System

Abstract

An object of a system according to an embodiment is to propose and execute a next optimal action in childcare.SOLUTION: A system includes a record collection unit, an analysis unit, a proposal unit, and an execution unit. The record collection unit collects daily excretion and lactation records. The analysis unit analyzes the data collected by the record collection unit. The proposal unit proposes a next optimal action based on a result analyzed by the analysis unit. The execution unit executes the action proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to determine the next best action to take when raising children, often leaving parents feeling overwhelmed.

[0005] The system according to the embodiment aims to propose and execute the next optimal action in child-rearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a record collection unit, an analysis unit, a suggestion unit, and an execution unit. The record collection unit collects daily excretion and breastfeeding records. The analysis unit analyzes the data collected by the record collection unit. The suggestion unit suggests the next optimal action based on the results of the analysis by the analysis unit. The execution unit carries out the action suggested by the suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest and execute the next optimal action in child-rearing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A child-rearing support system according to an embodiment of the present invention suggests optimal actions to parents when they are troubled by their child's crying. This system uses a generative AI to suggest the next optimal action based on daily records of excretion and breastfeeding, and automatically plays favorite music or videos and images that have proven effective in stopping crying. This allows parents to easily learn the optimal actions to stop their child's crying and put them to sleep.

[0029] A child-rearing support system according to an embodiment includes a record collection unit, an analysis unit, a suggestion unit, and an execution unit. The record collection unit collects daily excretion and breastfeeding records. For example, when a parent enters excretion and breastfeeding times into a smartphone app, the data is sent to the record collection unit. The record collection unit can also automatically collect excretion and breastfeeding data using sensors. For example, data is collected using smart diapers or breastfeeding sensors. The analysis unit analyzes the data collected by the record collection unit. For example, a generation AI analyzes the data and predicts the child's current condition and the next action required. The analysis unit can also analyze the data using a machine learning algorithm. For example, a generation AI performs statistical analysis based on past data to predict the next action. The suggestion unit suggests the next optimal action based on the results of the analysis by the analysis unit. For example, the generation AI may suggest, "Your child seems hungry recently. It may be time for the next feeding." The suggestion unit can also suggest actions to the parent using an interactive UI. For example, the generation AI may suggest specific actions through dialogue with the parent. The execution unit executes the action suggested by the suggestion unit. For example, the generation AI automatically plays music, videos, or images that have a proven track record of stopping a child from crying. The execution unit can also execute an action selected by a parent. For example, it can play music selected by the parent using a smartphone app. This allows the child-rearing support system according to the embodiment to easily know the optimal action to stop a child from crying and put them to sleep.

[0030] The record collection unit can collect data on sleep patterns and playtime in addition to records of excretion and breastfeeding. For example, when a parent enters data on sleep patterns and playtime into a smartphone app, the record collection unit transmits the data to the record collection unit. The record collection unit can also automatically collect data on sleep patterns and playtime using sensors. For example, data can be collected using a smartwatch or activity tracker. This makes it possible to provide comprehensive parenting advice.

[0031] The analysis unit can analyze the data taking into account the parent's lifestyle and stress level. For example, when a parent inputs lifestyle and stress level data into a smartphone app, the analysis unit transmits the data to the analysis unit. The analysis unit can also automatically collect lifestyle and stress level data using sensors. For example, data can be collected using a smartwatch or stress monitor. This makes it possible to provide parenting advice tailored to the parent's health condition.

[0032] The suggestion unit learns the parent's past action history and can make more personalized action suggestions. For example, the suggestion unit uses a generation AI to learn the parent's past action history and make more personalized action suggestions. For example, it optimizes the next suggestion based on actions that have been effective in the past. The suggestion unit also analyzes the parent's action history and makes personalized action suggestions. For example, it suggests actions that are effective at specific time periods. This makes it possible to suggest more appropriate actions to the parent.

[0033] The execution unit can select and suggest the most suitable music and videos according to the child's age and developmental stage. For example, the execution unit uses a generative AI to select and suggest the most suitable music and videos according to the child's age and developmental stage. For example, it can suggest relaxing music for infants and educational videos for toddlers. The execution unit also analyzes data according to the child's developmental stage and selects the most suitable music and videos. For example, it can suggest music and videos that are suited to the child's development. This makes it possible to provide appropriate content according to the child's development.

[0034] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0035] The acquisition unit collects learning content appropriate for a child's developmental stage, and the provision unit can suggest it to parents. For example, it can suggest picture books and toys that provide visual stimulation to infants. It can also suggest educational apps and videos for young children to learn language and number concepts. Furthermore, the acquisition unit can select appropriate learning content based on a child's interests. For example, it can suggest picture books and videos about animals to a child who is interested in animals. This can support a child's growth and learning.

[0036] The analysis unit can suggest times when the parent can relax based on the parent's daily rhythm and stress level. For example, it can suggest times when the parent can relax that avoid busy times. It can also identify times when the parent is feeling stressed and suggest relaxing activities for those times. For example, it can suggest yoga or meditation for times when the parent can relax. The analysis unit can also suggest ways to create a relaxing environment based on the parent's daily rhythm. For example, it can suggest adjusting the lighting and music to times when the parent can relax. This can reduce stress for the parent and provide them with time to relax.

[0037] The provision unit can suggest travel destinations and leisure activities that will help the parent relax, based on the parent's lifestyle. For example, it can suggest hot spring resorts or places rich in nature where the parent can relax. Also, if the parent is feeling stressed, it can suggest travel plans that include relaxing activities. For example, it can suggest travel plans that include spas and massages where the parent can relax. Furthermore, the provision unit can suggest relaxing leisure activities according to the parent's lifestyle. For example, it can suggest hiking or picnics where the parent can relax. This can reduce stress for the parent and provide them with time to relax.

[0038] The acquisition unit can suggest hobbies and activities that will help the parent relax, based on the parent's lifestyle and stress level. For example, it can suggest gardening or handicrafts that will help the parent relax. If the parent is feeling stressed, it can also suggest art or music activities that will help the parent relax. For example, it can suggest painting or playing an instrument that will help the parent relax. Furthermore, the acquisition unit can suggest hobbies and activities that will help the parent relax, based on the parent's lifestyle. For example, it can suggest hobbies or activities that will help the parent relax. This can reduce stress for the parent and provide them with time to relax.

[0039] The processing flow of the first embodiment will be briefly explained below.

[0040] Step 1: The record collection unit collects daily excretion and breastfeeding records. For example, when a parent enters the time of excretion or breastfeeding into a smartphone app, the data is sent to the record collection unit. The record collection unit can also automatically collect excretion and breastfeeding data using sensors. For example, data can be collected using smart diapers or breastfeeding sensors. Step 2: The analysis unit analyzes the data collected by the record collection unit. For example, a generative AI analyzes the data and predicts the child's current condition and the next action required. The analysis unit can also analyze the data using machine learning algorithms. For example, it performs statistical analysis based on past data to predict the next action. Step 3: The suggestion unit suggests the next optimal action based on the results of the analysis by the analysis unit. For example, the generation AI might suggest, "Your child seems hungry recently. It may be close to time for the next feeding." The suggestion unit can also suggest actions to the parent using a conversational UI. For example, the generation AI might suggest specific actions while interacting with the parent. Step 4: The execution unit executes the action proposed by the suggestion unit. For example, the generation AI automatically plays music, videos, or images that have a proven track record of stopping a child from crying. The execution unit can also execute an action selected by the parent. For example, it can play music selected by the parent on a smartphone app.

[0041] (Example 2) A child-rearing support system according to an embodiment of the present invention suggests optimal actions to parents when they are troubled by their child's crying. This system uses a generative AI to suggest the next optimal action based on daily records of excretion and breastfeeding, and automatically plays favorite music or videos and images that have proven effective in stopping crying. This allows parents to easily learn the optimal actions to stop their child's crying and put them to sleep.

[0042] A child-rearing support system according to an embodiment includes a record collection unit, an analysis unit, a suggestion unit, and an execution unit. The record collection unit collects daily excretion and breastfeeding records. For example, when a parent enters excretion and breastfeeding times into a smartphone app, the data is sent to the record collection unit. The record collection unit can also automatically collect excretion and breastfeeding data using sensors. For example, data is collected using smart diapers or breastfeeding sensors. The analysis unit analyzes the data collected by the record collection unit. For example, a generation AI analyzes the data and predicts the child's current condition and the next action required. The analysis unit can also analyze the data using a machine learning algorithm. For example, a generation AI performs statistical analysis based on past data to predict the next action. The suggestion unit suggests the next optimal action based on the results of the analysis by the analysis unit. For example, the generation AI may suggest, "Your child seems hungry recently. It may be time for the next feeding." The suggestion unit can also suggest actions to the parent using an interactive UI. For example, the generation AI may suggest specific actions through dialogue with the parent. The execution unit executes the action suggested by the suggestion unit. For example, the generation AI automatically plays music, videos, or images that have a proven track record of stopping a child from crying. The execution unit can also execute an action selected by a parent. For example, it can play music selected by the parent using a smartphone app. This allows the child-rearing support system according to the embodiment to easily know the optimal action to stop a child from crying and put them to sleep.

[0043] The record collection unit can collect data on sleep patterns and playtime in addition to records of excretion and breastfeeding. For example, when a parent enters data on sleep patterns and playtime into a smartphone app, the record collection unit transmits the data to the record collection unit. The record collection unit can also automatically collect data on sleep patterns and playtime using sensors. For example, data can be collected using a smartwatch or activity tracker. This makes it possible to provide comprehensive parenting advice.

[0044] The analysis unit can analyze the data taking into account the parent's lifestyle and stress level. For example, when a parent inputs lifestyle and stress level data into a smartphone app, the analysis unit transmits the data to the analysis unit. The analysis unit can also automatically collect lifestyle and stress level data using sensors. For example, data can be collected using a smartwatch or stress monitor. This makes it possible to provide parenting advice tailored to the parent's health condition.

[0045] The suggestion unit learns the parent's past action history and can make more personalized action suggestions. For example, the suggestion unit uses a generation AI to learn the parent's past action history and make more personalized action suggestions. For example, it optimizes the next suggestion based on actions that have been effective in the past. The suggestion unit also analyzes the parent's action history and makes personalized action suggestions. For example, it suggests actions that are effective at specific time periods. This makes it possible to suggest more appropriate actions to the parent.

[0046] The execution unit can select and suggest the most suitable music and videos according to the child's age and developmental stage. For example, the execution unit uses a generative AI to select and suggest the most suitable music and videos according to the child's age and developmental stage. For example, it can suggest relaxing music for infants and educational videos for toddlers. The execution unit also analyzes data according to the child's developmental stage and selects the most suitable music and videos. For example, it can suggest music and videos that are suited to the child's development. This makes it possible to provide appropriate content according to the child's development.

[0047] The execution unit can analyze the parent's emotional state and suggest content that will help the parent relax. For example, the execution unit uses an emotion estimation function in which the generation AI analyzes the parent's emotional state and suggests content that will help the parent relax. For example, it suggests music or videos that will help the parent relax. The execution unit also uses the emotion estimation function to monitor the parent's emotional state in real time and suggest content that will help the parent relax. For example, it suggests ways to create an environment where the parent can relax. This can reduce stress for the parent and provide an environment where the parent can relax.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The acquisition unit can analyze the parent's tone of voice and speaking style to estimate the parent's emotional state. For example, it can analyze the tone and speed of the parent's voice when speaking to the child to estimate whether the parent is feeling stressed. The acquisition unit can also capture the parent's facial expression with a camera and estimate the parent's emotion from that expression. For example, it can analyze whether the parent is smiling and determine whether the parent is relaxed. The acquisition unit can also measure the parent's body temperature and heart rate with a sensor to estimate the parent's emotional state. For example, if the parent's heart rate is elevated, it can determine that the parent is feeling stressed. This makes it possible to provide appropriate advice according to the parent's emotional state.

[0050] The providing unit can suggest ways to communicate with a child based on the parent's emotional state. For example, if a parent is feeling stressed, it can suggest ways to relax. Specifically, it can suggest deep breathing or light exercise. Furthermore, if a parent is relaxed, it can suggest ways to play or communicate with a child. For example, it can suggest time to read picture books together. Furthermore, the providing unit can provide advice on how to change how a parent interacts with a child depending on the parent's emotional state. For example, if a parent is tired, it can suggest ways to play that are short and effective. This can help maintain a better relationship between parent and child.

[0051] The determination unit can estimate the parent's emotional state and, based on the estimated emotion, suggest ways to create an environment where the parent can relax. For example, if the parent is feeling stressed, it can suggest playing relaxing music. Also, if the parent is tired, it can suggest relaxing lighting or scents. For example, it can suggest using soft lighting or an aroma diffuser. Furthermore, the determination unit can suggest a place where the parent can relax depending on the parent's emotional state. For example, it can suggest resting in a quiet place. This can reduce stress for the parent and provide an environment where the parent can relax.

[0052] The acquisition unit can estimate the emotional state of the parent and suggest activities that will help the parent relax based on the estimated emotion. For example, if the parent is feeling stressed, it can suggest relaxing yoga or meditation. Also, if the parent is tired, it can suggest relaxing massages or hot springs. Furthermore, the acquisition unit can suggest relaxing hobbies or activities according to the parent's emotional state. For example, it can suggest reading or watching a movie that will help the parent relax. This can reduce stress for the parent and provide them with time to relax.

[0053] The providing unit can suggest meals and drinks that will help the parent relax based on the parent's emotional state. For example, if the parent is feeling stressed, it can suggest relaxing herbal tea or light meals. Also, if the parent is tired, it can suggest nutritious meals and drinks that will help the parent relax. For example, it can suggest smoothies or salads that will help the parent relax. Furthermore, the providing unit can suggest relaxing meal times and places according to the parent's emotional state. For example, it can suggest eating in a quiet place. This can reduce stress for the parent and provide a relaxing mealtime.

[0054] The acquisition unit collects learning content appropriate for a child's developmental stage, and the provision unit can suggest it to parents. For example, it can suggest picture books and toys that provide visual stimulation to infants. It can also suggest educational apps and videos for young children to learn language and number concepts. Furthermore, the acquisition unit can select appropriate learning content based on a child's interests. For example, it can suggest picture books and videos about animals to a child who is interested in animals. This can support a child's growth and learning.

[0055] The analysis unit can suggest times when the parent can relax based on the parent's daily rhythm and stress level. For example, it can suggest times when the parent can relax that avoid busy times. It can also identify times when the parent is feeling stressed and suggest relaxing activities for those times. For example, it can suggest yoga or meditation for times when the parent can relax. The analysis unit can also suggest ways to create a relaxing environment based on the parent's daily rhythm. For example, it can suggest adjusting the lighting and music to times when the parent can relax. This can reduce stress for the parent and provide them with time to relax.

[0056] The provision unit can suggest travel destinations and leisure activities that will help the parent relax, based on the parent's lifestyle. For example, it can suggest hot spring resorts or places rich in nature where the parent can relax. Also, if the parent is feeling stressed, it can suggest travel plans that include relaxing activities. For example, it can suggest travel plans that include spas and massages where the parent can relax. Furthermore, the provision unit can suggest relaxing leisure activities according to the parent's lifestyle. For example, it can suggest hiking or picnics where the parent can relax. This can reduce stress for the parent and provide them with time to relax.

[0057] The acquisition unit can suggest hobbies and activities that will help the parent relax, based on the parent's lifestyle and stress level. For example, it can suggest gardening or handicrafts that will help the parent relax. If the parent is feeling stressed, it can also suggest art or music activities that will help the parent relax. For example, it can suggest painting or playing an instrument that will help the parent relax. Furthermore, the acquisition unit can suggest hobbies and activities that will help the parent relax, based on the parent's lifestyle. For example, it can suggest hobbies or activities that will help the parent relax. This can reduce stress for the parent and provide them with time to relax.

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

[0059] Step 1: The record collection unit collects daily excretion and breastfeeding records. For example, when a parent enters the time of excretion or breastfeeding into a smartphone app, the data is sent to the record collection unit. The record collection unit can also automatically collect excretion and breastfeeding data using sensors. For example, data can be collected using smart diapers or breastfeeding sensors. Step 2: The analysis unit analyzes the data collected by the record collection unit. For example, a generative AI analyzes the data and predicts the child's current condition and the next action required. The analysis unit can also analyze the data using machine learning algorithms. For example, it performs statistical analysis based on past data to predict the next action. Step 3: The suggestion unit suggests the next optimal action based on the results of the analysis by the analysis unit. For example, the generation AI might suggest, "Your child seems hungry recently. It may be close to time for the next feeding." The suggestion unit can also suggest actions to the parent using a conversational UI. For example, the generation AI might suggest specific actions while interacting with the parent. Step 4: The execution unit executes the action proposed by the suggestion unit. For example, the generation AI automatically plays music, videos, or images that have a proven track record of stopping a child from crying. The execution unit can also execute an action selected by the parent. For example, it can play music selected by the parent on a smartphone app.

[0060] 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.

[0061] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0062] 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.

[0063] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0064] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] 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).

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0077] 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.

[0078] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0079] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0089] 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.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0094] 7, the 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] In the robot 414, 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 robot 414 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.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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."

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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. [Explanation of symbols]

[0127] 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 record collection department that collects daily excretion and breastfeeding records; an analysis unit that analyzes the data collected by the record collection unit; a suggestion unit that proposes the next optimal action based on the result of the analysis by the analysis unit; an execution unit that executes the action proposed by the proposal unit. A system characterized by:

2. The record collection unit In addition to recording the toileting and feeding records, data on sleep patterns and playtime will also be collected.

2. The system of claim 1.

3. The analysis unit Analyze the data taking into account the parents' daily routines and stress levels.

2. The system of claim 1.

4. The proposal unit Learns parental behavior history to provide more personalized action suggestions 2. The system of claim 1.

5. The execution unit: Select and suggest the most suitable music and videos for children based on their age and developmental stage 2. The system of claim 1.

Images