growth," the analysis unit uses the generation AI to analyze the relevance and perform the analysis in an appropriate order. Adjusting the order of analysis based on the relevance enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the relevance of instructions and consultations into the generation AI, which then adjusts the order of analysis.

[0044] When recording, the recording unit can analyze the baby's past data and select the optimal recording method. The recording unit, for example, selects the optimal recording method based on the baby's past dietary data. For example, the recording unit analyzes what kind of meals the baby has eaten in the past and selects the optimal recording method. The recording unit can also select the optimal recording method based on the baby's past sleep data. For example, the recording unit analyzes what kind of sleep patterns the baby has had in the past and selects the optimal recording method. The recording unit can also select the optimal recording method based on the baby's past health data. For example, the recording unit analyzes what kind of health conditions the baby has had in the past and selects the optimal recording method. This enables efficient recording by selecting the optimal recording method based on past data. Some or all of the above-mentioned processes in the recording unit may be performed using or without the generation AI. For example, the recording unit inputs the baby's past data into the generation AI, and the generation AI selects the optimal recording method.

[0045] The recording unit can customize the recording method based on the baby's current health condition when recording. The recording unit selects an appropriate recording method based on, for example, the baby's current body temperature. For example, if the baby's body temperature is high, the recording unit selects a method for recording temperature fluctuations in detail. The recording unit can also select an appropriate recording method based on the baby's current eating status. For example, the recording unit selects a method for recording in detail what the baby is eating. The recording unit can also select an appropriate recording method based on the baby's current sleeping status. For example, the recording unit selects a method for recording in detail the baby's sleeping patterns. This makes it possible to provide an optimal recording method according to the baby's current health condition. Some or all of the above-mentioned processing in the recording unit may be performed using or without the generation AI. For example, the recording unit inputs the baby's current health condition into the generation AI, which selects the optimal recording method.

[0046] When recording, the recording unit can select the optimal recording method by taking into account the baby's geographical location information. For example, if the baby is in a specific area, the recording unit selects a recording method related to that area. For example, if the baby says, "Playing in the park," the recording unit analyzes the geographical location information using the generation AI and selects a recording method related to that area. The recording unit can also suggest the optimal recording method based on the baby's current location. For example, if the baby says, "I'm at home," the recording unit analyzes the geographical location information using the generation AI and suggests the optimal recording method. The recording unit can also select a highly relevant recording method by taking into account the baby's geographical location information. For example, if the baby says, "I'm traveling," the recording unit analyzes the geographical location information using the generation AI and selects a highly relevant recording method. This makes it possible to provide the optimal recording method based on the baby's geographical location information. Some or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the baby's geographical location information to the generation AI, which selects the optimal recording method.

[0047] The recording unit can analyze the baby's social media activity during recording and suggest a recording method. For example, the recording unit analyzes the baby's current interests from the baby's social media activity and suggests a related recording method. For example, if the baby says, "I've been sharing a lot of information about childcare recently," the recording unit can analyze the social media activity using a generation AI and suggest a childcare-related recording method. The recording unit can also suggest an optimal recording method based on the baby's social media activity. For example, if the baby says, "I've been sharing a lot of information about health recently," the recording unit can analyze the social media activity using a generation AI and suggest a health-related recording method. The recording unit can also select a highly relevant recording method taking the baby's social media activity into consideration. For example, if the baby says, "I've been sharing a lot of information about travel recently," the recording unit can analyze the social media activity using a generation AI and select a travel-related recording method. This makes it possible to provide an optimal recording method based on the baby's social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using or without the generation AI. For example, the recording unit inputs the baby's social media activity into the generation AI, which then suggests an optimal recording method.

[0048] When placing an order, the ordering unit can analyze past order history and select the optimal ordering method. The ordering unit selects the optimal ordering method based on, for example, the parent's past order history. For example, if the parent frequently "ordered milk" in the past, the ordering unit uses a generation AI to analyze the past order history and select the optimal ordering method. The ordering unit can also predict orders to be made at specific times of the day based on the parent's past order history and suggest the optimal method. For example, if the parent frequently "ordered milk late at night" in the past, the ordering unit can analyze the past history using a generation AI and suggest the optimal ordering method for late at night. The ordering unit can also analyze the parent's past order history and suggest the most efficient ordering method. For example, if the parent frequently "ordered childcare products" in the past, the ordering unit can analyze the past history using a generation AI and suggest an efficient way to order childcare products. This makes it possible to provide the optimal ordering method based on the past order history. Some or all of the above-mentioned processing in the ordering unit may be performed using or without a generation AI. For example, the ordering department inputs the parent's past order history into the generation AI, which then selects the optimal ordering method.

[0049] When placing an order, the order unit can customize the ordering method based on the parent's current living situation. For example, the order unit suggests the optimal ordering method depending on the parent's current living situation. For example, if the parent says, "I'm busy," the order unit uses the generation AI to analyze the parent's living situation and suggest an easy way to order. The order unit can also customize the ordering method taking the parent's current living situation into consideration. For example, if the parent says, "I'm at home," the order unit uses the generation AI to analyze the parent's living situation and suggest an easy way to order from home. The order unit can also select the optimal online store based on the parent's current living situation. For example, if the parent says, "I want to order from a nearby store," the order unit uses the generation AI to analyze the parent's living situation and select a nearby online store. This makes it possible to provide the optimal ordering method based on the parent's current living situation. Some or all of the above-mentioned processing in the order unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the order unit inputs the parent's living situation into the generation AI, which then suggests the optimal ordering method.

[0050] When placing an order, the order unit can select the optimal ordering method by taking into account the parent's geographic location information. For example, if the parent is in a specific area, the order unit selects an ordering method related to that area. For example, if the parent says, "I want to order from a nearby store," the order unit uses the generation AI to analyze the parent's geographic location information and selects an ordering method related to that area. The order unit can also suggest the optimal ordering method based on the parent's current location. For example, if the parent says, "Please tell me the nearest store from where I am now," the order unit uses the generation AI to analyze the parent's geographic location information and suggest the optimal ordering method. The order unit can also select a highly relevant ordering method by taking into account the parent's geographic location information. For example, if the parent says, "I want to order things I need while traveling," the order unit uses the generation AI to analyze the parent's geographic location information and select a highly relevant ordering method. This makes it possible to provide the optimal ordering method based on the parent's geographic location information. Some or all of the above-mentioned processing in the order unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ordering department inputs the parent's geographical location information into the generation AI, which then selects the optimal ordering method.

[0051] When placing an order, the ordering unit can analyze the parent's social media activity and suggest an ordering method. For example, the ordering unit analyzes the parent's current interests from their social media activity and suggests a related ordering method. For example, if the parent says, "I've been sharing a lot of information about childcare recently," the ordering unit can analyze their social media activity using a generation AI and suggest a childcare-related ordering method. The ordering unit can also suggest an optimal ordering method based on the parent's social media activity. For example, if the parent says, "I've been sharing a lot of information about health recently," the ordering unit can analyze their social media activity using a generation AI and suggest a health-related ordering method. The ordering unit can also select a highly relevant ordering method taking the parent's social media activity into consideration. For example, if the parent says, "I've been sharing a lot of information about travel recently," the ordering unit can analyze their social media activity using a generation AI and select a travel-related ordering method. This makes it possible to provide an optimal ordering method based on the parent's social media activity. Some or all of the above-mentioned processing in the ordering unit may be performed using or without a generation AI. For example, the ordering unit inputs the parent's social media activity into a generation AI, which then suggests an optimal ordering method.

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

[0053] When analyzing parental instructions and consultations, the analysis unit can learn the parent's past behavioral patterns and build a predictive model. For example, if a parent has tended to keep childcare records during a specific time period in the past, the analysis unit can automatically suggest childcare records for that time period. Also, if a parent has a tendency to order milk on a specific day of the week, the analysis unit can remind the parent to order milk on that day. Furthermore, if a parent frequently makes a specific inquiry, the analysis unit can prepare a solution to that inquiry in advance and provide it quickly. This makes it possible to provide more efficient childcare support by utilizing a predictive model based on parental behavioral patterns.

[0054] When analyzing the content of a parent's consultation, the analysis unit can refer to the consultation history and solutions of other parents. For example, by referring to the solutions of other parents with similar consultation content, more effective solutions can be proposed. For example, if a parent consults about their baby's severe nighttime crying, the analysis unit analyzes the consultation history of other parents and suggests methods for solving similar problems. The analysis unit can also create a database of solutions to common problems based on the consultation histories of other parents and quickly provide them. Furthermore, the analysis unit can analyze the consultation histories of other parents, find trends and patterns for common problems, and propose solutions based on those trends. In this way, more effective solutions can be provided by utilizing the consultation histories of other parents.

[0055] The recording unit can monitor the baby's health condition in real time and issue an alert if an abnormality is detected. For example, if the baby's body temperature rises suddenly, the recording unit can issue an alert and notify the parents. Similarly, an alert can be issued if the baby's heart rate or respiratory rate indicates an abnormal value. Furthermore, the recording unit is equipped with sensors for monitoring the baby's health condition and can analyze data obtained from these sensors in real time. For example, data obtained from the baby's body temperature sensor and heart rate sensor can be analyzed, and an alert can be issued if an abnormality is detected. This allows the baby's health condition to be monitored in real time and an abnormality to be detected and responded to quickly.

[0056] When analyzing a parent's instructions or consultation, the analysis unit can use natural language processing technology to more accurately understand the parent's intention. For example, if a parent says, "My baby's milk is running low," the analysis unit can use natural language processing technology to analyze the parent's intention and suggest ordering more milk. If a parent says, "My baby's temperature is high," the analysis unit can use natural language processing technology to analyze the parent's intention and suggest measures to lower the temperature. If a parent says, "My baby cries a lot at night," the analysis unit can use natural language processing technology to analyze the parent's intention and suggest ways to reduce the baby's crying. In this way, by utilizing natural language processing technology, the parent's intention can be more accurately understood and appropriate responses can be taken.

[0057] The recording unit can accumulate baby's growth data over the long term and analyze growth trends. For example, by accumulating data on a baby's weight and height over the long term and analyzing growth trends, abnormal growth patterns can be detected early. Data on a baby's diet and sleep can also be accumulated over the long term and analyzed for growth trends. Furthermore, data on a baby's health condition can also be accumulated over the long term and analyzed for growth trends. In this way, by accumulating baby's growth data over the long term and analyzing growth trends, abnormal growth patterns can be detected early and appropriate measures can be taken.

[0058] When accepting instructions or consultations from parents, the reception unit can refer to the parent's past behavioral history. For example, it can prioritize accepting instructions or consultations that the parent has frequently made in the past. For example, if the parent has frequently "ordered milk" in the past, the reception unit will prioritize accepting that instruction. Also, if the parent has frequently "taken the baby's temperature" in the past, it can prioritize accepting that instruction. Furthermore, if the parent has frequently "created a childcare record" in the past, it can prioritize accepting that instruction. This allows for more efficient reception by referring to the parent's past behavioral history.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit accepts instructions or inquiries from parents. Instructions or inquiries from parents include instructions on childcare and health-related inquiries. The reception unit accepts instructions or inquiries from parents via smartphone or smart speaker. Step 2: The analysis unit analyzes the instructions or inquiries received by the reception unit and takes appropriate action. The analysis unit analyzes the parent's instructions or inquiries using text analysis or voice analysis. Analysis can also be performed using generative AI. Step 3: The recording unit creates a childcare record based on the analysis by the analysis unit. The childcare record includes information on the baby's eating, sleeping, excretion, body temperature, etc. The recording unit can automatically create the childcare record using AI. Step 4: The ordering department searches online stores based on the content analyzed by the analysis department and completes the order process. The ordering department orders childcare items such as baby milk and diapers from the online store. The ordering department uses AI to search for the appropriate online store and automatically completes the order process.

[0061] (Example 2) A childcare support system according to an embodiment of the present invention provides support to mothers and fathers facing childcare challenges, offering solutions and keeping necessary records. This system is compatible with multiple devices, including smartphones and smart speakers. Parents simply speak to the system to provide instructions and advice, and the system performs recording, searches, applications, and orders. The system also provides suggestions and prompts to parents as needed. For example, parents may speak to their smartphones or smart speakers to provide instructions or advice. For example, they may issue commands such as "Keep a record of today's childcare" or "Order baby formula." These commands are input into a generation AI, which then analyzes the input and performs appropriate processing. For example, when keeping a childcare record, the generation AI analyzes the parent's statements and automatically creates a childcare record. When ordering formula, the generation AI searches for an appropriate online store and completes the order. Furthermore, the generation AI analyzes the parent's concerns and offers appropriate solutions. For example, when a parent consults the system about a baby's nighttime crying, the generation AI proposes solutions based on past data and expert advice. The system will also make suggestions and calls to parents as needed. For example, it will provide regular childcare advice tailored to the baby's growth and remind them of vaccination schedules. This system reduces the burden on parents in childcare and allows them to raise their children more efficiently. For example, by automatically creating childcare records, parents can save time and focus on raising their children. In addition, the generative AI can suggest appropriate solutions, making it easier for parents to resolve their childcare concerns. In this way, the childcare support system reduces the burden on parents in childcare and allows them to raise their children more efficiently.

[0062] A childcare support system according to an embodiment includes a reception unit, an analysis unit, a recording unit, and an order unit. The reception unit receives instructions or inquiries from parents. Examples of instructions or inquiries from parents include, but are not limited to, childcare instructions and health-related inquiries. The reception unit receives instructions or inquiries from parents via, for example, a smartphone or smart speaker. The analysis unit analyzes the instructions or inquiries received by the reception unit and performs appropriate processing. The analysis unit analyzes the parent's instructions or inquiries using, for example, text analysis or voice analysis. The analysis unit can analyze the parent's instructions or inquiries using a generation AI and perform appropriate processing. The recording unit creates a childcare record based on the content analyzed by the analysis unit. The childcare record includes, for example, information on the baby's diet, sleep, excretion, and body temperature, but is not limited to, examples. The recording unit records, for example, the baby's dietary content and amount, sleep duration, number and status of excretion, and body temperature measurement results. The recording unit can automatically create the childcare record using AI. The ordering unit searches online stores based on the content analyzed by the analysis unit and performs the ordering process. The ordering unit orders, for example, childcare products such as baby milk and diapers from online stores. The ordering unit can use AI to search for appropriate online stores and automatically perform the ordering process. As a result, the childcare support system according to the embodiment can efficiently accept and analyze instructions and inquiries from parents, and automatically record and place orders. Some or all of the above-described processing in the analysis unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the analysis unit inputs instructions and inquiries from parents into the generation AI, and the generation AI outputs the analysis results. Some or all of the above-described processing in the recording unit may be performed using an AI, or may be performed without using an AI. For example, the recording unit causes an AI to create a childcare record. Some or all of the above-described processing in the ordering unit may be performed using an AI, or may be performed without using an AI. For example, the ordering unit causes an AI to search online stores and perform the ordering process.

[0063] The analysis unit can analyze the parent's consultation content and present an appropriate solution. The analysis unit, for example, analyzes the parent's consultation content using text analysis or voice analysis. For example, if a parent complains that "my baby cries a lot at night," the analysis unit uses the generation AI to present a solution based on past data and expert advice. The analysis unit can analyze the parent's consultation content and present an appropriate solution using the generation AI. For example, the analysis unit inputs a prompt to the generation AI, such as "Please tell me how to solve my baby's night crying problem," and the generation AI outputs a solution based on past data and expert advice. This makes it possible to present an appropriate solution based on the parent's consultation content. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the parent's consultation content to the generation AI, and the generation AI outputs a solution.

[0064] The analysis unit can propose a solution based on past data or expert advice. The analysis unit, for example, proposes a solution using past data. For example, the analysis unit can propose a solution based on past consultation history and statistical data. The analysis unit can also propose a solution based on expert advice. For example, the analysis unit proposes a solution based on a doctor's opinion or guidelines from a childcare expert. The analysis unit can use a generation AI to propose a solution based on past data and expert advice. For example, the analysis unit inputs past consultation history and statistical data into the generation AI, which then outputs a solution. This makes it possible to propose a more accurate solution based on past data and expert advice. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs past data and expert advice into the generation AI, which then outputs a solution.

[0065] The recording unit can record information about the baby's diet, sleep, excretion, and body temperature. For example, the recording unit records the content and amount of the baby's diet. For example, the recording unit records what the baby ate and how much it ate. The recording unit can also record the baby's sleep time. For example, the recording unit records what time the baby went to bed and what time the baby woke up. The recording unit can also record the number of times the baby excretes and the condition of the excretion. For example, the recording unit can record how many times the baby excretes and the condition of the excretion. The recording unit can also record the results of measuring the baby's body temperature. For example, the recording unit measures the baby's body temperature and records the results. This makes it possible to record detailed information about the baby. Some or all of the above-mentioned processes in the recording unit may be performed using AI, or may be performed without using AI. For example, the recording unit inputs information about the baby's diet, sleep, excretion, and body temperature into AI, which then creates a record.

[0066] The ordering unit can search for an appropriate online store and carry out the order process. The ordering unit, for example, orders childcare products such as baby milk and diapers from an online store. For example, the ordering unit searches for an appropriate online store and orders milk and diapers. The ordering unit can also select an online store based on product quality, delivery speed, user reviews, etc. For example, the ordering unit selects an online store with high user reviews and carries out the order process. The ordering unit can also use AI to search for an appropriate online store and automatically carry out the order process. For example, the ordering unit has AI perform the online store search and order process. This allows the online store to be searched and the order process to be carried out automatically. Some or all of the above-mentioned processing in the ordering unit may be performed using AI, or may be performed without using AI. For example, the ordering unit has AI perform the online store search and order process.

[0067] The analysis unit can periodically provide childcare advice tailored to the baby's growth. The analysis unit, for example, provides dietary advice tailored to the baby's growth. For example, the analysis unit advises on the content and amount of food according to the baby's age. The analysis unit can also provide sleep advice tailored to the baby's growth. For example, the analysis unit advises on sleep times and how to put the baby to sleep according to the baby's age. The analysis unit can also provide health management advice tailored to the baby's growth. For example, the analysis unit advises on vaccination schedules and health check methods according to the baby's age. This makes it possible to periodically provide childcare advice tailored to the baby's growth. Some or all of the above-described processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs childcare advice tailored to the baby's growth into the generation AI, and the generation AI outputs the advice.

[0068] The analysis unit can remind users of vaccination schedules. For example, the analysis unit reminds users of a baby's vaccination schedule. For example, the analysis unit reminds users of the date and location of the baby's vaccination. The analysis unit can also adjust the timing of vaccination reminders. For example, the analysis unit can remind users one week or one day before the vaccination date. This allows users to be reminded of the vaccination schedule. Some or all of the above-mentioned processing in the analysis unit may be performed using or without the generation AI. For example, the analysis unit inputs the vaccination schedule into the generation AI, and the generation AI outputs the reminder.

[0069] The childcare support system further includes a reception unit that estimates the parent's emotions and adjusts the timing of receiving instructions and consultations based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit delays the timing of receiving instructions and consultations to provide time for the parent to relax. For example, if the parent says, "I'm tired," the reception unit uses a generation AI to analyze the parent's emotions and provide time for the parent to relax. Furthermore, if the parent is relaxed, the reception unit can immediately accept instructions and consultations and respond quickly. For example, if the parent says, "I'm fine now," the reception unit uses a generation AI to analyze the parent's emotions and immediately accept instructions and consultations. Furthermore, if the parent is in a hurry, the reception unit can prioritize instructions and consultations and start processing them quickly. For example, if the parent says, "I'm in a hurry," the reception unit uses a generation AI to analyze the parent's emotions and prioritize instructions and consultations. This allows for more appropriate responses by adjusting the timing of receiving instructions and consultations based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's emotions into the generation AI, and the generation AI analyzes the emotions.

[0070] The reception unit can analyze the parent's past instructions and consultation history and select the optimal reception method. For example, the reception unit prioritizes receiving instructions and consultations that the parent has frequently made in the past. For example, if the parent frequently "orders milk" in the past, the reception unit uses a generation AI to analyze the parent's past instructions and consultation history and prioritize milk orders. The reception unit can also predict instructions and consultations that will be made during specific time periods based on the parent's past history and adjust the reception method accordingly. For example, if the parent frequently instructs the parent to "take the baby's temperature in the middle of the night," the reception unit can analyze the parent's past history using a generation AI and prioritize temperature measurement instructions in the middle of the night. The reception unit can also suggest the most efficient reception method based on the parent's past history. For example, if the parent frequently "creates a childcare record" in the past, the reception unit can analyze the parent's past history using a generation AI and suggest an efficient way to create the childcare record. This enables efficient response by selecting the optimal reception method based on the parent's past history. Some or all of the above-described processing in the reception unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the reception unit inputs the parent's past instructions and consultation history into the generation AI, which then outputs the optimal reception method.

[0071] When receiving instructions or inquiries, the reception unit can filter them based on the parent's current living situation and areas of interest. For example, the reception unit prioritizes instructions and inquiries that are highly relevant to the parent's current living situation. For example, if a parent says, "I'm worried about my baby's health," the reception unit uses a generation AI to analyze the parent's living situation and prioritize health-related instructions and inquiries. The reception unit can also filter and accept specific instructions and inquiries based on the parent's areas of interest. For example, if a parent says, "I want to know the latest information about childcare," the reception unit can use a generation AI to analyze the parent's areas of interest and prioritize childcare-related instructions and inquiries. The reception unit can also suggest the optimal reception method, taking the parent's living situation and areas of interest into consideration. For example, if a parent says, "I'm busy, so I want to know an easy way to do this," the reception unit can use a generation AI to analyze the parent's living situation and suggest an easy method. This allows for more relevant responses by filtering according to the parent's living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's living situation and areas of interest into the generation AI, and the generation AI performs filtering.

[0072] The reception unit can estimate the parent's emotions and determine the priority of instructions and inquiries to be received based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit will prioritize instructions and inquiries of higher importance. For example, if the parent says, "I'm tired," the reception unit will use the generation AI to analyze the parent's emotions and prioritize instructions and inquiries of higher importance. In addition, if the parent is relaxed, the reception unit can accept instructions and inquiries with normal priority. For example, if the parent says, "I'm fine now," the reception unit will use the generation AI to analyze the parent's emotions and prioritize instructions and inquiries of higher importance. In addition, if the parent is in a hurry, the reception unit can prioritize instructions and inquiries of higher urgency. For example, if the parent says, "I'm in a hurry," the reception unit will use the generation AI to analyze the parent's emotions and prioritize instructions and inquiries of higher urgency. This allows for more appropriate responses by determining priorities according to the parent's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI may 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 may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's emotion into the generation AI, and the generation AI analyzes the emotion.

[0073] When receiving instructions or inquiries, the reception unit can prioritize receiving highly relevant content by taking into account the parent's geographical location information. For example, if the parent is in a specific area, the reception unit will prioritize receiving instructions or inquiries related to that area. For example, if the parent says, "Please tell me about a nearby hospital," the reception unit will use the generation AI to analyze the parent's geographical location information and prioritize receiving instructions or inquiries related to that area. The reception unit can also suggest optimal instructions or inquiries based on the parent's current location. For example, if the parent says, "Please tell me about the nearest pharmacy from where I am now," the reception unit will use the generation AI to analyze the parent's geographical location information and suggest optimal instructions or inquiries. The reception unit can also filter and accept highly relevant content by taking into account the parent's geographical location information. For example, if the parent says, "I want to play in a nearby park," the reception unit will use the generation AI to analyze the parent's geographical location information and filter and accept highly relevant content. This enables more relevant responses by taking the parent's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's geographical location information to the generation AI, and the generation AI filters out highly relevant content.

[0074] The reception unit can analyze the parent's social media activity when receiving instructions or consultations and accept relevant content. For example, the reception unit analyzes the parent's current interests from their social media activity and prioritizes accepting related instructions and consultations. For example, if a parent says, "I've been sharing a lot of information about child-rearing lately," the reception unit can use generative AI to analyze the parent's social media activity and prioritize accepting instructions and consultations related to child-rearing. The reception unit can also suggest optimal instructions and consultations based on the parent's social media activity. For example, if a parent says, "I've been sharing a lot of information about health lately," the reception unit can use generative AI to analyze the parent's social media activity and suggest instructions and consultations related to health. The reception unit can also filter and accept highly relevant content, taking the parent's social media activity into consideration. For example, if a parent says, "I've been sharing a lot of information about travel lately," the reception unit can use generative AI to analyze the parent's social media activity and filter and accept instructions and consultations related to travel. This enables more relevant responses by analyzing the parent's social media activity. Some or all of the above-described processing in the reception unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the reception unit inputs the parent's social media activity into the generation AI, and the generation AI analyzes the related content.

[0075] The analysis unit can estimate the parent's emotions and adjust the analysis expression based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit uses a simple and easy-to-understand expression. For example, if the parent says, "I'm tired," the analysis unit uses the generation AI to analyze the parent's emotions and uses a simple and easy-to-understand expression. Alternatively, if the parent is relaxed, the analysis unit can use an expression that includes more detailed information. For example, if the parent says, "I'm fine now," the analysis unit uses the generation AI to analyze the parent's emotions and uses an expression that includes more detailed information. Alternatively, if the parent is in a hurry, the analysis unit can use a concise expression that gets to the point. For example, if the parent says, "I'm in a hurry," the analysis unit uses the generation AI to analyze the parent's emotions and uses a concise expression that gets to the point. This allows for more appropriate information to be provided by adjusting the analysis expression based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the parent's emotions into the generation AI, and the generation AI analyzes the emotions.

[0076] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the instruction or consultation. For example, the analysis unit performs a detailed analysis for highly important instructions or consultations. For example, if a parent says, "I'm worried about my baby's health," the analysis unit uses the generation AI to analyze the importance and perform a detailed analysis. The analysis unit can also perform a concise analysis for less important instructions or consultations. For example, if a parent says, "Please order milk for my baby," the analysis unit uses the generation AI to analyze the importance and perform a concise analysis. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the instruction or consultation. For example, if a parent says, "Please keep a record of my baby's growth," the analysis unit uses the generation AI to analyze the importance and perform the analysis at an appropriate level of detail. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the instruction or consultation. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the importance of instructions or consultations into the generation AI, which then adjusts the level of detail in the analysis.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the instruction or consultation. For example, the analysis unit applies a dedicated analysis algorithm to instructions or consultations regarding childcare records. For example, if a parent says, "Please keep a record of my baby's meals," the analysis unit uses the generation AI to apply an analysis algorithm related to childcare records. The analysis unit can also apply a different analysis algorithm to instructions or consultations regarding milk orders. For example, if a parent says, "Please order milk for my baby," the analysis unit uses the generation AI to apply an analysis algorithm related to milk orders. The analysis unit can also select and apply the optimal analysis algorithm depending on the content of the parent's consultation. For example, if a parent says, "I'm worried about my baby's health," the analysis unit uses the generation AI to apply an analysis algorithm related to health. This enables more accurate analysis by applying an analysis algorithm depending on the category of the instruction or consultation. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the category of the instruction or consultation into the generation AI, which then applies the optimal analysis algorithm.

[0078] The analysis unit can estimate the parent's emotions and adjust the length of the analysis based on the estimated parent's emotions. For example, if the parent is stressed, the analysis unit performs a short, concise analysis. For example, if the parent says, "I'm tired," the analysis unit uses the generation AI to analyze the parent's emotions and perform a short, concise analysis. The analysis unit can also perform a detailed analysis if the parent is relaxed. For example, if the parent says, "I'm fine now," the analysis unit uses the generation AI to analyze the parent's emotions and perform a detailed analysis. The analysis unit can also perform a quick analysis and provide the results if the parent is in a hurry. For example, if the parent says, "I'm in a hurry," the analysis unit uses the generation AI to analyze the parent's emotions, perform the analysis quickly, and provide the results. This allows for adjusting the length of the analysis according to the parent's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the parent's emotions into the generation AI, and the generation AI analyzes the emotions.

[0079] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of instructions and inquiries. For example, the analysis unit prioritizes analysis of recently submitted instructions and inquiries. For example, if a parent says, "My baby's temperature was high this morning," the analysis unit uses the generation AI to analyze the time of submission and prioritizes analysis of recent instructions and inquiries. The analysis unit can also postpone analysis of older instructions and inquiries. For example, if a parent says, "I would like to order milk for my baby last week," the analysis unit uses the generation AI to analyze the time of submission and prioritize analysis of older instructions and inquiries. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. For example, if a parent says, "I was worried about my baby's health yesterday," the analysis unit uses the generation AI to analyze the time of submission and dynamically adjust the priority. This enables efficient analysis by determining the priority of analysis based on the time of submission. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the timing of submission of instructions and consultations into the generation AI, which then determines the priority.

[0080] During analysis, the analysis unit can adjust the order of analysis based on the relevance of instructions and inquiries. For example, the analysis unit prioritizes analysis of highly relevant instructions and inquiries. For example, if a parent says, "I'm worried about my baby's health," the analysis unit uses the generation AI to analyze the relevance and prioritizes analysis of instructions and inquiries related to health. The analysis unit can also postpone analysis of less relevant instructions and inquiries. For example, if a parent says, "Please order milk for my baby," the analysis unit uses the generation AI to analyze the relevance and postpone analysis of instructions and inquiries related to ordering milk. The analysis unit can also dynamically adjust the order of analysis based on the relevance of instructions and inquiries. For example, if a parent says, "Please keep a record of my baby's growth," the analysis unit uses the generation AI to analyze the relevance and perform the analysis in an appropriate order. Adjusting the order of analysis based on the relevance enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit inputs the relevance of instructions and consultations into the generation AI, which then adjusts the order of analysis.

[0081] The recording unit can estimate the parent's emotions and adjust the recording method based on the estimated parent's emotions. For example, if the parent is stressed, the recording unit uses a simple and easy-to-understand recording method. For example, if the parent says, "I'm tired," the recording unit uses a generation AI to analyze the parent's emotions and uses a simple and easy-to-understand recording method. Furthermore, if the parent is relaxed, the recording unit can use a recording method that includes more detailed information. For example, if the parent says, "I'm fine now," the recording unit can use a generation AI to analyze the parent's emotions and use a recording method that includes more detailed information. Furthermore, if the parent is in a hurry, the recording unit can use a quick recording method. For example, if the parent says, "I'm in a hurry," the recording unit uses a generation AI to analyze the parent's emotions and uses a quick recording method. This allows for more appropriate recording by adjusting the recording method according to the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the parent's emotions into the generation AI, and the generation AI analyzes the emotions.

[0082] When recording, the recording unit can analyze the baby's past data and select the optimal recording method. The recording unit, for example, selects the optimal recording method based on the baby's past dietary data. For example, the recording unit analyzes what kind of meals the baby has eaten in the past and selects the optimal recording method. The recording unit can also select the optimal recording method based on the baby's past sleep data. For example, the recording unit analyzes what kind of sleep patterns the baby has had in the past and selects the optimal recording method. The recording unit can also select the optimal recording method based on the baby's past health data. For example, the recording unit analyzes what kind of health conditions the baby has had in the past and selects the optimal recording method. This enables efficient recording by selecting the optimal recording method based on past data. Some or all of the above-mentioned processes in the recording unit may be performed using or without the generation AI. For example, the recording unit inputs the baby's past data into the generation AI, and the generation AI selects the optimal recording method.

[0083] The recording unit can customize the recording method based on the baby's current health condition when recording. The recording unit selects an appropriate recording method based on, for example, the baby's current body temperature. For example, if the baby's body temperature is high, the recording unit selects a method for recording temperature fluctuations in detail. The recording unit can also select an appropriate recording method based on the baby's current eating status. For example, the recording unit selects a method for recording in detail what the baby is eating. The recording unit can also select an appropriate recording method based on the baby's current sleeping status. For example, the recording unit selects a method for recording in detail the baby's sleeping patterns. This makes it possible to provide an optimal recording method according to the baby's current health condition. Some or all of the above-mentioned processing in the recording unit may be performed using or without the generation AI. For example, the recording unit inputs the baby's current health condition into the generation AI, which selects the optimal recording method.

[0084] The recording unit can estimate the parent's emotions and determine the recording priority based on the estimated parent's emotions. For example, if the parent is feeling stressed, the recording unit prioritizes recordings of higher importance. For example, if the parent says, "I'm tired," the recording unit analyzes the parent's emotions using the generation AI and prioritizes recordings of higher importance. Furthermore, if the parent is relaxed, the recording unit can record at normal priority. For example, if the parent says, "I'm fine now," the recording unit can analyze the parent's emotions using the generation AI and prioritize recordings of higher importance. Furthermore, if the parent is in a hurry, the recording unit can prioritize recordings of higher urgency. For example, if the parent says, "I'm in a hurry," the recording unit can analyze the parent's emotions using the generation AI and prioritize recordings of higher urgency. This allows for more appropriate recording by determining the recording priority based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the parent's emotions into the generation AI, and the generation AI analyzes the emotions.

[0085] When recording, the recording unit can select the optimal recording method by taking into account the baby's geographical location information. For example, if the baby is in a specific area, the recording unit selects a recording method related to that area. For example, if the baby says, "Playing in the park," the recording unit analyzes the geographical location information using the generation AI and selects a recording method related to that area. The recording unit can also suggest the optimal recording method based on the baby's current location. For example, if the baby says, "I'm at home," the recording unit analyzes the geographical location information using the generation AI and suggests the optimal recording method. The recording unit can also select a highly relevant recording method by taking into account the baby's geographical location information. For example, if the baby says, "I'm traveling," the recording unit analyzes the geographical location information using the generation AI and selects a highly relevant recording method. This makes it possible to provide the optimal recording method based on the baby's geographical location information. Some or all of the above-mentioned processing in the recording unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the recording unit inputs the baby's geographical location information to the generation AI, which selects the optimal recording method.

[0086] The recording unit can analyze the baby's social media activity during recording and suggest a recording method. For example, the recording unit analyzes the baby's current interests from the baby's social media activity and suggests a related recording method. For example, if the baby says, "I've been sharing a lot of information about childcare recently," the recording unit can analyze the social media activity using a generation AI and suggest a childcare-related recording method. The recording unit can also suggest an optimal recording method based on the baby's social media activity. For example, if the baby says, "I've been sharing a lot of information about health recently," the recording unit can analyze the social media activity using a generation AI and suggest a health-related recording method. The recording unit can also select a highly relevant recording method taking the baby's social media activity into consideration. For example, if the baby says, "I've been sharing a lot of information about travel recently," the recording unit can analyze the social media activity using a generation AI and select a travel-related recording method. This makes it possible to provide an optimal recording method based on the baby's social media activity. Some or all of the above-mentioned processing in the recording unit may be performed using or without the generation AI. For example, the recording unit inputs the baby's social media activity into the generation AI, which then suggests an optimal recording method.

[0087] The ordering unit can estimate the parent's emotions and adjust the ordering method based on the estimated parent's emotions. For example, if the parent is stressed, the ordering unit uses a simple and easy-to-understand ordering method. For example, if the parent says, "I'm tired," the ordering unit uses the generation AI to analyze the parent's emotions and uses a simple and easy-to-understand ordering method. Furthermore, if the parent is relaxed, the ordering unit can use an ordering method that includes detailed information. For example, if the parent says, "I'm fine now," the ordering unit can use the generation AI to analyze the parent's emotions and use a method that includes detailed information. Furthermore, if the parent is in a hurry, the ordering unit can use a method to quickly place an order. For example, if the parent says, "I'm in a hurry," the ordering unit uses the generation AI to analyze the parent's emotions and use a method to quickly place an order. This allows for a more appropriate order by adjusting the ordering method according to the parent'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, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the order unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the order unit inputs the parent's emotions into the generation AI, and the generation AI analyzes the emotions.

[0088] When placing an order, the ordering unit can analyze past order history and select the optimal ordering method. The ordering unit selects the optimal ordering method based on, for example, the parent's past order history. For example, if the parent frequently "ordered milk" in the past, the ordering unit uses a generation AI to analyze the past order history and select the optimal ordering method. The ordering unit can also predict orders to be made at specific times of the day based on the parent's past order history and suggest the optimal method. For example, if the parent frequently "ordered milk late at night" in the past, the ordering unit can analyze the past history using a generation AI and suggest the optimal ordering method for late at night. The ordering unit can also analyze the parent's past order history and suggest the most efficient ordering method. For example, if the parent frequently "ordered childcare products" in the past, the ordering unit can analyze the past history using a generation AI and suggest an efficient way to order childcare products. This makes it possible to provide the optimal ordering method based on the past order history. Some or all of the above-mentioned processing in the ordering unit may be performed using or without a generation AI. For example, the ordering department inputs the parent's past order history into the generation AI, which then selects the optimal ordering method.

[0089] When placing an order, the order unit can customize the ordering method based on the parent's current living situation. For example, the order unit suggests the optimal ordering method depending on the parent's current living situation. For example, if the parent says, "I'm busy," the order unit uses the generation AI to analyze the parent's living situation and suggest an easy way to order. The order unit can also customize the ordering method taking the parent's current living situation into consideration. For example, if the parent says, "I'm at home," the order unit uses the generation AI to analyze the parent's living situation and suggest an easy way to order from home. The order unit can also select the optimal online store based on the parent's current living situation. For example, if the parent says, "I want to order from a nearby store," the order unit uses the generation AI to analyze the parent's living situation and select a nearby online store. This makes it possible to provide the optimal ordering method based on the parent's current living situation. Some or all of the above-mentioned processing in the order unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the order unit inputs the parent's living situation into the generation AI, which then suggests the optimal ordering method.

[0090] The order unit can estimate the parent's emotions and prioritize orders based on the estimated parent's emotions. For example, if the parent is feeling stressed, the order unit prioritizes orders with higher importance. For example, if the parent says, "I'm tired," the order unit uses the generation AI to analyze the parent's emotions and prioritize orders with higher importance. Alternatively, if the parent is relaxed, the order unit can process orders with normal priority. For example, if the parent says, "I'm fine now," the order unit can analyze the parent's emotions using the generation AI and prioritize orders with normal priority. Alternatively, if the parent is in a hurry, the order unit can prioritize orders with higher urgency. For example, if the parent says, "I'm in a hurry," the order unit can analyze the parent's emotions using the generation AI and prioritize orders with higher urgency. This allows for more appropriate orders by prioritizing orders based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the order unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the order unit inputs the parent's emotions into the generation AI, and the generation AI analyzes the emotions.

[0091] When placing an order, the order unit can select the optimal ordering method by taking into account the parent's geographic location information. For example, if the parent is in a specific area, the order unit selects an ordering method related to that area. For example, if the parent says, "I want to order from a nearby store," the order unit uses the generation AI to analyze the parent's geographic location information and selects an ordering method related to that area. The order unit can also suggest the optimal ordering method based on the parent's current location. For example, if the parent says, "Please tell me the nearest store from where I am now," the order unit uses the generation AI to analyze the parent's geographic location information and suggest the optimal ordering method. The order unit can also select a highly relevant ordering method by taking into account the parent's geographic location information. For example, if the parent says, "I want to order things I need while traveling," the order unit uses the generation AI to analyze the parent's geographic location information and select a highly relevant ordering method. This makes it possible to provide the optimal ordering method based on the parent's geographic location information. Some or all of the above-mentioned processing in the order unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the ordering department inputs the parent's geographical location information into the generation AI, which then selects the optimal ordering method.

[0092] When placing an order, the ordering unit can analyze the parent's social media activity and suggest an ordering method. For example, the ordering unit analyzes the parent's current interests from their social media activity and suggests a related ordering method. For example, if the parent says, "I've been sharing a lot of information about childcare recently," the ordering unit can analyze their social media activity using a generation AI and suggest a childcare-related ordering method. The ordering unit can also suggest an optimal ordering method based on the parent's social media activity. For example, if the parent says, "I've been sharing a lot of information about health recently," the ordering unit can analyze their social media activity using a generation AI and suggest a health-related ordering method. The ordering unit can also select a highly relevant ordering method taking the parent's social media activity into consideration. For example, if the parent says, "I've been sharing a lot of information about travel recently," the ordering unit can analyze their social media activity using a generation AI and select a travel-related ordering method. This makes it possible to provide an optimal ordering method based on the parent's social media activity. Some or all of the above-mentioned processing in the ordering unit may be performed using or without a generation AI. For example, the ordering unit inputs the parent's social media activity into a generation AI, which then suggests an optimal ordering method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, recording unit, and order unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives instructions and inquiries from the parent. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the instructions and inquiries from the parent. The recording unit is implemented by the specific processing unit 290 of the data processing device 12 and creates a childcare record. The order unit is implemented by the specific processing unit 290 of the data processing device 12 and searches online stores and processes orders. Furthermore, the reception unit has a function of estimating the parent's emotions and adjusting the timing of receiving instructions and inquiries based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit uses a generative AI to analyze the parent's emotions and provide them with time to relax. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, recording unit, and order unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives instructions and inquiries from the parent. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the instructions and inquiries from the parent. The recording unit is implemented by the specific processing unit 290 of the data processing device 12 and creates a childcare record. The order unit is implemented by the specific processing unit 290 of the data processing device 12 and searches online stores and processes orders. Furthermore, the reception unit has a function of estimating the parent's emotions and adjusting the timing of receiving instructions and inquiries based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit uses a generative AI to analyze the parent's emotions and provide them with time to relax. === Hard Collateral 1-3 === Each of the multiple elements, including the reception unit, analysis unit, recording unit, and order unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives instructions and inquiries from the parent. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the instructions and inquiries from the parent. The recording unit is implemented by the specific processing unit 290 of the data processing device 12 and creates a childcare record. The order unit is implemented by the specific processing unit 290 of the data processing device 12 and searches online stores and processes orders. Furthermore, the reception unit has a function of estimating the parent's emotions and adjusting the timing of receiving instructions and inquiries based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit uses a generation AI to analyze the parent's emotions and provide them with time to relax. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, recording unit, and order 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 control unit 46A of the robot 414 and receives instructions and inquiries from the parent. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the instructions and inquiries from the parent. The recording unit is realized by the specific processing unit 290 of the data processing device 12 and creates a childcare record. The order unit is realized by the specific processing unit 290 of the data processing device 12 and searches online stores and performs ordering procedures. Furthermore, the reception unit has a function of estimating the parent's emotions and adjusting the timing of receiving instructions and inquiries based on the estimated parent's emotions. For example, if the parent is feeling stressed, the reception unit uses a generative AI to analyze the parent's emotions and provide them with time to relax.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] When analyzing parental instructions and consultations, the analysis unit can learn the parent's past behavioral patterns and build a predictive model. For example, if a parent has tended to keep childcare records during a specific time period in the past, the analysis unit can automatically suggest childcare records for that time period. Also, if a parent has a tendency to order milk on a specific day of the week, the analysis unit can remind the parent to order milk on that day. Furthermore, if a parent frequently makes a specific inquiry, the analysis unit can prepare a solution to that inquiry in advance and provide it quickly. This makes it possible to provide more efficient childcare support by utilizing a predictive model based on parental behavioral patterns.

[0095] When analyzing the parent's consultation content, the analysis unit can use the emotion estimation function to estimate the parent's emotions and customize a solution based on the estimated emotions. For example, if the parent is feeling stressed, the analysis unit can suggest a solution that will help them relax. For example, if the parent says, "I'm tired," the analysis unit can use the generation AI to analyze the parent's emotions and suggest a way to help them relax. If the parent is relaxed, a detailed solution can also be provided. For example, if the parent says, "I'm fine now," the analysis unit can use the generation AI to analyze the parent's emotions and suggest a detailed solution. Furthermore, if the parent is in a hurry, a solution that can be implemented quickly can be suggested. For example, if the parent says, "I'm in a hurry," the analysis unit can use the generation AI to analyze the parent's emotions and suggest a solution that can be implemented quickly. This enables more appropriate responses by providing solutions that correspond to the parent's emotions.

[0096] When analyzing the content of a parent's consultation, the analysis unit can refer to the consultation history and solutions of other parents. For example, by referring to the solutions of other parents with similar consultation content, more effective solutions can be proposed. For example, if a parent consults about their baby's severe nighttime crying, the analysis unit analyzes the consultation history of other parents and suggests methods for solving similar problems. The analysis unit can also create a database of solutions to common problems based on the consultation histories of other parents and quickly provide them. Furthermore, the analysis unit can analyze the consultation histories of other parents, find trends and patterns for common problems, and propose solutions based on those trends. In this way, more effective solutions can be provided by utilizing the consultation histories of other parents.

[0097] The recording unit can monitor the baby's health condition in real time and issue an alert if an abnormality is detected. For example, if the baby's body temperature rises suddenly, the recording unit can issue an alert and notify the parents. Similarly, an alert can be issued if the baby's heart rate or respiratory rate indicates an abnormal value. Furthermore, the recording unit is equipped with sensors for monitoring the baby's health condition and can analyze data obtained from these sensors in real time. For example, data obtained from the baby's body temperature sensor and heart rate sensor can be analyzed, and an alert can be issued if an abnormality is detected. This allows the baby's health condition to be monitored in real time and an abnormality to be detected and responded to quickly.

[0098] The ordering department can estimate the parent's emotions and suggest order contents based on the estimated emotions. For example, if the parent is feeling stressed, it can suggest products that will help them relax. For example, if the parent says, "I'm tired," the ordering department can use the generation AI to analyze the parent's emotions and suggest products that will help them relax. Also, if the parent is relaxed, it can suggest products that are useful for childcare. For example, if the parent says, "I'm fine now," the ordering department can use the generation AI to analyze the parent's emotions and suggest products that are useful for childcare. Furthermore, if the parent is in a hurry, it can suggest products that can be obtained quickly. For example, if the parent says, "I'm in a hurry," the ordering department can use the generation AI to analyze the parent's emotions and suggest products that can be obtained quickly. This allows for more appropriate orders by suggesting products that correspond to the parent's emotions.

[0099] When analyzing a parent's instructions or consultation, the analysis unit can use natural language processing technology to more accurately understand the parent's intention. For example, if a parent says, "My baby's milk is running low," the analysis unit can use natural language processing technology to analyze the parent's intention and suggest ordering more milk. If a parent says, "My baby's temperature is high," the analysis unit can use natural language processing technology to analyze the parent's intention and suggest measures to lower the temperature. If a parent says, "My baby cries a lot at night," the analysis unit can use natural language processing technology to analyze the parent's intention and suggest ways to reduce the baby's crying. In this way, by utilizing natural language processing technology, the parent's intention can be more accurately understood and appropriate responses can be taken.

[0100] The analysis unit can estimate the parent's emotions and adjust the content of the parenting advice based on the estimated emotions. For example, if the parent is feeling stressed, the analysis unit can provide simple, easy-to-follow advice. For example, if the parent says, "I'm tired," the analysis unit uses the generation AI to analyze the parent's emotions and provide simple, easy-to-follow advice. Furthermore, if the parent is relaxed, the analysis unit can provide detailed advice. For example, if the parent says, "I'm fine now," the analysis unit can use the generation AI to analyze the parent's emotions and provide detailed advice. Furthermore, if the parent is in a hurry, the analysis unit can provide advice that can be implemented quickly. For example, if the parent says, "I'm in a hurry," the analysis unit uses the generation AI to analyze the parent's emotions and provide advice that can be implemented quickly. This enables more appropriate parenting advice to be provided based on the parent's emotions.

[0101] The recording unit can accumulate baby's growth data over the long term and analyze growth trends. For example, by accumulating data on a baby's weight and height over the long term and analyzing growth trends, abnormal growth patterns can be detected early. Data on a baby's diet and sleep can also be accumulated over the long term and analyzed for growth trends. Furthermore, data on a baby's health condition can also be accumulated over the long term and analyzed for growth trends. In this way, by accumulating baby's growth data over the long term and analyzing growth trends, abnormal growth patterns can be detected early and appropriate measures can be taken.

[0102] The reception unit can estimate the parent's emotions and adjust the reception method based on the estimated emotions. For example, if the parent is feeling stressed, the reception procedure can be simplified. For example, if the parent says, "I'm tired," the reception unit uses the generation AI to analyze the parent's emotions and simplify the reception procedure. Also, if the parent is relaxed, the normal reception procedure can be carried out. For example, if the parent says, "I'm fine now," the reception unit uses the generation AI to analyze the parent's emotions and carry out the normal reception procedure. Furthermore, if the parent is in a hurry, the reception can be expedited. For example, if the parent says, "I'm in a hurry," the reception unit uses the generation AI to analyze the parent's emotions and carry out the reception quickly. This enables more appropriate responses by providing a reception method that suits the parent's emotions.

[0103] When accepting instructions or consultations from parents, the reception unit can refer to the parent's past behavioral history. For example, it can prioritize accepting instructions or consultations that the parent has frequently made in the past. For example, if the parent has frequently "ordered milk" in the past, the reception unit will prioritize accepting that instruction. Also, if the parent has frequently "taken the baby's temperature" in the past, it can prioritize accepting that instruction. Furthermore, if the parent has frequently "created a childcare record" in the past, it can prioritize accepting that instruction. This allows for more efficient reception by referring to the parent's past behavioral history.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit accepts instructions or inquiries from parents. Instructions or inquiries from parents include instructions on childcare and health-related inquiries. The reception unit accepts instructions or inquiries from parents via smartphone or smart speaker. Step 2: The analysis unit analyzes the instructions or inquiries received by the reception unit and takes appropriate action. The analysis unit analyzes the parent's instructions or inquiries using text analysis or voice analysis. Analysis can also be performed using generative AI. Step 3: The recording unit creates a childcare record based on the analysis by the analysis unit. The childcare record includes information on the baby's eating, sleeping, excretion, body temperature, etc. The recording unit can automatically create the childcare record using AI. Step 4: The ordering department searches online stores based on the content analyzed by the analysis department and completes the order process. The ordering department orders childcare items such as baby milk and diapers from the online store. The ordering department uses AI to search for the appropriate online store and automatically completes the order process.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 desk that accepts instructions or inquiries from parents; an analysis unit that analyzes and processes the instruction or consultation received by the reception unit; a recording unit that creates a childcare record based on the content analyzed by the analysis unit; an ordering unit that searches an online store based on the content analyzed by the analysis unit and performs an ordering procedure. A system characterized by:

2. The analysis unit Analyze the parent's concerns and propose appropriate solutions 2. The system of claim 1.

3. The analysis unit Propose solutions based on historical data or expert advice 2. The system of claim 1.

4. The recording unit Record your baby's feeding, sleeping, toileting and temperature information 2. The system of claim 1.

5. The ordering unit Find the right online store and check out 2. The system of claim 1.

6. The analysis unit Regularly provide parenting advice as your baby grows 2. The system of claim 1.

7. The analysis unit Vaccination schedule reminders 2. The system of claim 1.

8. The reception unit Estimate the parent's emotions and adjust the timing of receiving instructions or consultations based on the estimated parent's emotions 2. The system of claim 1.

Images