system

Abstract

The system according to this embodiment aims to understand the baby's expressions and needs and provide appropriate dialogue and advice. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a dialogue generation unit, and an advice provision unit. The collection unit collects language data of the baby. The analysis unit analyzes the data collected by the collection unit to understand the baby's expressions and needs. The dialogue generation unit generates appropriate dialogue based on the results obtained by the analysis unit. The advice provision unit provides advice to parents and childcare workers based on the dialogue generated by the dialogue generation unit.

Description

Technical Field

[0006] , , , ,

[0005] , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to accurately understand the subtle expressions and needs of a baby and respond appropriately.

[0005] The system according to the embodiment aims to understand the expressions and needs of a baby and provide appropriate conversations and advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a dialogue generation unit, and an advice provision unit. The data collection unit collects language data from the baby. The analysis unit analyzes the data collected by the data collection unit to understand the baby's expressions and needs. The dialogue generation unit generates appropriate dialogues based on the results obtained by the analysis unit. The advice provision unit provides advice to parents and childcare workers based on the dialogues generated by the dialogue generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can understand the baby's expressions and needs and provide appropriate dialogue and advice. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-based childcare support system according to an embodiment of the present invention is a system for facilitating communication with babies. This system brings new possibilities to childcare support for parents and childcare workers. The AI-based childcare support system learns language data from tens of thousands of babies and builds a sophisticated database of baby talk. This makes it possible to understand the subtle expressions and needs of babies with high accuracy and propose appropriate responses. The real-time dialogue support function interprets the baby's vocalizations on the spot and presents the optimal communication method. Furthermore, by learning the language patterns and preferences of each baby and personalizing the dialogue, it achieves more effective communication. For example, for babies who react strongly to certain words or sounds, it is possible to generate dialogues that utilize them, allowing for detailed responses. The AI-based childcare support system is not merely a translation tool, but also provides comprehensive advice on the baby's behavior and expressions. This allows parents and childcare workers to understand the baby's needs more deeply and respond appropriately. As a result, it is expected that the bond with the baby will deepen and healthy development will be promoted. For example, the AI-based childcare support system collects language data from tens of thousands of babies and uses it to train the AI. In this process, the system meticulously records the baby's vocalizations and expressions to build a database. For example, if a baby says "mama," the audio data is collected and used to train the AI. This allows the system to understand the baby's language patterns. Next, the AI-based childcare support system provides real-time dialogue support. When a baby makes a sound, the AI ​​analyzes the sound and suggests an appropriate response. For example, if a baby says "I'm hungry," the AI ​​analyzes the sound and notifies the parent that "the baby seems hungry." This allows the parent to respond quickly to the baby's needs. Furthermore, the AI-based childcare support system learns each baby's language patterns and preferences to personalize the dialogue. For example, for a baby who strongly responds to certain words or sounds, the system generates dialogues that utilize them. For instance, if a baby strongly responds to the word "toy," the AI ​​uses that word to generate dialogues that facilitate communication with the baby.Furthermore, AI-based childcare support systems also provide comprehensive advice regarding a baby's behavior and expressions. For example, if a baby is crying, the system analyzes the reason and notifies the parents that "the baby seems sleepy." This allows parents to better understand their baby's needs and respond appropriately. AI-based childcare support systems are extremely useful for parents and caregivers, and are expected to deepen the bond with babies and promote healthy development. For example, it can be difficult for first-time or inexperienced parents to correctly understand a baby's behavior and expressions, but by using an AI-based childcare support system, they can respond appropriately to the baby's needs. Also, for childcare workers and kindergarten teachers, communication with babies and toddlers is a crucial element of education and childcare, and by using an AI-based childcare support system, they can accurately grasp the baby's needs and emotions. In this way, AI-based childcare support systems can streamline communication with babies and bring new possibilities to childcare support for parents and caregivers.

[0029] The AI-based childcare support system according to this embodiment comprises a data collection unit, an analysis unit, a dialogue generation unit, and an advice provision unit. The data collection unit collects language data of babies. The data collection unit can collect language data from, for example, tens of thousands of babies. The data collection unit meticulously records the babies' vocalizations and expressions and builds a database. For example, if a baby says "mama," the data collection unit collects the audio data and uses it to train the AI. This allows the data collection unit to understand the babies' language patterns. The analysis unit analyzes the data collected by the data collection unit to understand the babies' expressions and needs. For example, the analysis unit can analyze the collected baby language data to understand the babies' subtle expressions and needs. The analysis unit can use AI to analyze the babies' language data. For example, the analysis unit inputs the baby's vocalization data into the AI, which then analyzes the data to understand the babies' expressions and needs. The dialogue generation unit generates appropriate dialogues based on the results obtained by the analysis unit. For example, the dialogue generation unit can learn the babies' language patterns and preferences and generate personalized dialogues. The dialogue generation unit generates dialogue using AI based on the baby's language data. For example, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. The advice provision unit provides advice to parents and caregivers based on the dialogue generated by the dialogue generation unit. The advice provision unit can, for example, provide comprehensive advice regarding the baby's behavior and expressions. The advice provision unit analyzes the baby's behavior and expressions and generates advice using AI. For example, if the baby is crying, the advice provision unit analyzes the reason and notifies the parents that "the baby seems sleepy." As a result, the AI-based childcare support system according to this embodiment can collect, analyze, generate dialogue, and provide advice based on the baby's language data.

[0030] The data collection unit collects language data from babies. For example, the unit can collect language data from tens of thousands of babies. Specifically, the unit meticulously records the babies' vocalizations and expressions to build a database. For instance, if a baby says "mama," the unit collects that audio data and uses it to train the AI. The unit collects the babies' vocalizations using high-precision microphones and speech recognition devices, and stores the audio data digitally. Furthermore, the unit can record not only the babies' vocalizations but also non-verbal communication such as facial expressions and gestures. This allows the unit to build a rich dataset for understanding the babies' language patterns. The collected data is stored on a cloud server, making it accessible to the analysis and dialogue generation units. The unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a baby is most vocal during a particular time period, data collection can be concentrated during that time. This allows the unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit to understand the baby's expressions and needs. For example, the analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. The analysis unit can use AI to analyze the baby's language data. Specifically, the analysis unit inputs the baby's vocalization data into the AI, which then analyzes the data to understand the baby's expressions and needs. The AI ​​uses speech recognition and natural language processing technologies to analyze the baby's vocalization patterns and vocal characteristics. For example, if the baby says "toy," the analysis unit can analyze the tone and rhythm of the vocalization to determine whether the baby is excited or calm. Furthermore, the analysis unit can analyze not only the baby's vocalization data but also non-verbal data such as facial expressions and gestures. This allows the analysis unit to understand the baby's overall communication patterns and grasp their needs more accurately. The analysis unit can also utilize past data and statistical information to analyze long-term trends and patterns. For example, if a baby tends to make certain vocalizations at specific times of day, the system can analyze these patterns and provide appropriate advice to parents and caregivers. This allows the analysis unit to not only monitor the situation in real time but also provide long-term childcare support, improving the overall reliability and usefulness of the system.

[0032] The dialogue generation unit generates appropriate dialogue based on the results obtained by the analysis unit. For example, the dialogue generation unit can learn the baby's language patterns and preferences to generate personalized dialogue. The dialogue generation unit uses AI to generate dialogue based on the baby's language data. Specifically, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. For example, if the baby says "toy," the dialogue generation unit will generate a response such as "Do you want to play with a toy?" to facilitate dialogue with the baby. The dialogue generation unit uses natural language generation technology to generate appropriate responses to the baby's vocalizations and expressions. Furthermore, the dialogue generation unit can monitor the baby's responses in real time and dynamically adjust the content of the dialogue. For example, if the baby is excited, the dialogue generation unit will select calming words to proceed with the dialogue. In addition, the dialogue generation unit can generate dialogue considering not only the baby's vocalizations but also nonverbal communication such as facial expressions and gestures. As a result, the dialogue generation unit can achieve natural communication with the baby and support the baby's development. The dialogue generation unit can also provide parents and childcare workers with methods for interacting with babies and examples of appropriate responses. This allows the dialogue generation unit to facilitate communication with babies and enhance the effectiveness of childcare support.

[0033] The advice-providing unit provides advice to parents and caregivers based on dialogues generated by the dialogue generation unit. For example, the advice-providing unit can provide comprehensive advice regarding a baby's behavior and expressions. It analyzes the baby's behavior and expressions and generates advice using AI. Specifically, if a baby is crying, the advice-providing unit analyzes the reason and notifies the parents that "the baby seems sleepy." Based on the baby's behavior and expressions, the advice-providing unit provides appropriate responses and parenting tips. For example, if a baby tends to cry at certain times, it can suggest appropriate responses for those times. Furthermore, the advice-providing unit can provide customized advice tailored to the baby's developmental stage and individual needs. For example, when a baby is beginning to learn language, it can suggest specific methods to encourage language development to the parents. The advice-providing unit can also collect feedback from parents and caregivers and continuously improve the content of its advice. This allows the advice-providing unit to provide reliable parenting support to parents and caregivers and support the healthy growth of babies. The advice-providing unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the advice department to provide advice quickly and reliably to parents and childcare workers, maximizing the effectiveness of childcare support.

[0034] The data collection unit can collect language data from tens of thousands of babies. For example, the data collection unit collects language data from tens of thousands of babies. The data collection unit meticulously records the babies' vocalizations and expressions to build a database. For example, when a baby says "mama," the data collection unit collects that audio data and uses it to train an AI. This allows the data collection unit to understand the babies' language patterns. By collecting a large amount of baby language data, highly accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's vocalization data into a generating AI, which can then analyze the data to understand the babies' language patterns.

[0035] The analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. For example, the analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. The analysis unit can use AI to analyze the baby's language data. For example, the analysis unit can input the baby's vocalization data into the AI, which will analyze the data to understand the baby's expressions and needs. This allows for appropriate responses by understanding the baby's subtle expressions and needs. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the baby's vocalization data into a generating AI, which will analyze the data to understand the baby's expressions and needs.

[0036] The dialogue generation unit can learn the baby's language patterns and preferences and generate personalized dialogues. For example, the dialogue generation unit learns the baby's language patterns and preferences and generates personalized dialogues. The dialogue generation unit uses AI to generate dialogues based on the baby's language data. For example, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. This enables more effective communication by generating personalized dialogues based on the baby's language patterns and preferences. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's language data into a generating AI, which can then analyze the data to generate personalized dialogues.

[0037] The advice-providing unit can provide comprehensive advice regarding the baby's behavior and expressions. For example, the advice-providing unit can provide comprehensive advice regarding the baby's behavior and expressions. The advice-providing unit analyzes the baby's behavior and expressions and generates advice using AI. For example, if the baby is crying, the advice-providing unit analyzes the reason and notifies the parents that "the baby seems sleepy." By providing comprehensive advice regarding the baby's behavior and expressions, parents and caregivers can better understand the baby's needs and respond appropriately. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the baby's behavior data into a generating AI, which can then analyze the data and generate advice.

[0038] The dialogue generation unit can generate dialogues for babies who strongly respond to specific words or sounds, utilizing those words or sounds. For example, the dialogue generation unit generates dialogues for babies who strongly respond to specific words or sounds, utilizing those words or sounds. The dialogue generation unit generates dialogues using AI based on the baby's language data. For example, if the baby strongly responds to the word "toy," the dialogue generation unit will generate dialogue using that word. This makes it possible to communicate more effectively with babies who strongly respond to specific words or sounds by generating dialogues that utilize those words or sounds. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's language data into a generating AI, and the generating AI can analyze that data to generate dialogue.

[0039] The data collection unit can estimate the baby's emotions and adjust the timing of language data collection based on the estimated emotions. For example, the AI ​​can adjust the data collection unit to collect more language data when the baby is in a good mood. The AI ​​can also adjust the data collection unit to temporarily suspend collection when the baby is crying and resume it after the baby has calmed down. The AI ​​can also adjust the data collection unit to focus on collecting responses to specific words or sounds when the baby is agitated. This allows for more appropriate data collection by adjusting the timing of language data collection based on the baby's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's emotional data into a generating AI, which can then analyze the data and adjust the collection timing.

[0040] The data collection unit can analyze the baby's past language data and select the optimal collection method. For example, the data collection unit can identify the time of day when the baby vocalizes most frequently from past data and concentrate data collection during that time. The data collection unit can also analyze the baby's tendency to vocalize in specific situations from past data and enhance data collection in those situations. The data collection unit can also analyze the baby's tendency to react to specific sounds from past data and use those sounds for data collection. By analyzing the baby's past language data, the optimal collection method can be selected, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's past language data into a generating AI, which can then analyze the data and select the optimal collection method.

[0041] The data collection unit can filter language data based on the baby's current living situation and areas of interest. For example, when the baby is playing, the data collection unit can prioritize collecting language data related to play. When the baby is eating, the data collection unit can also prioritize collecting language data related to eating. Before the baby falls asleep, the data collection unit can also prioritize collecting language data in a relaxed state. This allows for the collection of more relevant data by filtering based on the baby's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's living situation data into a generating AI, which can then analyze and filter that data.

[0042] The data collection unit can estimate the baby's emotions and determine the priority of language data to collect based on the estimated emotions. For example, the data collection unit can prioritize collecting more language data when the baby is in a good mood. The data collection unit can also prioritize temporarily suspending collection when the baby is crying and resuming it after the baby has calmed down. The data collection unit can also prioritize focusing on collecting responses to specific words or sounds when the baby is agitated. This makes it possible to prioritize the collection of more important data by determining the priority of language data to collect based on the baby's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the baby's emotion data into a generating AI, which can then analyze the data to determine the priority of language data to collect.

[0043] The data collection unit can prioritize the collection of highly relevant data based on the baby's geographical location when collecting language data. For example, when the baby is at home, the data collection unit prioritizes collecting language data from within the home. When the baby is in a park, the data collection unit can also prioritize the collection of language data related to outdoor play. When the baby is in daycare, the data collection unit can also prioritize the collection of language data related to activities at daycare. This allows for more effective data collection by prioritizing the collection of highly relevant data based on the baby's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's geographical location data into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0044] The data collection unit can analyze the baby's social media activity and collect relevant data when collecting language data. For example, the data collection unit can analyze how often the baby appears on the parents' social media and collect language data at that time. The data collection unit can also analyze the baby's tendency to react to specific social media posts and collect language data related to those posts. The data collection unit can also collect language data when the baby participates in live streams on social media. This allows for more comprehensive data collection by analyzing the baby's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the baby's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0045] The analysis unit can estimate the baby's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, when the baby is in a good mood, the analysis unit will present the analysis results using positive expressions. When the baby is crying, the analysis unit can also present the analysis results using calming expressions. When the baby is agitated, the analysis unit can also present the analysis results using expressions that soothe the agitation. By adjusting the way the analysis is presented based on the baby's emotions, it becomes possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the way the analysis is presented.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the language data during the analysis. For example, the analysis unit can perform a detailed analysis on important language data and present specific results. For less important language data, the analysis unit can perform a simplified analysis and present only an overview. For highly important language data, the analysis unit can use multiple analysis methods to present detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the language data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, and the generating AI can analyze that data to adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the category of the language data during analysis. For example, the analysis unit can apply a voice analysis algorithm to baby vocalization data. The analysis unit can also apply an image analysis algorithm to baby facial expression data. The analysis unit can also apply a motion analysis algorithm to baby motion data. By applying different analysis algorithms depending on the category of the language data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, and the generating AI can analyze that data and apply different analysis algorithms.

[0048] The analysis unit can estimate the baby's emotions and adjust the length of the analysis based on the estimated emotions. For example, when the baby is in a good mood, the analysis unit can perform a detailed analysis and present a longer result. When the baby is crying, the analysis unit can perform a simplified analysis and present a shorter result. When the baby is agitated, the analysis unit can present an analysis result of an appropriate length to soothe the agitation. By adjusting the length of the analysis based on the baby's emotions, it becomes possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the length of the analysis.

[0049] The analysis unit can determine the priority of analysis based on when the language data was collected during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent language data, enabling real-time responses. The analysis unit can also analyze current data while referring to past language data. The analysis unit can also prioritize the analysis of language data collected during a specific period, allowing for the understanding of trends during that period. This enables efficient analysis by determining the priority of analysis based on when the language data was collected. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, which can then analyze the data to determine the priority of analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the language data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant language data to quickly grasp important information. The analysis unit can also postpone the analysis of less relevant language data to perform efficient analysis. The analysis unit can also group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the language data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, which can then analyze the data and adjust the order of analysis.

[0051] The dialogue generation unit can estimate the baby's emotions and adjust the way the dialogue is expressed based on the estimated emotions. For example, when the baby is in a good mood, the dialogue generation unit generates dialogue using cheerful and fun expressions. When the baby is crying, the dialogue generation unit can also generate dialogue using calming expressions. When the baby is excited, the dialogue generation unit can also generate dialogue using expressions that soothe the excitement. By adjusting the way the dialogue is expressed based on the baby's emotions, it becomes possible to generate more appropriate dialogue. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the way the dialogue is expressed.

[0052] The dialogue generation unit can adjust the level of detail in a dialogue based on the importance of the language data during dialogue generation. For example, the dialogue generation unit can generate a detailed dialogue based on important language data. The dialogue generation unit can also generate a simplified dialogue based on less important language data. The dialogue generation unit can also generate multiple dialogue patterns based on highly important language data. This allows for efficient dialogue generation by adjusting the level of detail in a dialogue based on the importance of the language data. Some or all of the above-described processes in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data to adjust the level of detail in the dialogue.

[0053] The dialogue generation unit can apply different dialogue generation algorithms depending on the category of language data when generating dialogue. For example, the dialogue generation unit can generate voice dialogue based on a baby's vocalization data. The dialogue generation unit can also generate visual dialogue based on a baby's facial expression data. The dialogue generation unit can also generate dialogue with actions based on a baby's action data. By applying different dialogue generation algorithms depending on the category of language data, more accurate dialogue generation becomes possible. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generating AI, which can then analyze the data and apply different dialogue generation algorithms.

[0054] The dialogue generation unit can estimate the baby's emotions and adjust the length of the dialogue based on the estimated emotions. For example, the dialogue generation unit can generate a longer dialogue when the baby is in a good mood. It can also generate a shorter dialogue when the baby is crying. It can also generate a dialogue of an appropriate length when the baby is excited. By adjusting the length of the dialogue based on the baby's emotions, it becomes possible to generate more appropriate dialogues. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the length of the dialogue.

[0055] The dialogue generation unit can determine dialogue priorities based on the timing of language data collection during dialogue generation. For example, the dialogue generation unit can prioritize dialogue generation based on the most recent language data. The dialogue generation unit can also generate dialogues based on current data while referring to past language data. The dialogue generation unit can also prioritize dialogue generation based on language data collected during a specific period. This enables efficient dialogue generation by determining dialogue priorities based on the timing of language data collection. Some or all of the above-described processes in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data to determine dialogue priorities.

[0056] The dialogue generation unit can adjust the order of dialogues based on the relevance of language data during dialogue generation. For example, the dialogue generation unit can prioritize generating dialogues based on highly relevant language data. The dialogue generation unit can also postpone generating dialogues based on less relevant language data. The dialogue generation unit can also group highly relevant data and generate dialogues in a batch. This enables efficient dialogue generation by adjusting the order of dialogues based on the relevance of language data. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data and adjust the order of dialogues.

[0057] The advice-providing unit can estimate the baby's emotions and adjust the way it expresses advice based on the estimated emotions. For example, when the baby is in a good mood, the advice-providing unit will provide advice using positive language. When the baby is crying, the advice-providing unit can also provide advice using calming language. When the baby is agitated, the advice-providing unit can also provide advice using calming language. By adjusting the way it expresses advice based on the baby's emotions, it becomes possible to provide more appropriate advice. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the baby's emotional data into a generating AI, which can then analyze the data and adjust the way it expresses advice.

[0058] The advice-providing unit can provide optimal advice by referring to the baby's past behavioral data when providing advice. For example, the advice-providing unit can refer to data from when the baby has performed a specific action in the past and provide advice for similar situations. The advice-providing unit can also analyze the baby's past behavioral patterns and provide advice based on predicted behavior. The advice-providing unit can also provide advice for similar situations based on successful examples of when the baby has performed a specific action in the past. This makes it possible to provide optimal advice by referring to the baby's past behavioral data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the baby's past behavioral data into a generating AI, which can then analyze the data and provide optimal advice.

[0059] The advice-providing unit can estimate the baby's emotions and determine the priority of advice based on the estimated emotions. For example, when the baby is in a good mood, the advice-providing unit will prioritize long-term advice. When the baby is crying, the advice-providing unit can also prioritize advice that requires immediate attention. When the baby is agitated, the advice-providing unit can also prioritize advice to calm the agitation. This makes it possible to provide more appropriate advice by prioritizing advice based on the baby's emotions. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the baby's emotional data into a generating AI, which can then analyze the data to determine the priority of advice.

[0060] The advice-providing unit can provide optimal advice based on the baby's geographical location information when providing advice. For example, when the baby is at home, the advice-providing unit can provide advice related to activities within the home. When the baby is at a park, the advice-providing unit can also provide advice related to activities at the daycare center when the baby is at daycare. This allows for more effective advice by providing optimal advice based on the baby's geographical location information. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the baby's geographical location data into a generating AI, which can then analyze the data to provide optimal advice.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The data collection unit can analyze the baby's past language data and select the optimal collection method. For example, the data collection unit can identify the time of day when the baby vocalizes most frequently from past data and concentrate data collection during that time. The data collection unit can also analyze the baby's tendency to vocalize in specific situations from past data and enhance data collection in those situations. The data collection unit can also analyze the baby's tendency to react to specific sounds from past data and use those sounds for data collection. By analyzing the baby's past language data, the optimal collection method can be selected, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's past language data into a generating AI, which can then analyze the data and select the optimal collection method.

[0063] The analysis unit can adjust the level of detail of the analysis based on the importance of the language data during the analysis. For example, the analysis unit can perform a detailed analysis on important language data and present specific results. For less important language data, the analysis unit can perform a simplified analysis and present only an overview. For highly important language data, the analysis unit can use multiple analysis methods to present detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the language data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, and the generating AI can analyze that data to adjust the level of detail of the analysis.

[0064] The dialogue generation unit can adjust the level of detail in a dialogue based on the importance of the language data during dialogue generation. For example, the dialogue generation unit can generate a detailed dialogue based on important language data. The dialogue generation unit can also generate a simplified dialogue based on less important language data. The dialogue generation unit can also generate multiple dialogue patterns based on highly important language data. This allows for efficient dialogue generation by adjusting the level of detail in a dialogue based on the importance of the language data. Some or all of the above-described processes in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data to adjust the level of detail in the dialogue.

[0065] The advice-providing unit can provide optimal advice by referring to the baby's past behavioral data when providing advice. For example, the advice-providing unit can refer to data from when the baby has performed a specific action in the past and provide advice for similar situations. The advice-providing unit can also analyze the baby's past behavioral patterns and provide advice based on predicted behavior. The advice-providing unit can also provide advice for similar situations based on successful examples of when the baby has performed a specific action in the past. This makes it possible to provide optimal advice by referring to the baby's past behavioral data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the baby's past behavioral data into a generating AI, which can then analyze the data and provide optimal advice.

[0066] The data collection unit can filter language data based on the baby's current living situation and areas of interest. For example, when the baby is playing, the data collection unit can prioritize collecting language data related to play. When the baby is eating, the data collection unit can also prioritize collecting language data related to eating. Before the baby falls asleep, the data collection unit can also prioritize collecting language data in a relaxed state. This allows for the collection of more relevant data by filtering based on the baby's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's living situation data into a generating AI, which can then analyze and filter that data.

[0067] The analysis unit can determine the priority of analysis based on when the language data was collected during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent language data, enabling real-time responses. The analysis unit can also analyze current data while referring to past language data. The analysis unit can also prioritize the analysis of language data collected during a specific period, allowing for the understanding of trends during that period. This enables efficient analysis by determining the priority of analysis based on when the language data was collected. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, which can then analyze the data to determine the priority of analysis.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects language data from babies. The data collection unit can collect language data from tens of thousands of babies, for example. The data collection unit meticulously records the babies' vocalizations and expressions to build a database. For example, when a baby says "mama," the data collection unit collects that audio data and uses it to train the AI. This allows the data collection unit to understand the babies' language patterns. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the baby's expressions and needs. For example, the analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. The analysis unit can use AI to analyze the baby's language data. For example, the analysis unit inputs the baby's vocalization data into the AI, which then analyzes the data to understand the baby's expressions and needs. Step 3: The dialogue generation unit generates an appropriate dialogue based on the results obtained by the analysis unit. The dialogue generation unit can, for example, learn the baby's language patterns and preferences to generate personalized dialogues. The dialogue generation unit uses AI to generate dialogue based on the baby's language data. For example, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. Step 4: The advice-providing unit provides advice to parents and caregivers based on the dialogue generated by the dialogue generation unit. The advice-providing unit can, for example, provide comprehensive advice regarding the baby's behavior and expressions. The advice-providing unit analyzes the baby's behavior and expressions and generates advice using AI. For example, if the baby is crying, the advice-providing unit analyzes the reason and notifies the parents that "the baby seems sleepy."

[0070] (Example of form 2) The AI-based childcare support system according to an embodiment of the present invention is a system for facilitating communication with babies. This system brings new possibilities to childcare support for parents and childcare workers. The AI-based childcare support system learns language data from tens of thousands of babies and builds a sophisticated database of baby talk. This makes it possible to understand the subtle expressions and needs of babies with high accuracy and propose appropriate responses. The real-time dialogue support function interprets the baby's vocalizations on the spot and presents the optimal communication method. Furthermore, by learning the language patterns and preferences of each baby and personalizing the dialogue, it achieves more effective communication. For example, for babies who react strongly to certain words or sounds, it is possible to generate dialogues that utilize them, allowing for detailed responses. The AI-based childcare support system is not merely a translation tool, but also provides comprehensive advice on the baby's behavior and expressions. This allows parents and childcare workers to understand the baby's needs more deeply and respond appropriately. As a result, it is expected that the bond with the baby will deepen and healthy development will be promoted. For example, the AI-based childcare support system collects language data from tens of thousands of babies and uses it to train the AI. In this process, the system meticulously records the baby's vocalizations and expressions to build a database. For example, if a baby says "mama," the audio data is collected and used to train the AI. This allows the system to understand the baby's language patterns. Next, the AI-based childcare support system provides real-time dialogue support. When a baby makes a sound, the AI ​​analyzes the sound and suggests an appropriate response. For example, if a baby says "I'm hungry," the AI ​​analyzes the sound and notifies the parent that "the baby seems hungry." This allows the parent to respond quickly to the baby's needs. Furthermore, the AI-based childcare support system learns each baby's language patterns and preferences to personalize the dialogue. For example, for a baby who strongly responds to certain words or sounds, the system generates dialogues that utilize them. For instance, if a baby strongly responds to the word "toy," the AI ​​uses that word to generate dialogues that facilitate communication with the baby.Furthermore, AI-based childcare support systems also provide comprehensive advice regarding a baby's behavior and expressions. For example, if a baby is crying, the system analyzes the reason and notifies the parents that "the baby seems sleepy." This allows parents to better understand their baby's needs and respond appropriately. AI-based childcare support systems are extremely useful for parents and caregivers, and are expected to deepen the bond with babies and promote healthy development. For example, it can be difficult for first-time or inexperienced parents to correctly understand a baby's behavior and expressions, but by using an AI-based childcare support system, they can respond appropriately to the baby's needs. Also, for childcare workers and kindergarten teachers, communication with babies and toddlers is a crucial element of education and childcare, and by using an AI-based childcare support system, they can accurately grasp the baby's needs and emotions. In this way, AI-based childcare support systems can streamline communication with babies and bring new possibilities to childcare support for parents and caregivers.

[0071] The AI-based childcare support system according to this embodiment comprises a data collection unit, an analysis unit, a dialogue generation unit, and an advice provision unit. The data collection unit collects language data of babies. The data collection unit can collect language data from, for example, tens of thousands of babies. The data collection unit meticulously records the babies' vocalizations and expressions and builds a database. For example, if a baby says "mama," the data collection unit collects the audio data and uses it to train the AI. This allows the data collection unit to understand the babies' language patterns. The analysis unit analyzes the data collected by the data collection unit to understand the babies' expressions and needs. For example, the analysis unit can analyze the collected baby language data to understand the babies' subtle expressions and needs. The analysis unit can use AI to analyze the babies' language data. For example, the analysis unit inputs the baby's vocalization data into the AI, which then analyzes the data to understand the babies' expressions and needs. The dialogue generation unit generates appropriate dialogues based on the results obtained by the analysis unit. For example, the dialogue generation unit can learn the babies' language patterns and preferences and generate personalized dialogues. The dialogue generation unit generates dialogue using AI based on the baby's language data. For example, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. The advice provision unit provides advice to parents and caregivers based on the dialogue generated by the dialogue generation unit. The advice provision unit can, for example, provide comprehensive advice regarding the baby's behavior and expressions. The advice provision unit analyzes the baby's behavior and expressions and generates advice using AI. For example, if the baby is crying, the advice provision unit analyzes the reason and notifies the parents that "the baby seems sleepy." As a result, the AI-based childcare support system according to this embodiment can collect, analyze, generate dialogue, and provide advice based on the baby's language data.

[0072] The data collection unit collects language data from babies. For example, the unit can collect language data from tens of thousands of babies. Specifically, the unit meticulously records the babies' vocalizations and expressions to build a database. For instance, if a baby says "mama," the unit collects that audio data and uses it to train the AI. The unit collects the babies' vocalizations using high-precision microphones and speech recognition devices, and stores the audio data digitally. Furthermore, the unit can record not only the babies' vocalizations but also non-verbal communication such as facial expressions and gestures. This allows the unit to build a rich dataset for understanding the babies' language patterns. The collected data is stored on a cloud server, making it accessible to the analysis and dialogue generation units. The unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a baby is most vocal during a particular time period, data collection can be concentrated during that time. This allows the unit to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis unit analyzes the data collected by the data collection unit to understand the baby's expressions and needs. For example, the analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. The analysis unit can use AI to analyze the baby's language data. Specifically, the analysis unit inputs the baby's vocalization data into the AI, which then analyzes the data to understand the baby's expressions and needs. The AI ​​uses speech recognition and natural language processing technologies to analyze the baby's vocalization patterns and vocal characteristics. For example, if the baby says "toy," the analysis unit can analyze the tone and rhythm of the vocalization to determine whether the baby is excited or calm. Furthermore, the analysis unit can analyze not only the baby's vocalization data but also non-verbal data such as facial expressions and gestures. This allows the analysis unit to understand the baby's overall communication patterns and grasp their needs more accurately. The analysis unit can also utilize past data and statistical information to analyze long-term trends and patterns. For example, if a baby tends to make certain vocalizations at specific times of day, the system can analyze these patterns and provide appropriate advice to parents and caregivers. This allows the analysis unit to not only monitor the situation in real time but also provide long-term childcare support, improving the overall reliability and usefulness of the system.

[0074] The dialogue generation unit generates appropriate dialogue based on the results obtained by the analysis unit. For example, the dialogue generation unit can learn the baby's language patterns and preferences to generate personalized dialogue. The dialogue generation unit uses AI to generate dialogue based on the baby's language data. Specifically, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. For example, if the baby says "toy," the dialogue generation unit will generate a response such as "Do you want to play with a toy?" to facilitate dialogue with the baby. The dialogue generation unit uses natural language generation technology to generate appropriate responses to the baby's vocalizations and expressions. Furthermore, the dialogue generation unit can monitor the baby's responses in real time and dynamically adjust the content of the dialogue. For example, if the baby is excited, the dialogue generation unit will select calming words to proceed with the dialogue. In addition, the dialogue generation unit can generate dialogue considering not only the baby's vocalizations but also nonverbal communication such as facial expressions and gestures. As a result, the dialogue generation unit can achieve natural communication with the baby and support the baby's development. The dialogue generation unit can also provide parents and childcare workers with methods for interacting with babies and examples of appropriate responses. This allows the dialogue generation unit to facilitate communication with babies and enhance the effectiveness of childcare support.

[0075] The advice-providing unit provides advice to parents and caregivers based on dialogues generated by the dialogue generation unit. For example, the advice-providing unit can provide comprehensive advice regarding a baby's behavior and expressions. It analyzes the baby's behavior and expressions and generates advice using AI. Specifically, if a baby is crying, the advice-providing unit analyzes the reason and notifies the parents that "the baby seems sleepy." Based on the baby's behavior and expressions, the advice-providing unit provides appropriate responses and parenting tips. For example, if a baby tends to cry at certain times, it can suggest appropriate responses for those times. Furthermore, the advice-providing unit can provide customized advice tailored to the baby's developmental stage and individual needs. For example, when a baby is beginning to learn language, it can suggest specific methods to encourage language development to the parents. The advice-providing unit can also collect feedback from parents and caregivers and continuously improve the content of its advice. This allows the advice-providing unit to provide reliable parenting support to parents and caregivers and support the healthy growth of babies. The advice-providing unit can reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the advice department to provide advice quickly and reliably to parents and childcare workers, maximizing the effectiveness of childcare support.

[0076] The data collection unit can collect language data from tens of thousands of babies. For example, the data collection unit collects language data from tens of thousands of babies. The data collection unit meticulously records the babies' vocalizations and expressions to build a database. For example, when a baby says "mama," the data collection unit collects that audio data and uses it to train an AI. This allows the data collection unit to understand the babies' language patterns. By collecting a large amount of baby language data, highly accurate analysis becomes possible. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's vocalization data into a generating AI, which can then analyze the data to understand the babies' language patterns.

[0077] The analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. For example, the analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. The analysis unit can use AI to analyze the baby's language data. For example, the analysis unit can input the baby's vocalization data into the AI, which will analyze the data to understand the baby's expressions and needs. This allows for appropriate responses by understanding the baby's subtle expressions and needs. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the baby's vocalization data into a generating AI, which will analyze the data to understand the baby's expressions and needs.

[0078] The dialogue generation unit can learn the baby's language patterns and preferences and generate personalized dialogues. For example, the dialogue generation unit learns the baby's language patterns and preferences and generates personalized dialogues. The dialogue generation unit uses AI to generate dialogues based on the baby's language data. For example, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. This enables more effective communication by generating personalized dialogues based on the baby's language patterns and preferences. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's language data into a generating AI, which can then analyze the data to generate personalized dialogues.

[0079] The advice-providing unit can provide comprehensive advice regarding the baby's behavior and expressions. For example, the advice-providing unit can provide comprehensive advice regarding the baby's behavior and expressions. The advice-providing unit analyzes the baby's behavior and expressions and generates advice using AI. For example, if the baby is crying, the advice-providing unit analyzes the reason and notifies the parents that "the baby seems sleepy." By providing comprehensive advice regarding the baby's behavior and expressions, parents and caregivers can better understand the baby's needs and respond appropriately. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the baby's behavior data into a generating AI, which can then analyze the data and generate advice.

[0080] The dialogue generation unit can generate dialogues for babies who strongly respond to specific words or sounds, utilizing those words or sounds. For example, the dialogue generation unit generates dialogues for babies who strongly respond to specific words or sounds, utilizing those words or sounds. The dialogue generation unit generates dialogues using AI based on the baby's language data. For example, if the baby strongly responds to the word "toy," the dialogue generation unit will generate dialogue using that word. This makes it possible to communicate more effectively with babies who strongly respond to specific words or sounds by generating dialogues that utilize those words or sounds. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's language data into a generating AI, and the generating AI can analyze that data to generate dialogue.

[0081] The data collection unit can estimate the baby's emotions and adjust the timing of language data collection based on the estimated emotions. For example, the AI ​​can adjust the data collection unit to collect more language data when the baby is in a good mood. The AI ​​can also adjust the data collection unit to temporarily suspend collection when the baby is crying and resume it after the baby has calmed down. The AI ​​can also adjust the data collection unit to focus on collecting responses to specific words or sounds when the baby is agitated. This allows for more appropriate data collection by adjusting the timing of language data collection based on the baby's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's emotional data into a generating AI, which can then analyze the data and adjust the collection timing.

[0082] The data collection unit can analyze the baby's past language data and select the optimal collection method. For example, the data collection unit can identify the time of day when the baby vocalizes most frequently from past data and concentrate data collection during that time. The data collection unit can also analyze the baby's tendency to vocalize in specific situations from past data and enhance data collection in those situations. The data collection unit can also analyze the baby's tendency to react to specific sounds from past data and use those sounds for data collection. By analyzing the baby's past language data, the optimal collection method can be selected, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's past language data into a generating AI, which can then analyze the data and select the optimal collection method.

[0083] The data collection unit can filter language data based on the baby's current living situation and areas of interest. For example, when the baby is playing, the data collection unit can prioritize collecting language data related to play. When the baby is eating, the data collection unit can also prioritize collecting language data related to eating. Before the baby falls asleep, the data collection unit can also prioritize collecting language data in a relaxed state. This allows for the collection of more relevant data by filtering based on the baby's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's living situation data into a generating AI, which can then analyze and filter that data.

[0084] The data collection unit can estimate the baby's emotions and determine the priority of language data to collect based on the estimated emotions. For example, the data collection unit can prioritize collecting more language data when the baby is in a good mood. The data collection unit can also prioritize temporarily suspending collection when the baby is crying and resuming it after the baby has calmed down. The data collection unit can also prioritize focusing on collecting responses to specific words or sounds when the baby is agitated. This makes it possible to prioritize the collection of more important data by determining the priority of language data to collect based on the baby's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the baby's emotion data into a generating AI, which can then analyze the data to determine the priority of language data to collect.

[0085] The data collection unit can prioritize the collection of highly relevant data based on the baby's geographical location when collecting language data. For example, when the baby is at home, the data collection unit prioritizes collecting language data from within the home. When the baby is in a park, the data collection unit can also prioritize the collection of language data related to outdoor play. When the baby is in daycare, the data collection unit can also prioritize the collection of language data related to activities at daycare. This allows for more effective data collection by prioritizing the collection of highly relevant data based on the baby's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's geographical location data into a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0086] The data collection unit can analyze the baby's social media activity and collect relevant data when collecting language data. For example, the data collection unit can analyze how often the baby appears on the parents' social media and collect language data at that time. The data collection unit can also analyze the baby's tendency to react to specific social media posts and collect language data related to those posts. The data collection unit can also collect language data when the baby participates in live streams on social media. This allows for more comprehensive data collection by analyzing the baby's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the baby's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0087] The analysis unit can estimate the baby's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, when the baby is in a good mood, the analysis unit will present the analysis results using positive expressions. When the baby is crying, the analysis unit can also present the analysis results using calming expressions. When the baby is agitated, the analysis unit can also present the analysis results using expressions that soothe the agitation. By adjusting the way the analysis is presented based on the baby's emotions, it becomes possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the way the analysis is presented.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the language data during the analysis. For example, the analysis unit can perform a detailed analysis on important language data and present specific results. For less important language data, the analysis unit can perform a simplified analysis and present only an overview. For highly important language data, the analysis unit can use multiple analysis methods to present detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the language data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, and the generating AI can analyze that data to adjust the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of the language data during analysis. For example, the analysis unit can apply a voice analysis algorithm to baby vocalization data. The analysis unit can also apply an image analysis algorithm to baby facial expression data. The analysis unit can also apply a motion analysis algorithm to baby motion data. By applying different analysis algorithms depending on the category of the language data, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, and the generating AI can analyze that data and apply different analysis algorithms.

[0090] The analysis unit can estimate the baby's emotions and adjust the length of the analysis based on the estimated emotions. For example, when the baby is in a good mood, the analysis unit can perform a detailed analysis and present a longer result. When the baby is crying, the analysis unit can perform a simplified analysis and present a shorter result. When the baby is agitated, the analysis unit can present an analysis result of an appropriate length to soothe the agitation. By adjusting the length of the analysis based on the baby's emotions, it becomes possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on when the language data was collected during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent language data, enabling real-time responses. The analysis unit can also analyze current data while referring to past language data. The analysis unit can also prioritize the analysis of language data collected during a specific period, allowing for the understanding of trends during that period. This enables efficient analysis by determining the priority of analysis based on when the language data was collected. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, which can then analyze the data to determine the priority of analysis.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the language data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant language data to quickly grasp important information. The analysis unit can also postpone the analysis of less relevant language data to perform efficient analysis. The analysis unit can also group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the language data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, which can then analyze the data and adjust the order of analysis.

[0093] The dialogue generation unit can estimate the baby's emotions and adjust the way the dialogue is expressed based on the estimated emotions. For example, when the baby is in a good mood, the dialogue generation unit generates dialogue using cheerful and fun expressions. When the baby is crying, the dialogue generation unit can also generate dialogue using calming expressions. When the baby is excited, the dialogue generation unit can also generate dialogue using expressions that soothe the excitement. By adjusting the way the dialogue is expressed based on the baby's emotions, it becomes possible to generate more appropriate dialogue. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the way the dialogue is expressed.

[0094] The dialogue generation unit can adjust the level of detail in a dialogue based on the importance of the language data during dialogue generation. For example, the dialogue generation unit can generate a detailed dialogue based on important language data. The dialogue generation unit can also generate a simplified dialogue based on less important language data. The dialogue generation unit can also generate multiple dialogue patterns based on highly important language data. This allows for efficient dialogue generation by adjusting the level of detail in a dialogue based on the importance of the language data. Some or all of the above-described processes in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data to adjust the level of detail in the dialogue.

[0095] The dialogue generation unit can apply different dialogue generation algorithms depending on the category of language data when generating dialogue. For example, the dialogue generation unit can generate voice dialogue based on a baby's vocalization data. The dialogue generation unit can also generate visual dialogue based on a baby's facial expression data. The dialogue generation unit can also generate dialogue with actions based on a baby's action data. By applying different dialogue generation algorithms depending on the category of language data, more accurate dialogue generation becomes possible. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generating AI, which can then analyze the data and apply different dialogue generation algorithms.

[0096] The dialogue generation unit can estimate the baby's emotions and adjust the length of the dialogue based on the estimated emotions. For example, the dialogue generation unit can generate a longer dialogue when the baby is in a good mood. It can also generate a shorter dialogue when the baby is crying. It can also generate a dialogue of an appropriate length when the baby is excited. By adjusting the length of the dialogue based on the baby's emotions, it becomes possible to generate more appropriate dialogues. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the length of the dialogue.

[0097] The dialogue generation unit can determine dialogue priorities based on the timing of language data collection during dialogue generation. For example, the dialogue generation unit can prioritize dialogue generation based on the most recent language data. The dialogue generation unit can also generate dialogues based on current data while referring to past language data. The dialogue generation unit can also prioritize dialogue generation based on language data collected during a specific period. This enables efficient dialogue generation by determining dialogue priorities based on the timing of language data collection. Some or all of the above-described processes in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data to determine dialogue priorities.

[0098] The dialogue generation unit can adjust the order of dialogues based on the relevance of language data during dialogue generation. For example, the dialogue generation unit can prioritize generating dialogues based on highly relevant language data. The dialogue generation unit can also postpone generating dialogues based on less relevant language data. The dialogue generation unit can also group highly relevant data and generate dialogues in a batch. This enables efficient dialogue generation by adjusting the order of dialogues based on the relevance of language data. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data and adjust the order of dialogues.

[0099] The advice-providing unit can estimate the baby's emotions and adjust the way it expresses advice based on the estimated emotions. For example, when the baby is in a good mood, the advice-providing unit will provide advice using positive language. When the baby is crying, the advice-providing unit can also provide advice using calming language. When the baby is agitated, the advice-providing unit can also provide advice using calming language. By adjusting the way it expresses advice based on the baby's emotions, it becomes possible to provide more appropriate advice. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the baby's emotional data into a generating AI, which can then analyze the data and adjust the way it expresses advice.

[0100] The advice-providing unit can provide optimal advice by referring to the baby's past behavioral data when providing advice. For example, the advice-providing unit can refer to data from when the baby has performed a specific action in the past and provide advice for similar situations. The advice-providing unit can also analyze the baby's past behavioral patterns and provide advice based on predicted behavior. The advice-providing unit can also provide advice for similar situations based on successful examples of when the baby has performed a specific action in the past. This makes it possible to provide optimal advice by referring to the baby's past behavioral data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the baby's past behavioral data into a generating AI, which can then analyze the data and provide optimal advice.

[0101] The advice-providing unit can estimate the baby's emotions and determine the priority of advice based on the estimated emotions. For example, when the baby is in a good mood, the advice-providing unit will prioritize long-term advice. When the baby is crying, the advice-providing unit can also prioritize advice that requires immediate attention. When the baby is agitated, the advice-providing unit can also prioritize advice to calm the agitation. This makes it possible to provide more appropriate advice by prioritizing advice based on the baby's emotions. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the baby's emotional data into a generating AI, which can then analyze the data to determine the priority of advice.

[0102] The advice-providing unit can provide optimal advice based on the baby's geographical location information when providing advice. For example, when the baby is at home, the advice-providing unit can provide advice related to activities within the home. When the baby is at a park, the advice-providing unit can also provide advice related to activities at the daycare center when the baby is at daycare. This allows for more effective advice by providing optimal advice based on the baby's geographical location information. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the baby's geographical location data into a generating AI, which can then analyze the data to provide optimal advice.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The data collection unit can estimate the baby's emotions and adjust the timing of language data collection based on the estimated emotions. For example, the AI ​​can adjust the data collection unit to collect more language data when the baby is in a good mood. The AI ​​can also adjust the data collection unit to temporarily suspend collection when the baby is crying and resume it after the baby has calmed down. The AI ​​can also adjust the data collection unit to focus on collecting responses to specific words or sounds when the baby is agitated. This allows for more appropriate data collection by adjusting the timing of language data collection based on the baby's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's emotional data into a generating AI, which can then analyze the data and adjust the collection timing.

[0105] The analysis unit can estimate the baby's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, when the baby is in a good mood, the analysis unit will present the analysis results using positive expressions. When the baby is crying, the analysis unit can also present the analysis results using calming expressions. When the baby is agitated, the analysis unit can also present the analysis results using expressions that soothe the agitation. By adjusting the way the analysis is presented based on the baby's emotions, it becomes possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the way the analysis is presented.

[0106] The dialogue generation unit can estimate the baby's emotions and adjust the way the dialogue is expressed based on the estimated emotions. For example, when the baby is in a good mood, the dialogue generation unit generates dialogue using cheerful and fun expressions. When the baby is crying, the dialogue generation unit can also generate dialogue using calming expressions. When the baby is excited, the dialogue generation unit can also generate dialogue using expressions that soothe the excitement. By adjusting the way the dialogue is expressed based on the baby's emotions, it becomes possible to generate more appropriate dialogue. Some or all of the above processing in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input the baby's emotion data into a generating AI, which can then analyze the data and adjust the way the dialogue is expressed.

[0107] The advice-providing unit can estimate the baby's emotions and adjust the way it expresses advice based on the estimated emotions. For example, when the baby is in a good mood, the advice-providing unit will provide advice using positive language. When the baby is crying, the advice-providing unit can also provide advice using calming language. When the baby is agitated, the advice-providing unit can also provide advice using calming language. By adjusting the way it expresses advice based on the baby's emotions, it becomes possible to provide more appropriate advice. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or not using AI. For example, the advice-providing unit can input the baby's emotional data into a generating AI, which can then analyze the data and adjust the way it expresses advice.

[0108] The data collection unit can analyze the baby's past language data and select the optimal collection method. For example, the data collection unit can identify the time of day when the baby vocalizes most frequently from past data and concentrate data collection during that time. The data collection unit can also analyze the baby's tendency to vocalize in specific situations from past data and enhance data collection in those situations. The data collection unit can also analyze the baby's tendency to react to specific sounds from past data and use those sounds for data collection. By analyzing the baby's past language data, the optimal collection method can be selected, enabling efficient data collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's past language data into a generating AI, which can then analyze the data and select the optimal collection method.

[0109] The analysis unit can adjust the level of detail of the analysis based on the importance of the language data during the analysis. For example, the analysis unit can perform a detailed analysis on important language data and present specific results. For less important language data, the analysis unit can perform a simplified analysis and present only an overview. For highly important language data, the analysis unit can use multiple analysis methods to present detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the language data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, and the generating AI can analyze that data to adjust the level of detail of the analysis.

[0110] The dialogue generation unit can adjust the level of detail in a dialogue based on the importance of the language data during dialogue generation. For example, the dialogue generation unit can generate a detailed dialogue based on important language data. The dialogue generation unit can also generate a simplified dialogue based on less important language data. The dialogue generation unit can also generate multiple dialogue patterns based on highly important language data. This allows for efficient dialogue generation by adjusting the level of detail in a dialogue based on the importance of the language data. Some or all of the above-described processes in the dialogue generation unit may be performed using AI, for example, or without AI. For example, the dialogue generation unit can input language data into a generation AI, which can then analyze the data to adjust the level of detail in the dialogue.

[0111] The advice-providing unit can provide optimal advice by referring to the baby's past behavioral data when providing advice. For example, the advice-providing unit can refer to data from when the baby has performed a specific action in the past and provide advice for similar situations. The advice-providing unit can also analyze the baby's past behavioral patterns and provide advice based on predicted behavior. The advice-providing unit can also provide advice for similar situations based on successful examples of when the baby has performed a specific action in the past. This makes it possible to provide optimal advice by referring to the baby's past behavioral data. Some or all of the above processing in the advice-providing unit may be performed using AI, for example, or without AI. For example, the advice-providing unit can input the baby's past behavioral data into a generating AI, which can then analyze the data and provide optimal advice.

[0112] The data collection unit can filter language data based on the baby's current living situation and areas of interest. For example, when the baby is playing, the data collection unit can prioritize collecting language data related to play. When the baby is eating, the data collection unit can also prioritize collecting language data related to eating. Before the baby falls asleep, the data collection unit can also prioritize collecting language data in a relaxed state. This allows for the collection of more relevant data by filtering based on the baby's current living situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the baby's living situation data into a generating AI, which can then analyze and filter that data.

[0113] The analysis unit can determine the priority of analysis based on when the language data was collected during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent language data, enabling real-time responses. The analysis unit can also analyze current data while referring to past language data. The analysis unit can also prioritize the analysis of language data collected during a specific period, allowing for the understanding of trends during that period. This enables efficient analysis by determining the priority of analysis based on when the language data was collected. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input language data into a generating AI, which can then analyze the data to determine the priority of analysis.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The data collection unit collects language data from babies. The data collection unit can collect language data from tens of thousands of babies, for example. The data collection unit meticulously records the babies' vocalizations and expressions to build a database. For example, when a baby says "mama," the data collection unit collects that audio data and uses it to train the AI. This allows the data collection unit to understand the babies' language patterns. Step 2: The analysis unit analyzes the data collected by the collection unit to understand the baby's expressions and needs. For example, the analysis unit can analyze the collected baby's language data to understand the baby's subtle expressions and needs. The analysis unit can use AI to analyze the baby's language data. For example, the analysis unit inputs the baby's vocalization data into the AI, which then analyzes the data to understand the baby's expressions and needs. Step 3: The dialogue generation unit generates an appropriate dialogue based on the results obtained by the analysis unit. The dialogue generation unit can, for example, learn the baby's language patterns and preferences to generate personalized dialogues. The dialogue generation unit uses AI to generate dialogue based on the baby's language data. For example, if the baby responds strongly to the word "toy," the dialogue generation unit will use that word to generate dialogue. Step 4: The advice-providing unit provides advice to parents and caregivers based on the dialogue generated by the dialogue generation unit. The advice-providing unit can, for example, provide comprehensive advice regarding the baby's behavior and expressions. The advice-providing unit analyzes the baby's behavior and expressions and generates advice using AI. For example, if the baby is crying, the advice-providing unit analyzes the reason and notifies the parents that "the baby seems sleepy."

[0116] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue generation unit, and advice provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the baby's language data using the camera 42 and microphone 38B of the smart device 14 and records the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the baby's expressions and needs. The dialogue generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates appropriate dialogue based on the analysis results. The advice provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides advice to parents and caregivers based on the generated dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 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.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue generation unit, and advice provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the baby's language data using the camera 42 and microphone 238 of the smart glasses 214 and records the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the baby's expressions and needs. The dialogue generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates appropriate dialogue based on the analysis results. The advice provision unit is implemented in the specific processing unit 46A of the smart glasses 214 and provides advice to parents and caregivers based on the generated dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue generation unit, and advice provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the baby's language data using the camera 42 and microphone 238 of the headset terminal 314 and records the data with the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to understand the baby's expressions and needs. The dialogue generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates appropriate dialogue based on the analysis results. The advice provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides advice to parents and caregivers based on the generated dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 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.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the data collection unit, analysis unit, dialogue generation unit, and advice provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects the baby's language data using the camera 42 and microphone 238 of the robot 414 and records the data with the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to understand the baby's expressions and needs. The dialogue generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates appropriate dialogue based on the analysis results. The advice provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides advice to parents and caregivers based on the generated dialogue. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0169] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] 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.

[0179] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) A collection unit that collects baby language data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the baby's expressions and needs, A dialogue generation unit generates an appropriate dialogue based on the results obtained by the analysis unit, The system includes an advice provision unit that provides advice to parents and childcare workers based on the dialogue generated by the dialogue generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect language data from tens of thousands of babies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By analyzing the language data collected from babies, we can understand their subtle expressions and needs. The system described in Appendix 1, characterized by the features described herein. (Note 4) The dialogue generation unit, It learns the baby's language patterns and preferences and generates personalized dialogues. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice-providing unit, We provide comprehensive advice on your baby's behavior and expressions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The dialogue generation unit, For babies who react strongly to specific words or sounds, we generate dialogues that utilize those elements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the baby's emotions and adjusts the timing of language data collection based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the baby's past language data to select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting language data, filtering is performed based on the baby's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the baby's emotions and prioritizes the language data to collect based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting language data, the system prioritizes collecting data with high relevance based on the baby's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting language data, analyze the baby's social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the baby's emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the linguistic data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the linguistic data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the baby's emotions and adjusts the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the language data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the linguistic data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The dialogue generation unit, It estimates the baby's emotions and adjusts the way it expresses itself in conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The dialogue generation unit, When generating dialogue, adjust the level of detail in the dialogue based on the importance of the language data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The dialogue generation unit, When generating dialogue, different dialogue generation algorithms are applied depending on the category of the language data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The dialogue generation unit, It estimates the baby's emotions and adjusts the length of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The dialogue generation unit, When generating dialogues, the priority of the dialogues is determined based on when the language data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The dialogue generation unit, During dialogue generation, the order of dialogues is adjusted based on the relevance of the language data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice-providing unit, The system estimates the baby's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advice-providing unit, When providing advice, we refer to the baby's past behavioral data to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice-providing unit, The system estimates the baby's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice-providing unit, When providing advice, we will provide the most appropriate advice based on the baby's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects baby language data, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand the baby's expressions and needs, A dialogue generation unit generates an appropriate dialogue based on the results obtained by the analysis unit, The system includes an advice provision unit that provides advice to parents and childcare workers based on the dialogue generated by the dialogue generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect language data from tens of thousands of babies. The system according to feature 1.

3. The aforementioned analysis unit, By analyzing the language data collected from babies, we can understand their subtle expressions and needs. The system according to feature 1.

4. The dialogue generation unit, It learns the baby's language patterns and preferences and generates personalized dialogues. The system according to feature 1.

5. The aforementioned advice-providing unit, We provide comprehensive advice on your baby's behavior and expressions. The system according to feature 1.

6. The dialogue generation unit, For babies who react strongly to specific words or sounds, we generate dialogues that utilize those elements. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the baby's emotions and adjusts the timing of language data collection based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the baby's past language data to select the optimal collection method. The system according to feature 1.

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