system

Abstract

The system according to the embodiment aims to analyze a child's crying, make specific suggestions based on the child's needs, and automatically purchase the necessary items. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a purchase unit. The collection unit collects the sounds of a child crying. The analysis unit analyzes the sounds of the child crying collected by the collection unit and determines the child's needs. The suggestion unit makes specific suggestions based on the needs determined by the analysis unit. The purchase unit automatically purchases necessary items based on the suggestions made by the suggestion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately determine a child's needs from their crying and provide appropriate support.

[0005] The system according to the embodiment aims to analyze a child's crying, make specific suggestions based on the child's needs, and automatically purchase the necessary items. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a suggestion unit, and a purchase unit. The collection unit collects the sounds of a child crying. The analysis unit analyzes the sounds of a child crying collected by the collection unit and determines the child's needs. The suggestion unit makes specific suggestions based on the needs determined by the analysis unit. The purchase unit automatically purchases necessary items based on the suggestions made by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze a child's crying, make specific suggestions based on the child's needs, and automatically purchase the necessary items. [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 is a service provided to parents and daycare centers to address the declining birthrate and childcare worker shortage. This childcare support system aims to reduce childcare stress, increase the birth rate, and address the childcare worker shortage. This system uses a baby-talk translation AI device to analyze a child's cries and provide information on the child's appetite and sleep needs, suggesting what needs to be done. It also offers functions such as suggesting baby food recipes and automatically purchasing missing ingredients and consumables via an e-commerce site. For example, the baby-talk translation AI device collects the child's cries and analyzes them with an AI. For example, when a child is crying, the device collects the sounds and analyzes them with an AI to determine whether the child is hungry, sleepy, or crying for some other reason. This allows parents and childcare workers to quickly understand the child's needs and take appropriate measures. The AI ​​then makes specific suggestions based on the analysis results. For example, if it determines that the child is hungry, the AI ​​suggests an appropriate baby food recipe. Additionally, if necessary ingredients or consumables are in short supply, the AI ​​automatically processes the purchase through the e-commerce site. This allows parents and childcare workers to purchase the necessary items without any hassle. This service has business models for both individuals and corporations. It is provided free of charge to individuals, with the aim of increasing sales on the e-commerce site. On the other hand, corporations are charged a monthly fee, promoting its use in daycare centers and childcare facilities. In this way, the use of the baby language translation AI device is expected to reduce parenting stress, contribute to increasing the birth rate, and alleviate the childcare worker shortage. As a result, the childcare support system can reduce parenting stress, contribute to increasing the birth rate, and alleviate the childcare worker shortage.

[0029] A childcare support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a purchase unit. The collection unit collects a child's crying sound. For example, the collection unit collects the child's crying sound using a microphone built into a device. When collecting the child's crying sound, the collection unit may also filter surrounding environmental sounds to remove noise. For example, the collection unit may filter surrounding traffic sounds to collect only the crying sound. The collection unit may also filter out operating sounds of home appliances to improve the accuracy of the crying sound analysis. The collection unit may also filter out other children's voices to collect only the crying sound of a specific child. The analysis unit analyzes the crying sound collected by the collection unit to determine the child's needs. For example, the analysis unit may analyze the frequency and pattern of the crying sound to determine needs such as hunger and sleepiness. The analysis unit may also compare the crying sound with past data to improve the accuracy of the analysis. For example, the analysis unit may compare past crying sound data with the current crying sound to improve the accuracy of the analysis. The analysis unit can also learn specific crying patterns based on past data and reflect them in the analysis. Furthermore, the analysis unit can integrate past data and current data to obtain more accurate analysis results. The suggestion unit makes specific suggestions based on the needs determined by the analysis unit. For example, the suggestion unit can suggest baby food recipes. The suggestion unit can also present a list of necessary ingredients and consumables. The suggestion unit can also estimate the child's emotions and adjust the way the suggestions are presented based on the emotions. For example, if the child is crying because they are hungry, the suggestion unit can highlight suggestions related to appetite. If the child is sleepy, the suggestion unit can display suggestions related to sleepiness. If the child is feeling uncomfortable, the suggestion unit can display suggestions to identify the cause. The purchasing unit automatically purchases necessary items based on the suggestions made by the suggestion unit. For example, the purchasing unit can automatically perform a purchase procedure through an e-commerce site. Furthermore, the purchasing unit can select the optimal purchase method by referring to past purchase history. For example, the purchasing department may repurchase the same items based on ingredients or consumables previously purchased.The purchasing unit can also predict the optimal timing for purchase based on past purchase history and carry out the purchase procedure. Furthermore, the purchasing unit can also determine the priority of specific brands and products based on past purchase history. As a result, the childcare support system according to the embodiment can reduce childcare stress, contribute to improving the birth rate, and resolve the shortage of childcare workers.

[0030] The collection unit can collect the baby's crying sound using a microphone built into the device. The collection unit, for example, collects the baby's crying sound using a microphone built into the device. For example, the collection unit efficiently collects the baby's crying sound using a microphone built into the device. The collection unit can also collect the baby's crying sound with high accuracy using a microphone built into the device. For example, the collection unit can collect the baby's crying sound in real time using a microphone built into the device. As a result, the baby's crying sound can be efficiently collected using a microphone built into the device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the crying sound data collected using the microphone built into the device to a generation AI and cause the generation AI to analyze the crying sound data.

[0031] The analysis unit can analyze the frequency or pattern of the crying to determine hunger or sleepiness needs. The analysis unit, for example, analyzes the frequency of the crying to determine hunger needs. For example, the analysis unit analyzes the frequency of the crying to determine whether the child is hungry. The analysis unit can also analyze the pattern of the crying to determine sleepiness needs. For example, the analysis unit analyzes the pattern of the crying to determine whether the child is sleepy. The analysis unit can also analyze the frequency and pattern of the crying in combination to comprehensively determine hunger or sleepiness needs. For example, the analysis unit analyzes the frequency and pattern of the crying in combination to grasp the child's needs in detail. In this way, the child's needs can be accurately determined by analyzing the frequency and pattern of the crying. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input crying data to a generation AI and cause the generation AI to analyze the frequency and pattern of the crying.

[0032] The suggestion unit can suggest baby food recipes. The suggestion unit can suggest appropriate baby food recipes based on, for example, the child's age and weight. For example, the suggestion unit can suggest nutritionally balanced baby food recipes based on the child's age and weight. The suggestion unit can also suggest baby food recipes taking allergy information into consideration. For example, the suggestion unit can suggest safe baby food recipes taking into consideration the child's allergy information. The suggestion unit can also suggest baby food recipes taking into consideration seasonal and local ingredients. For example, the suggestion unit can suggest fresh and nutritious baby food recipes taking into consideration seasonal and local ingredients. This allows parents and childcare workers to provide appropriate meals by suggesting baby food recipes. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input information about the child into the generation AI and cause the generation AI to suggest appropriate baby food recipes.

[0033] The suggestion unit can present a list of necessary ingredients and consumables. For example, the suggestion unit presents a list of ingredients and consumables necessary for a child's meals and daily life. For example, the suggestion unit presents a list of ingredients necessary for a child's meals. The suggestion unit can also present a list of consumables necessary for a child's daily life. For example, the suggestion unit presents a list of consumables necessary for a child's daily life so that parents and childcare workers can understand the items they need. The suggestion unit can also periodically update the list of ingredients and consumables to provide the latest information. For example, the suggestion unit periodically updates the list of ingredients and consumables to provide the latest information. In this way, by presenting the list of necessary ingredients and consumables, parents and childcare workers can understand the items they need. Some or all of the above-mentioned processing by the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the child into the generation AI and cause the generation AI to present a list of necessary ingredients and consumables.

[0034] The purchasing unit can automatically complete the purchase process through an e-commerce site. The purchasing unit, for example, automatically purchases necessary ingredients and consumables through the e-commerce site. For example, the purchasing unit automatically purchases necessary ingredients through the e-commerce site. The purchasing unit can also automatically purchase necessary consumables through the e-commerce site. For example, the purchasing unit automatically purchases necessary consumables through the e-commerce site, thereby reducing the effort required by parents and childcare workers. The purchasing unit can also compare prices on multiple e-commerce sites during the purchase process to purchase at the optimal price. For example, the purchasing unit compares prices on multiple e-commerce sites and completes the purchase process at the lowest price. This allows the necessary items to be quickly obtained by automatically completing the purchase process through the e-commerce site. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input information about the necessary items into a generation AI and have the generation AI execute the optimal purchase process.

[0035] The collection unit can analyze the child's past crying data and select an appropriate collection method. For example, if the past crying data shows that the child tends to cry during a certain time period, the collection unit can concentrate collection on that time period. For example, if the past crying data shows that the child tends to cry during a certain time period, the collection unit can concentrate collection on that time period. Furthermore, if the past data shows that a certain environmental sound affects the crying, the collection unit can filter out that environmental sound. For example, if the past data shows that a certain environmental sound affects the crying, the collection unit can filter out that environmental sound. Furthermore, the collection unit can learn crying patterns based on the past data and collect data in accordance with the predicted timing of crying. For example, the collection unit learns crying patterns based on the past data and collects data in accordance with the predicted timing of crying. In this way, the optimal collection method can be selected by analyzing the past crying data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past crying data into the generation AI and have the generation AI select the optimal collection method.

[0036] When collecting the crying sound, the collection unit can filter surrounding environmental sounds to remove noise. For example, the collection unit filters surrounding traffic sounds and collects only the crying sound. For example, the collection unit filters surrounding traffic sounds and collects only the crying sound. The collection unit can also filter operating sounds of home appliances to improve the accuracy of analyzing the crying sound. For example, the collection unit filters operating sounds of home appliances to improve the accuracy of analyzing the crying sound. The collection unit can also filter other children's voices and collect only the crying sound of a specific child. For example, the collection unit filters other children's voices and collects only the crying sound of a specific child. In this way, by filtering the surrounding environmental sounds, the accuracy of analyzing the crying sound is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental sound data to a generation AI and have the generation AI perform noise removal filtering.

[0037] The collection unit can simultaneously collect biometric information such as the child's body temperature and heart rate when collecting the crying sound. For example, the collection unit can measure the body temperature simultaneously with the crying sound and check for the presence or absence of a fever. For example, the collection unit can measure the body temperature simultaneously with the crying sound and check for the presence or absence of a fever. The collection unit can also simultaneously measure the heart rate simultaneously with the crying sound and evaluate the stress level. For example, the collection unit can measure the heart rate simultaneously with the crying sound and evaluate the stress level. The collection unit can also measure the respiratory rate simultaneously with the crying sound and check for signs of respiratory distress. For example, the collection unit can measure the respiratory rate simultaneously with the crying sound and check for signs of respiratory distress. In this way, by collecting biometric information simultaneously with the crying sound, the child's health condition can be comprehensively understood. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the crying sound data and biometric information data into a generation AI and have the generation AI perform a comprehensive assessment of the child's health condition.

[0038] When collecting crying sounds, the collection unit can adjust the collection range taking into account the child's location information. For example, when a child is in a crib, the collection unit prioritizes collecting crying sounds from the surrounding area. For example, when a child is in a crib, the collection unit prioritizes collecting crying sounds from the surrounding area. Furthermore, when a child is in a living room, the collection unit can collect crying sounds from that area. For example, when a child is in the living room, the collection unit collects crying sounds from that area. Furthermore, when a child is out, the collection unit can adjust the collection range based on GPS information. For example, when a child is out, the collection unit adjusts the collection range based on GPS information. In this way, crying sounds can be collected within an appropriate range by taking the child's location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input location information data to a generation AI and cause the generation AI to adjust the collection range.

[0039] When analyzing the frequency and pattern of a cry, the analysis unit can improve the accuracy of the analysis by comparing it with past data. For example, the analysis unit compares past crying data with the current crying to improve the analysis accuracy. For example, the analysis unit compares past crying data with the current crying to improve the analysis accuracy. The analysis unit can also learn specific crying patterns based on past data and reflect them in the analysis. For example, the analysis unit can learn specific crying patterns based on past data and reflect them in the analysis. The analysis unit can also integrate past data with current data to obtain more accurate analysis results. For example, the analysis unit integrates past data with current data to obtain more accurate analysis results. This improves the analysis accuracy by comparing with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past crying data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0040] When analyzing a cry, the analysis unit can apply different analysis methods depending on the child's age and developmental stage. For example, in the case of a newborn, the analysis unit analyzes a unique crying pattern. For example, in the case of a newborn, the analysis unit analyzes a unique crying pattern. In addition, in the case of a toddler, the analysis unit can also take into account changes in crying that occur with growth. For example, in the case of a toddler, the analysis unit can take into account changes in crying that occur with growth. In addition, in the case of a school-age child, the analysis unit can also analyze crying that includes words. For example, in the case of a school-age child, the analysis unit analyzes crying that includes words. In this way, by applying an analysis method depending on the child's age and developmental stage, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the child's age and developmental stage into the generation AI and cause the generation AI to apply the analysis method.

[0041] When analyzing the crying, the analysis unit can take into account the child's health condition and past medical history. For example, if the child has had a fever in the past, the analysis unit can perform the analysis by referring to the crying pattern at that time. For example, if the child has had a fever in the past, the analysis unit can perform the analysis by referring to the crying pattern at that time. The analysis unit can also perform the analysis by taking into account the influence of allergies, if the child has allergies. For example, if the child has allergies, the analysis unit can perform the analysis by taking into account the influence of allergies. The analysis unit can also perform the analysis based on the crying patterns of illnesses the child has experienced in the past. For example, the analysis unit performs the analysis based on the crying patterns of illnesses the child has experienced in the past. By taking the child's health condition and past medical history into account, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the child's health condition and past medical history into the generation AI and have the generation AI perform the analysis.

[0042] When analyzing a cry, the analysis unit can improve the accuracy of the analysis by comparing it with other children's crying data. For example, the analysis unit compares it with other children's crying data to find common patterns. For example, the analysis unit compares it with other children's crying data to find common patterns. The analysis unit can also learn specific crying patterns based on other children's crying data. For example, the analysis unit learns specific crying patterns based on other children's crying data. The analysis unit can also integrate it with other children's crying data to improve the analysis accuracy. For example, the analysis unit integrates it with other children's crying data to improve the analysis accuracy. As a result, the analysis accuracy is improved by comparing it with other children's crying data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other children's crying data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0043] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the child's needs. For example, if the child is hungry and crying, the suggestion unit makes a detailed meal suggestion. For example, if the child is hungry and crying, the suggestion unit makes a detailed meal suggestion. The suggestion unit can also make a brief sleep suggestion if the child is sleepy. For example, if the child is sleepy, the suggestion unit makes a brief sleep suggestion. The suggestion unit can also make a detailed suggestion to identify the cause of discomfort if the child is feeling uncomfortable. For example, if the child is feeling uncomfortable, the suggestion unit makes a detailed suggestion to identify the cause. In this way, by adjusting the level of detail of the suggestion based on the importance of the child's needs, appropriate information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the child's needs data into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0044] When making a suggestion, the suggestion unit can provide different suggestion contents depending on the child's age and developmental stage. For example, in the case of a newborn, the suggestion unit makes specific dietary and sleeping suggestions. For example, in the case of a newborn, the suggestion unit makes specific dietary and sleeping suggestions. In addition, in the case of a toddler, the suggestion unit can also make dietary and sleeping suggestions that correspond to the child's growth. For example, in the case of a toddler, the suggestion unit makes dietary and sleeping suggestions that correspond to the child's growth. In addition, in the case of a school-age child, the suggestion unit can also make suggestions related to learning and play. For example, in the case of a school-age child, the suggestion unit makes suggestions related to learning and play. This enables more appropriate advice by providing suggestions according to the child's age and developmental stage. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the child's age and developmental stage into the generation AI and cause the generation AI to provide the suggestion contents.

[0045] When making a suggestion, the suggestion unit can make an appropriate suggestion by referring to the child's past behavioral history. The suggestion unit, for example, makes an appropriate meal suggestion based on the child's past eating history. For example, the suggestion unit makes an appropriate meal suggestion based on the child's past eating history. The suggestion unit can also make an appropriate sleep suggestion based on the child's past sleep history. For example, the suggestion unit makes an appropriate sleep suggestion based on the child's past sleep history. The suggestion unit can also make an appropriate play or study suggestion based on the child's past behavioral history. For example, the suggestion unit makes an appropriate play or study suggestion based on the child's past behavioral history. This enables more appropriate suggestions to be made by referring to the child's past behavioral history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input past behavioral history data into a generation AI and cause the generation AI to execute an appropriate suggestion.

[0046] When making a suggestion, the suggestion unit can customize the suggestion content by taking into account the child's living environment and family situation. For example, if the child has an allergy, the suggestion unit makes a meal suggestion that takes into account the influence of the allergy. For example, if the child has an allergy, the suggestion unit makes a meal suggestion that takes into account the influence of the allergy. Furthermore, if the child has a specific lifestyle rhythm, the suggestion unit can make a suggestion that matches the lifestyle rhythm. Furthermore, if the child is in a specific home environment, the suggestion unit can make a suggestion that is appropriate for that environment. For example, if the child is in a specific home environment, the suggestion unit makes a suggestion that is appropriate for that environment. This enables more appropriate suggestions by taking into account the living environment and family situation. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data about the child's living environment and family situation into the generation AI and cause the generation AI to customize the suggestion content.

[0047] At the time of purchase, the purchasing unit can select an appropriate purchasing method by referring to past purchase history. For example, the purchasing unit repurchases the same product based on ingredients or consumables previously purchased. For example, the purchasing unit repurchases the same product based on ingredients or consumables previously purchased. The purchasing unit can also predict the optimal timing for purchase based on the past purchase history and perform the procedure. For example, the purchasing unit predicts the optimal timing for purchase based on the past purchase history and perform the procedure. The purchasing unit can also determine the priority of specific brands or products based on the past purchase history. For example, the purchasing unit determines the priority of specific brands or products based on the past purchase history. In this way, the optimal purchasing method can be selected by referring to the past purchase history. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input past purchase history data into a generation AI and have the generation AI select an appropriate purchasing method.

[0048] At the time of purchase, the purchasing unit can check the inventory status of necessary items in real time and complete the purchase procedure. For example, the purchasing unit checks the inventory status of necessary ingredients in real time and completes the purchase procedure if the ingredients are in stock. For example, the purchasing unit checks the inventory status of necessary ingredients in real time and completes the purchase procedure if the ingredients are in stock. The purchasing unit can also check the inventory status of consumables and complete the purchase procedure preferentially if the ingredients are low in stock. For example, the purchasing unit checks the inventory status of consumables and completes the purchase procedure preferentially if the ingredients are low in stock. The purchasing unit can also check the inventory status of specific brands or products and complete the purchase procedure if the ingredients are in stock. For example, the purchasing unit checks the inventory status of specific brands or products and completes the purchase procedure if the ingredients are in stock. This allows the necessary items to be purchased reliably by checking the inventory status in real time. Some or all of the above-described processing in the purchasing unit may be performed using AI, for example, or may be performed without using AI. For example, the purchasing unit can input inventory status data into a generation AI and have the generation AI execute the purchase procedure.

[0049] At the time of purchase, the purchasing unit can suggest appropriate items by taking into account the user's purchasing history and preferences. For example, the purchasing unit suggests the same items based on ingredients or consumables that the user has purchased in the past. For example, the purchasing unit suggests the same items based on ingredients or consumables that the user has purchased in the past. The purchasing unit can also suggest specific brands or products by taking into account the user's preferences. For example, the purchasing unit suggests specific brands or products by taking into account the user's preferences. The purchasing unit can also predict and suggest the optimal purchase timing based on the user's purchasing history. For example, the purchasing unit predicts and suggests the optimal purchase timing based on the user's purchasing history. This allows the optimal item to be suggested by taking into account the user's purchasing history and preferences. Some or all of the above-described processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's purchasing history and preference data into a generation AI and have the generation AI suggest appropriate items.

[0050] At the time of purchase, the purchasing unit can compare multiple e-commerce sites and complete the purchase process at the appropriate price. For example, the purchasing unit compares prices on multiple e-commerce sites and completes the purchase process at the lowest price. For example, the purchasing unit compares prices on multiple e-commerce sites and completes the purchase process at the lowest price. The purchasing unit can also check inventory status on multiple sites and complete the purchase process at the site that has inventory. For example, the purchasing unit checks inventory status on multiple sites and completes the purchase process at the site that has inventory. The purchasing unit can also compare delivery conditions on multiple sites and complete the purchase process at the site that offers the fastest delivery. For example, the purchasing unit compares delivery conditions on multiple sites and completes the purchase process at the site that offers the fastest delivery. In this way, by comparing multiple e-commerce sites, the purchase process can be completed at the optimal price. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input data from multiple e-commerce sites into a generation AI and have the generation AI complete the purchase process at the optimal price.

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

[0052] The childcare support system may further include a health management unit that monitors the child's health. The health management unit collects biological information such as the child's body temperature, heart rate, and respiratory rate, and can notify parents or caregivers if an abnormality is detected. For example, if a child's body temperature is high, the health management unit may notify the child of the possibility of a fever. Furthermore, if the heart rate is abnormally high, this may indicate stress or excitement, so a notification can be sent urging appropriate action. Furthermore, if the respiratory rate is abnormally low, this may indicate a sign of respiratory distress, so a notification can be sent urging emergency action. This allows for real-time monitoring of the child's health and enables prompt action.

[0053] The childcare support system may further include a growth recorder that manages a child's growth record. The growth recorder collects data such as a child's height, weight, and developmental stage, allowing parents and caregivers to easily check the data. For example, the growth recorder may periodically record a child's height and weight and display a growth curve. It may also provide advice according to the child's developmental stage. For example, the growth recorder may record important events, such as the day a child first walked or first spoke, and notify the parents. Furthermore, the growth recorder may evaluate growth progress by comparing with past data and detect any abnormalities early. This allows for comprehensive management of a child's growth and provides appropriate support.

[0054] The childcare support system can further include a sleep analysis unit that analyzes a child's sleep patterns. The sleep analysis unit monitors the child's sleep duration, sleep quality, number of nighttime awakenings, etc., and can provide appropriate advice to parents and childcare workers. For example, if a child is not getting enough sleep, the sleep analysis unit can provide advice to encourage the habit of going to bed early and waking up early. If the sleep quality is poor, the sleep analysis unit can suggest ways to improve the bedroom environment or find ways to relax. Furthermore, if the child is waking up frequently at night, the cause can be identified and appropriate measures can be suggested. This makes it possible to analyze a child's sleep patterns and support healthy sleep habits.

[0055] The childcare support system can further include a play and learning unit that supports children's play and learning. The play and learning unit can suggest play and learning programs according to the child's age and interests, allowing parents and childcare workers to provide appropriate activities. For example, the play and learning unit can suggest educational toys and picture books according to the child's age. It can also provide play and learning programs tailored to the seasons and events. For example, it can suggest water play and nature observation in the summer, and indoor crafts and reading in the winter. Furthermore, the play and learning unit can provide individually customized programs taking into account the child's interests and areas of expertise. This can support children's play and learning and promote their healthy development.

[0056] The childcare support system can further include a relaxation unit to reduce stress for parents and childcare workers. The relaxation unit can monitor the stress level of parents and childcare workers and suggest appropriate relaxation methods. For example, the relaxation unit can monitor the heart rate and respiratory rate of parents and childcare workers, and suggest deep breathing or meditation if stress levels are high. The relaxation unit can also suggest relaxing music or aromatherapy. Furthermore, the relaxation unit can suggest short stretches and exercises to help parents and childcare workers refresh themselves. This can reduce stress for parents and childcare workers and improve the quality of childcare and care.

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

[0058] Step 1: The collection unit collects the child's crying sound. The collection unit collects the child's crying sound using a microphone built into the device. The collection unit can also filter out surrounding environmental sounds to remove noise. For example, it can filter out surrounding traffic sounds, the operating sounds of household appliances, and other children's voices, and collect only the crying sound of a specific child. Step 2: The analysis unit analyzes the crying sounds collected by the collection unit and determines the child's needs. The analysis unit analyzes the frequency and pattern of the crying sounds to determine needs such as hunger and sleepiness. It can also compare the data with past data to improve the accuracy of the analysis. For example, it can compare past crying data with the current crying sound, learn specific crying patterns, and reflect them in the analysis. Step 3: The suggestion unit makes specific suggestions based on the needs determined by the analysis unit. For example, the suggestion unit suggests baby food recipes and presents a list of necessary ingredients and consumables. It can also estimate the child's emotions and adjust the way suggestions are presented based on those emotions. For example, if the child is crying because they are hungry, it will emphasize suggestions related to appetite, and if they are sleepy, it will display suggestions related to sleepiness. Step 4: The purchasing department automatically purchases the necessary items based on the recommendations made by the proposal department. The purchasing department automatically completes the purchasing procedure through the e-commerce site. It can also refer to past purchase history to select the optimal purchasing method, repurchase the same item, or predict the optimal purchase timing and complete the procedure.

[0059] (Example 2) A childcare support system according to an embodiment of the present invention is a service provided to parents and daycare centers to address the declining birthrate and childcare worker shortage. This childcare support system aims to reduce childcare stress, increase the birth rate, and address the childcare worker shortage. This system uses a baby-talk translation AI device to analyze a child's cries and provide information on the child's appetite and sleep needs, suggesting what needs to be done. It also offers functions such as suggesting baby food recipes and automatically purchasing missing ingredients and consumables via an e-commerce site. For example, the baby-talk translation AI device collects the child's cries and analyzes them with an AI. For example, when a child is crying, the device collects the sounds and analyzes them with an AI to determine whether the child is hungry, sleepy, or crying for some other reason. This allows parents and childcare workers to quickly understand the child's needs and take appropriate measures. The AI ​​then makes specific suggestions based on the analysis results. For example, if it determines that the child is hungry, the AI ​​suggests an appropriate baby food recipe. Additionally, if necessary ingredients or consumables are in short supply, the AI ​​automatically processes the purchase through the e-commerce site. This allows parents and childcare workers to purchase the necessary items without any hassle. This service has business models for both individuals and corporations. It is provided free of charge to individuals, with the aim of increasing sales on the e-commerce site. On the other hand, corporations are charged a monthly fee, promoting its use in daycare centers and childcare facilities. In this way, the use of the baby language translation AI device is expected to reduce parenting stress, contribute to increasing the birth rate, and alleviate the childcare worker shortage. As a result, the childcare support system can reduce parenting stress, contribute to increasing the birth rate, and alleviate the childcare worker shortage.

[0060] A childcare support system according to an embodiment includes a collection unit, an analysis unit, a suggestion unit, and a purchase unit. The collection unit collects a child's crying sound. For example, the collection unit collects the child's crying sound using a microphone built into a device. When collecting the child's crying sound, the collection unit may also filter surrounding environmental sounds to remove noise. For example, the collection unit may filter surrounding traffic sounds to collect only the crying sound. The collection unit may also filter out operating sounds of home appliances to improve the accuracy of the crying sound analysis. The collection unit may also filter out other children's voices to collect only the crying sound of a specific child. The analysis unit analyzes the crying sound collected by the collection unit to determine the child's needs. For example, the analysis unit may analyze the frequency and pattern of the crying sound to determine needs such as hunger and sleepiness. The analysis unit may also compare the crying sound with past data to improve the accuracy of the analysis. For example, the analysis unit may compare past crying sound data with the current crying sound to improve the accuracy of the analysis. The analysis unit can also learn specific crying patterns based on past data and reflect them in the analysis. Furthermore, the analysis unit can integrate past data and current data to obtain more accurate analysis results. The suggestion unit makes specific suggestions based on the needs determined by the analysis unit. For example, the suggestion unit can suggest baby food recipes. The suggestion unit can also present a list of necessary ingredients and consumables. The suggestion unit can also estimate the child's emotions and adjust the way the suggestions are presented based on the emotions. For example, if the child is crying because they are hungry, the suggestion unit can highlight suggestions related to appetite. If the child is sleepy, the suggestion unit can display suggestions related to sleepiness. If the child is feeling uncomfortable, the suggestion unit can display suggestions to identify the cause. The purchasing unit automatically purchases necessary items based on the suggestions made by the suggestion unit. For example, the purchasing unit can automatically perform a purchase procedure through an e-commerce site. Furthermore, the purchasing unit can select the optimal purchase method by referring to past purchase history at the time of purchase. For example, the purchasing department may repurchase the same items based on ingredients or consumables previously purchased.The purchasing unit can also predict the optimal timing for purchase based on past purchase history and carry out the purchase procedure. Furthermore, the purchasing unit can also determine the priority of specific brands and products based on past purchase history. As a result, the childcare support system according to the embodiment can reduce childcare stress, contribute to improving the birth rate, and resolve the shortage of childcare workers.

[0061] The collection unit can collect the baby's crying sound using a microphone built into the device. The collection unit, for example, collects the baby's crying sound using a microphone built into the device. For example, the collection unit efficiently collects the baby's crying sound using a microphone built into the device. The collection unit can also collect the baby's crying sound with high accuracy using a microphone built into the device. For example, the collection unit can collect the baby's crying sound in real time using a microphone built into the device. As a result, the baby's crying sound can be efficiently collected using a microphone built into the device. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the crying sound data collected using the microphone built into the device to a generation AI and cause the generation AI to analyze the crying sound data.

[0062] The analysis unit can analyze the frequency or pattern of the crying to determine hunger or sleepiness needs. The analysis unit, for example, analyzes the frequency of the crying to determine hunger needs. For example, the analysis unit analyzes the frequency of the crying to determine whether the child is hungry. The analysis unit can also analyze the pattern of the crying to determine sleepiness needs. For example, the analysis unit analyzes the pattern of the crying to determine whether the child is sleepy. The analysis unit can also analyze the frequency and pattern of the crying in combination to comprehensively determine hunger or sleepiness needs. For example, the analysis unit analyzes the frequency and pattern of the crying in combination to grasp the child's needs in detail. In this way, the child's needs can be accurately determined by analyzing the frequency and pattern of the crying. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input crying data to a generation AI and cause the generation AI to analyze the frequency and pattern of the crying.

[0063] The suggestion unit can suggest baby food recipes. The suggestion unit can suggest appropriate baby food recipes based on, for example, the child's age and weight. For example, the suggestion unit can suggest nutritionally balanced baby food recipes based on the child's age and weight. The suggestion unit can also suggest baby food recipes taking allergy information into consideration. For example, the suggestion unit can suggest safe baby food recipes taking into consideration the child's allergy information. The suggestion unit can also suggest baby food recipes taking into consideration seasonal and local ingredients. For example, the suggestion unit can suggest fresh and nutritious baby food recipes taking into consideration seasonal and local ingredients. This allows parents and childcare workers to provide appropriate meals by suggesting baby food recipes. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input information about the child into the generation AI and cause the generation AI to suggest appropriate baby food recipes.

[0064] The suggestion unit can present a list of necessary ingredients and consumables. For example, the suggestion unit presents a list of ingredients and consumables necessary for a child's meals and daily life. For example, the suggestion unit presents a list of ingredients necessary for a child's meals. The suggestion unit can also present a list of consumables necessary for a child's daily life. For example, the suggestion unit presents a list of consumables necessary for a child's daily life so that parents and childcare workers can understand the items they need. The suggestion unit can also periodically update the list of ingredients and consumables to provide the latest information. For example, the suggestion unit periodically updates the list of ingredients and consumables to provide the latest information. In this way, by presenting the list of necessary ingredients and consumables, parents and childcare workers can understand the items they need. Some or all of the above-mentioned processing by the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input information about the child into the generation AI and cause the generation AI to present a list of necessary ingredients and consumables.

[0065] The purchasing unit can automatically complete the purchase process through an e-commerce site. The purchasing unit, for example, automatically purchases necessary ingredients and consumables through the e-commerce site. For example, the purchasing unit automatically purchases necessary ingredients through the e-commerce site. The purchasing unit can also automatically purchase necessary consumables through the e-commerce site. For example, the purchasing unit automatically purchases necessary consumables through the e-commerce site, thereby reducing the effort required by parents and childcare workers. The purchasing unit can also compare prices on multiple e-commerce sites during the purchase process to purchase at the optimal price. For example, the purchasing unit compares prices on multiple e-commerce sites and completes the purchase process at the lowest price. This allows the necessary items to be quickly obtained by automatically completing the purchase process through the e-commerce site. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input information about the necessary items into a generation AI and have the generation AI execute the optimal purchase process.

[0066] The collection unit can estimate the child's emotions and adjust the timing of collecting crying sounds based on the emotions. For example, when the child is excited, the collection unit increases the frequency of collecting crying sounds to capture changes in emotions in real time. For example, when the child is excited, the collection unit increases the frequency of collecting crying sounds to capture changes in emotions in real time. The collection unit can also reduce the collection frequency when the child is relaxed to reduce battery consumption of the device. For example, when the child is relaxed, the collection unit can reduce the collection frequency to reduce battery consumption of the device. The collection unit can also adjust the collection timing when the child seems sleepy to start collecting before crying occurs. For example, when the child seems sleepy, the collection unit adjusts the collection timing to start collecting before crying occurs. In this way, by adjusting the collection timing based on the child's emotions, crying sounds can be collected at more appropriate timing. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the child's emotion data to the generation AI and cause the generation AI to adjust the collection timing.

[0067] The collection unit can analyze the child's past crying data and select an appropriate collection method. For example, if the past crying data shows that the child tends to cry during a certain time period, the collection unit can concentrate collection on that time period. For example, if the past crying data shows that the child tends to cry during a certain time period, the collection unit can concentrate collection on that time period. Furthermore, if the past data shows that a certain environmental sound affects the crying, the collection unit can filter out that environmental sound. For example, if the past data shows that a certain environmental sound affects the crying, the collection unit can filter out that environmental sound. Furthermore, the collection unit can learn crying patterns based on the past data and collect data in accordance with the predicted timing of crying. For example, the collection unit learns crying patterns based on the past data and collects data in accordance with the predicted timing of crying. In this way, the optimal collection method can be selected by analyzing the past crying data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past crying data into the generation AI and have the generation AI select the optimal collection method.

[0068] When collecting the crying sound, the collection unit can filter surrounding environmental sounds to remove noise. For example, the collection unit filters surrounding traffic sounds and collects only the crying sound. For example, the collection unit filters surrounding traffic sounds and collects only the crying sound. The collection unit can also filter operating sounds of home appliances to improve the accuracy of analyzing the crying sound. For example, the collection unit filters operating sounds of home appliances to improve the accuracy of analyzing the crying sound. The collection unit can also filter other children's voices and collect only the crying sound of a specific child. For example, the collection unit filters other children's voices and collects only the crying sound of a specific child. In this way, by filtering the surrounding environmental sounds, the accuracy of analyzing the crying sound is improved. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input environmental sound data to a generation AI and have the generation AI perform noise removal filtering.

[0069] The collection unit can estimate the child's emotions and determine the priority of the cries to be collected based on the emotions. For example, if the child is crying because he or she is hungry, the collection unit prioritizes collecting the cries. For example, if the child is crying because he or she is hungry, the collection unit prioritizes collecting the cries. The collection unit can also prioritize collecting the cries of the child when he or she is sleepy. For example, if the child is sleepy, the collection unit prioritizes collecting the cries. The collection unit can also prioritize collecting the cries of the child when he or she is feeling uncomfortable. For example, if the child is feeling uncomfortable, the collection unit prioritizes collecting the cries. In this way, by prioritizing the cries based on the child's emotions, important cries can be prioritized and collected. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the child's emotion data to the generation AI and cause the generation AI to determine the priority of the cries.

[0070] The collection unit can simultaneously collect biometric information such as the child's body temperature and heart rate when collecting the crying sound. For example, the collection unit can measure the body temperature simultaneously with the crying sound and check for the presence or absence of a fever. For example, the collection unit can measure the body temperature simultaneously with the crying sound and check for the presence or absence of a fever. The collection unit can also simultaneously measure the heart rate simultaneously with the crying sound and evaluate the stress level. For example, the collection unit can measure the heart rate simultaneously with the crying sound and evaluate the stress level. The collection unit can also measure the respiratory rate simultaneously with the crying sound and check for signs of respiratory distress. For example, the collection unit can measure the respiratory rate simultaneously with the crying sound and check for signs of respiratory distress. In this way, by collecting biometric information simultaneously with the crying sound, the child's health condition can be comprehensively understood. Some or all of the above-mentioned processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the crying sound data and biometric information data into a generation AI and have the generation AI perform a comprehensive assessment of the child's health condition.

[0071] When collecting crying sounds, the collection unit can adjust the collection range taking into account the child's location information. For example, when a child is in a crib, the collection unit prioritizes collecting crying sounds from the surrounding area. For example, when a child is in a crib, the collection unit prioritizes collecting crying sounds from the surrounding area. Furthermore, when a child is in a living room, the collection unit can collect crying sounds from that area. For example, when a child is in the living room, the collection unit collects crying sounds from that area. Furthermore, when a child is out, the collection unit can adjust the collection range based on GPS information. For example, when a child is out, the collection unit adjusts the collection range based on GPS information. In this way, crying sounds can be collected within an appropriate range by taking the child's location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input location information data to a generation AI and cause the generation AI to adjust the collection range.

[0072] The analysis unit can estimate the child's emotions and adjust the analysis algorithm based on the emotions. For example, if the child is crying because he or she is hungry, the analysis unit prioritizes analyzing crying patterns related to appetite. For example, if the child is crying because he or she is hungry, the analysis unit prioritizes analyzing crying patterns related to appetite. Furthermore, if the child is sleepy, the analysis unit can analyze crying patterns related to sleepiness. For example, if the child is sleepy, the analysis unit analyzes crying patterns related to sleepiness. Furthermore, if the child is feeling uncomfortable, the analysis unit can perform an analysis to identify the cause. For example, if the child is feeling uncomfortable, the analysis unit performs an analysis to identify the cause. This improves the accuracy of the analysis by adjusting the analysis algorithm based on the child's emotions. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotional data into the generation AI and cause the generation AI to adjust the analysis algorithm.

[0073] When analyzing the frequency and pattern of a cry, the analysis unit can improve the accuracy of the analysis by comparing it with past data. For example, the analysis unit compares past crying data with the current crying to improve the analysis accuracy. For example, the analysis unit compares past crying data with the current crying to improve the analysis accuracy. The analysis unit can also learn specific crying patterns based on past data and reflect them in the analysis. For example, the analysis unit can learn specific crying patterns based on past data and reflect them in the analysis. The analysis unit can also integrate past data with current data to obtain more accurate analysis results. For example, the analysis unit integrates past data with current data to obtain more accurate analysis results. This improves the analysis accuracy by comparing with past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past crying data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0074] When analyzing a cry, the analysis unit can apply different analysis methods depending on the child's age and developmental stage. For example, in the case of a newborn, the analysis unit analyzes a unique crying pattern. For example, in the case of a newborn, the analysis unit analyzes a unique crying pattern. In addition, in the case of a toddler, the analysis unit can also take into account changes in crying that occur with growth. For example, in the case of a toddler, the analysis unit can take into account changes in crying that occur with growth. In addition, in the case of a school-age child, the analysis unit can also analyze crying that includes words. For example, in the case of a school-age child, the analysis unit analyzes crying that includes words. In this way, by applying an analysis method depending on the child's age and developmental stage, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input data on the child's age and developmental stage into the generation AI and cause the generation AI to apply the analysis method.

[0075] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the emotions. For example, if a child is hungry and crying, the analysis unit highlights and displays information related to appetite. For example, if a child is hungry and crying, the analysis unit highlights and displays information related to appetite. The analysis unit can also display information related to sleepiness if the child is sleepy. For example, if a child is sleepy, the analysis unit displays information related to sleepiness. The analysis unit can also display information for identifying the cause of discomfort if the child is feeling uncomfortable. For example, if a child is feeling uncomfortable, the analysis unit displays information for identifying the cause. By adjusting the display method based on the child's emotions, information can be provided in a form that is easy for parents and caregivers to understand. Some or all of the above-described processing by the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the display method.

[0076] When analyzing the crying, the analysis unit can take into account the child's health condition and past medical history. For example, if the child has had a fever in the past, the analysis unit can perform the analysis by referring to the crying pattern at that time. For example, if the child has had a fever in the past, the analysis unit can perform the analysis by referring to the crying pattern at that time. The analysis unit can also perform the analysis by taking into account the influence of allergies, if the child has allergies. For example, if the child has allergies, the analysis unit can perform the analysis by taking into account the influence of allergies. The analysis unit can also perform the analysis based on the crying patterns of illnesses the child has experienced in the past. For example, the analysis unit performs the analysis based on the crying patterns of illnesses the child has experienced in the past. By taking the child's health condition and past medical history into account, more accurate analysis results can be obtained. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the child's health condition and past medical history into the generation AI and have the generation AI perform the analysis.

[0077] When analyzing a cry, the analysis unit can improve the accuracy of the analysis by comparing it with other children's crying data. For example, the analysis unit compares it with other children's crying data to find common patterns. For example, the analysis unit compares it with other children's crying data to find common patterns. The analysis unit can also learn specific crying patterns based on other children's crying data. For example, the analysis unit learns specific crying patterns based on other children's crying data. The analysis unit can also integrate it with other children's crying data to improve the analysis accuracy. For example, the analysis unit integrates it with other children's crying data to improve the analysis accuracy. As a result, the analysis accuracy is improved by comparing it with other children's crying data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input other children's crying data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0078] The suggestion unit can estimate the child's emotions and adjust the way suggestions are expressed based on the emotions. For example, if a child is hungry and crying, the suggestion unit highlights and displays suggestions related to appetite. For example, if a child is hungry and crying, the suggestion unit highlights and displays suggestions related to appetite. The suggestion unit can also display suggestions related to sleepiness when a child is sleepy. For example, if a child is sleepy, the suggestion unit displays suggestions related to sleepiness. The suggestion unit can also display suggestions for identifying the cause of discomfort when a child is feeling uncomfortable. For example, if a child is feeling uncomfortable, the suggestion unit displays suggestions for identifying the cause. By adjusting the way suggestions are expressed based on the child's emotions, information can be provided in a form that is easy for parents and caregivers to understand. Some or all of the above-described processing by the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the child's emotion data into a generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0079] When making a suggestion, the suggestion unit can adjust the level of detail of the suggestion based on the importance of the child's needs. For example, if the child is hungry and crying, the suggestion unit makes a detailed meal suggestion. For example, if the child is hungry and crying, the suggestion unit makes a detailed meal suggestion. The suggestion unit can also make a brief sleep suggestion if the child is sleepy. For example, if the child is sleepy, the suggestion unit makes a brief sleep suggestion. The suggestion unit can also make a detailed suggestion to identify the cause of discomfort if the child is feeling uncomfortable. For example, if the child is feeling uncomfortable, the suggestion unit makes a detailed suggestion to identify the cause. In this way, by adjusting the level of detail of the suggestion based on the importance of the child's needs, appropriate information can be provided. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the child's needs data into a generation AI and cause the generation AI to adjust the level of detail of the suggestion.

[0080] When making a suggestion, the suggestion unit can provide different suggestion contents depending on the child's age and developmental stage. For example, in the case of a newborn, the suggestion unit makes specific dietary and sleeping suggestions. For example, in the case of a newborn, the suggestion unit makes specific dietary and sleeping suggestions. In addition, in the case of a toddler, the suggestion unit can also make dietary and sleeping suggestions that correspond to the child's growth. For example, in the case of a toddler, the suggestion unit makes dietary and sleeping suggestions that correspond to the child's growth. In addition, in the case of a school-age child, the suggestion unit can also make suggestions related to learning and play. For example, in the case of a school-age child, the suggestion unit makes suggestions related to learning and play. This enables more appropriate advice by providing suggestions according to the child's age and developmental stage. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the child's age and developmental stage into the generation AI and cause the generation AI to provide the suggestion contents.

[0081] The suggestion unit can estimate the child's emotions and determine the priority of suggestions based on the emotions. For example, if the child is hungry and crying, the suggestion unit gives the highest priority to meal suggestions. For example, if the child is hungry and crying, the suggestion unit gives the highest priority to meal suggestions. Furthermore, if the child is sleepy, the suggestion unit can give the next highest priority to sleep suggestions. For example, if the child is sleepy, the suggestion unit can give the next highest priority to sleep suggestions. Furthermore, if the child is feeling uncomfortable, the suggestion unit can give the highest priority to suggestions to identify the cause of discomfort. For example, if the child is feeling uncomfortable, the suggestion unit gives the highest priority to suggestions to identify the cause of discomfort. In this way, by determining the priority of suggestions based on the child's emotions, important suggestions can be given priority. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit may input the child's emotion data into the generation AI and cause the generation AI to determine the priority of suggestions.

[0082] When making a suggestion, the suggestion unit can make an appropriate suggestion by referring to the child's past behavioral history. The suggestion unit, for example, makes an appropriate meal suggestion based on the child's past eating history. For example, the suggestion unit makes an appropriate meal suggestion based on the child's past eating history. The suggestion unit can also make an appropriate sleep suggestion based on the child's past sleep history. For example, the suggestion unit makes an appropriate sleep suggestion based on the child's past sleep history. The suggestion unit can also make an appropriate play or study suggestion based on the child's past behavioral history. For example, the suggestion unit makes an appropriate play or study suggestion based on the child's past behavioral history. This enables more appropriate suggestions to be made by referring to the child's past behavioral history. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input past behavioral history data into a generation AI and cause the generation AI to execute an appropriate suggestion.

[0083] When making a suggestion, the suggestion unit can customize the suggestion content by taking into account the child's living environment and family situation. For example, if the child has an allergy, the suggestion unit makes a meal suggestion that takes into account the influence of the allergy. For example, if the child has an allergy, the suggestion unit makes a meal suggestion that takes into account the influence of the allergy. Furthermore, if the child has a specific lifestyle rhythm, the suggestion unit can make a suggestion that matches the lifestyle rhythm. Furthermore, if the child is in a specific home environment, the suggestion unit can make a suggestion that is appropriate for that environment. For example, if the child is in a specific home environment, the suggestion unit makes a suggestion that is appropriate for that environment. This enables more appropriate suggestions by taking into account the living environment and family situation. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input data about the child's living environment and family situation into the generation AI and cause the generation AI to customize the suggestion content.

[0084] The purchasing unit can estimate the child's emotions and adjust the timing of the purchase process based on the emotions. For example, if a child is crying because they are hungry, the purchasing unit can quickly complete the purchase process for ingredients. For example, if a child is crying because they are hungry, the purchasing unit can quickly complete the purchase process for ingredients. Furthermore, if a child is sleepy, the purchasing unit can prioritize the purchase process for sleep-related items. For example, if a child is sleepy, the purchasing unit can prioritize the purchase process for sleep-related items. Furthermore, if a child is feeling uncomfortable, the purchasing unit can prioritize the purchase process for items to resolve the cause of the discomfort. For example, if a child is feeling uncomfortable, the purchasing unit can prioritize the purchase process for items to resolve the cause of the discomfort. In this way, by adjusting the timing of the purchase process based on the child's emotions, necessary items can be purchased quickly. Some or all of the above-mentioned processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input the child's emotion data into a generation AI and cause the generation AI to adjust the timing of the purchase process.

[0085] At the time of purchase, the purchasing unit can select an appropriate purchasing method by referring to past purchase history. For example, the purchasing unit repurchases the same product based on ingredients or consumables previously purchased. For example, the purchasing unit repurchases the same product based on ingredients or consumables previously purchased. The purchasing unit can also predict the optimal timing for purchase based on the past purchase history and perform the procedure. For example, the purchasing unit predicts the optimal timing for purchase based on the past purchase history and perform the procedure. The purchasing unit can also determine the priority of specific brands or products based on the past purchase history. For example, the purchasing unit determines the priority of specific brands or products based on the past purchase history. In this way, the optimal purchasing method can be selected by referring to the past purchase history. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input past purchase history data into a generation AI and have the generation AI select an appropriate purchasing method.

[0086] At the time of purchase, the purchasing unit can check the inventory status of necessary items in real time and complete the purchase procedure. For example, the purchasing unit checks the inventory status of necessary ingredients in real time and completes the purchase procedure if the ingredients are in stock. For example, the purchasing unit checks the inventory status of necessary ingredients in real time and completes the purchase procedure if the ingredients are in stock. The purchasing unit can also check the inventory status of consumables and complete the purchase procedure preferentially if the ingredients are low in stock. For example, the purchasing unit checks the inventory status of consumables and completes the purchase procedure preferentially if the ingredients are low in stock. The purchasing unit can also check the inventory status of specific brands or products and complete the purchase procedure if the ingredients are in stock. For example, the purchasing unit checks the inventory status of specific brands or products and completes the purchase procedure if the ingredients are in stock. This allows the necessary items to be purchased reliably by checking the inventory status in real time. Some or all of the above-described processing in the purchasing unit may be performed using AI, for example, or may be performed without using AI. For example, the purchasing unit can input inventory status data into a generation AI and have the generation AI execute the purchase procedure.

[0087] The purchasing unit can estimate the child's emotions and prioritize the items to be purchased based on the emotions. For example, if a child is crying because he or she is hungry, the purchasing unit can prioritize purchasing food items. For example, if a child is crying because he or she is hungry, the purchasing unit can prioritize purchasing food items. Furthermore, if a child is sleepy, the purchasing unit can prioritize purchasing sleep-related items. For example, if a child is sleepy, the purchasing unit can prioritize purchasing sleep-related items. Furthermore, if a child is feeling uncomfortable, the purchasing unit can prioritize purchasing items to alleviate the cause of the discomfort. For example, if a child is feeling uncomfortable, the purchasing unit can prioritize purchasing items to alleviate the cause of the discomfort. In this way, by prioritizing the items based on the child's emotions, important items can be purchased first. Some or all of the above-described processing in the purchasing unit may be performed using AI, for example, or without AI. For example, the purchasing unit can input the child's emotion data into a generation AI and have the generation AI determine the priority of the items.

[0088] At the time of purchase, the purchasing unit can suggest appropriate items by taking into account the user's purchasing history and preferences. For example, the purchasing unit suggests the same items based on ingredients or consumables that the user has purchased in the past. For example, the purchasing unit suggests the same items based on ingredients or consumables that the user has purchased in the past. The purchasing unit can also suggest specific brands or products by taking into account the user's preferences. For example, the purchasing unit suggests specific brands or products by taking into account the user's preferences. The purchasing unit can also predict and suggest the optimal purchase timing based on the user's purchasing history. For example, the purchasing unit predicts and suggests the optimal purchase timing based on the user's purchasing history. This allows the optimal item to be suggested by taking into account the user's purchasing history and preferences. Some or all of the above-described processing in the purchasing unit may be performed using, or without, AI. For example, the purchasing unit can input the user's purchasing history and preference data into a generation AI and have the generation AI suggest appropriate items.

[0089] At the time of purchase, the purchasing unit can compare multiple e-commerce sites and complete the purchase process at the appropriate price. For example, the purchasing unit compares prices on multiple e-commerce sites and completes the purchase process at the lowest price. For example, the purchasing unit compares prices on multiple e-commerce sites and completes the purchase process at the lowest price. The purchasing unit can also check inventory status on multiple sites and complete the purchase process at the site that has inventory. For example, the purchasing unit checks inventory status on multiple sites and completes the purchase process at the site that has inventory. The purchasing unit can also compare delivery conditions on multiple sites and complete the purchase process at the site that offers the fastest delivery. For example, the purchasing unit compares delivery conditions on multiple sites and completes the purchase process at the site that offers the fastest delivery. In this way, by comparing multiple e-commerce sites, the purchase process can be completed at the optimal price. Some or all of the above-described processing in the purchasing unit may be performed using, for example, AI, or may be performed without using AI. For example, the purchasing unit can input data from multiple e-commerce sites into a generation AI and have the generation AI complete the purchase process at the optimal price. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and purchase unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects a child's crying sound using the microphone 38B of the smart device 14 and filters surrounding environmental sounds to remove noise. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected crying sound to determine the child's needs. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes specific suggestions based on the analysis results. The purchase unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically purchases necessary items based on the suggested content. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and purchase unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects a child's crying sound using the microphone 238 of the smart glasses 214 and filters surrounding environmental sounds to remove noise. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected crying sound and determines the child's needs. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, makes specific suggestions based on the analysis results. The purchase unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically purchases necessary items based on the suggested content. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and purchase unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects a child's crying sound using the microphone 238 of the headset-type terminal 314 and filters surrounding environmental sounds to remove noise. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected crying sound to determine the child's needs. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes specific suggestions based on the analysis results. The purchase unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically purchases necessary items based on the suggestions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and purchase unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects a child's crying sound using the microphone 238 of the robot 414 and filters surrounding environmental sounds to remove noise. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected crying sound and determines the child's needs. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, makes specific suggestions based on the analysis results. The purchase unit, realized, for example, by the specific processing unit 290 of the data processing device 12, automatically purchases necessary items based on the suggested content.

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

[0091] The childcare support system may further include a health management unit that monitors the child's health. The health management unit collects biological information such as the child's body temperature, heart rate, and respiratory rate, and can notify parents or caregivers if an abnormality is detected. For example, if a child's body temperature is high, the health management unit may notify the child of the possibility of a fever. Furthermore, if the heart rate is abnormally high, this may indicate stress or excitement, so a notification can be sent urging appropriate action. Furthermore, if the respiratory rate is abnormally low, this may indicate a sign of respiratory distress, so a notification can be sent urging emergency action. This allows for real-time monitoring of the child's health and enables prompt action.

[0092] The childcare support system may further include a growth recorder that manages a child's growth record. The growth recorder collects data such as a child's height, weight, and developmental stage, allowing parents and caregivers to easily check the data. For example, the growth recorder may periodically record a child's height and weight and display a growth curve. It may also provide advice according to the child's developmental stage. For example, the growth recorder may record important events, such as the day a child first walked or first spoke, and notify the parents. Furthermore, the growth recorder may evaluate growth progress by comparing with past data and detect any abnormalities early. This allows for comprehensive management of a child's growth and provides appropriate support.

[0093] The childcare support system can further include a sleep analysis unit that analyzes a child's sleep patterns. The sleep analysis unit monitors the child's sleep duration, sleep quality, number of nighttime awakenings, etc., and can provide appropriate advice to parents and childcare workers. For example, if a child is not getting enough sleep, the sleep analysis unit can provide advice to encourage the habit of going to bed early and waking up early. If the sleep quality is poor, the sleep analysis unit can suggest ways to improve the bedroom environment or find ways to relax. Furthermore, if the child is waking up frequently at night, the cause can be identified and appropriate measures can be suggested. This makes it possible to analyze a child's sleep patterns and support healthy sleep habits.

[0094] The childcare support system can further include a play and learning unit that supports children's play and learning. The play and learning unit can suggest play and learning programs according to the child's age and interests, allowing parents and childcare workers to provide appropriate activities. For example, the play and learning unit can suggest educational toys and picture books according to the child's age. It can also provide play and learning programs tailored to the seasons and events. For example, it can suggest water play and nature observation in the summer, and indoor crafts and reading in the winter. Furthermore, the play and learning unit can provide individually customized programs taking into account the child's interests and areas of expertise. This can support children's play and learning and promote their healthy development.

[0095] The childcare support system can further include a relaxation unit to reduce stress for parents and childcare workers. The relaxation unit can monitor the stress level of parents and childcare workers and suggest appropriate relaxation methods. For example, the relaxation unit can monitor the heart rate and respiratory rate of parents and childcare workers, and suggest deep breathing or meditation if stress levels are high. The relaxation unit can also suggest relaxing music or aromatherapy. Furthermore, the relaxation unit can suggest short stretches and exercises to help parents and childcare workers refresh themselves. This can reduce stress for parents and childcare workers and improve the quality of childcare and care.

[0096] The childcare support system can also estimate a child's emotions and suggest appropriate play and learning programs based on those emotions. For example, if a child is excited, it can suggest physical play that will help the child release energy. If a child is relaxed, it can suggest activities that will help the child concentrate, such as quiet picture book reading or puzzles. Furthermore, if a child is feeling anxious, it can suggest playing with stuffed toys or physical contact with a parent to give the child a sense of security. This allows the system to provide appropriate play and learning programs based on a child's emotions and support the child's emotional development.

[0097] The childcare support system can also estimate a child's emotions and make appropriate meal suggestions based on those emotions. For example, if a child is crying because they are hungry, it can suggest a nutritionally balanced meal. If a child is feeling anxious, it can suggest a warm soup or porridge that will give them a sense of security. Furthermore, if a child is excited, it can suggest a light meal that is easy to digest. In this way, it is possible to make appropriate meal suggestions based on a child's emotions and support their health.

[0098] The childcare support system can also estimate a child's emotions and suggest an appropriate sleeping environment based on those emotions. For example, if a child is sleepy, it can suggest quiet music and dim lighting. If a child is feeling anxious, it can suggest a reassuring stuffed animal or a recording of the parent's voice. Furthermore, if a child is excited, it can suggest relaxing aromatherapy or deep breathing techniques. This allows the system to provide an appropriate sleeping environment according to a child's emotions and improve the quality of their sleep.

[0099] The childcare support system can also estimate a child's emotions and suggest appropriate relaxation methods based on those emotions. For example, if a child is excited, it can suggest physical play that will help the child release energy. If a child is feeling anxious, it can suggest playing with a stuffed animal or physical contact with the parent, which will give the child a sense of security. Furthermore, if a child is relaxed, it can suggest activities that will help improve concentration, such as quiet picture book reading or puzzles. This allows the system to provide appropriate relaxation methods according to a child's emotions and support their emotional development.

[0100] The childcare support system can also estimate a child's emotions and suggest appropriate health management methods based on those emotions. For example, if a child is feeling anxious, advice can be given to create a relaxing environment. If a child is excited, advice can be given to exercise that will help the child release energy. Furthermore, if a child is relaxed, advice can be given to rest in a quiet environment. This allows for the provision of appropriate health management methods according to a child's emotions and comprehensive support for the child's health.

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

[0102] Step 1: The collection unit collects the child's crying sound. The collection unit collects the child's crying sound using a microphone built into the device. The collection unit can also filter out surrounding environmental sounds to remove noise. For example, it can filter out surrounding traffic sounds, the operating sounds of household appliances, and other children's voices, and collect only the crying sound of a specific child. Step 2: The analysis unit analyzes the crying sounds collected by the collection unit and determines the child's needs. The analysis unit analyzes the frequency and pattern of the crying sounds to determine needs such as hunger and sleepiness. It can also compare the data with past data to improve the accuracy of the analysis. For example, it can compare past crying data with the current crying sound, learn specific crying patterns, and reflect them in the analysis. Step 3: The suggestion unit makes specific suggestions based on the needs determined by the analysis unit. For example, the suggestion unit suggests baby food recipes and presents a list of necessary ingredients and consumables. It can also estimate the child's emotions and adjust the way suggestions are presented based on those emotions. For example, if the child is crying because they are hungry, it will emphasize suggestions related to appetite, and if they are sleepy, it will display suggestions related to sleepiness. Step 4: The purchasing department automatically purchases the necessary items based on the recommendations made by the proposal department. The purchasing department automatically completes the purchasing procedure through the e-commerce site. It can also refer to past purchase history to select the optimal purchasing method, repurchase the same item, or predict the optimal purchase timing and complete the procedure.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

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

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

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

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

[0146] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0154] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0156] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

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

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0166] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects the cries of children; an analysis unit that analyzes the crying sounds collected by the collection unit and determines the needs of the child; a proposal unit that makes specific proposals based on the needs determined by the analysis unit; a purchasing unit that automatically purchases necessary items based on the content suggested by the purchasing unit; A system characterized by:

2. The collecting unit The device's built-in microphone collects the sound of the baby crying. The system of claim 1 .

3. The analysis unit Analyzing the frequency or pattern of crying to determine hunger or sleep needs The system of claim 1 .

4. The proposal unit Suggest baby food recipes The system of claim 1 .

5. The proposal unit Provide a list of ingredients and supplies needed The system of claim 1 .

6. The purchasing department Automate checkouts through e-commerce sites The system of claim 1 .

7. The collecting unit Estimate the child's emotions and adjust the timing of collecting crying sounds based on those emotions The system of claim 1 .

8. The collecting unit Analyze past crying data of children and select the appropriate collection method The system of claim 1 .

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